Chapter 1: Introduction
Artificial Intelligence (AI) refers to the usage of machines that are organized with human intelligence to replicate their work and think in the same way. They are programmed in a way so that they can make decisions thereby evaluating data. In recent times of digitalization, the influence of AI has been emerging in every sector of industries thereby paving a way for development and improvement. On the other hand, it has become an essential tool within the health sector in the present situation. The outbreak of “Novel Coronavirus” and the situation of the pandemic in 2020 have exceptionally enhanced the significance of AI in this industry. As opined by Reddy and Purohit (2019), AI has essentially assisted the healthcare industry by supporting the delivery of equipment and supervising daily activities. In this respect, it can be stated that the significance of AI has increased in this discussed industry in recent times as there have been changes and developments in the sector.
The Covid-19 spread all over the country has created a pandemic situation reflecting an adverse situation. As stated by Vaishya et al. (2020), AI has played a significant role thereby assisting the healthcare industry in monitoring the spread of the virus across the country and thereby maintaining the daily count of cases. Additionally, it has been successful in identifying high-risk patients and helped the professionals to look after them. In this respect, it has emerged that the development in the concerned industry with the help of AI has made an interesting research area that can enlighten the other researcher about the emerging technologies in the world.
Artificial intelligence has been playing a major role in the healthcare sector thereby providing many benefits to the professions in this field. As explained by Sun and Medaglia (2019), technologies of AI have essentially guided the professions in the diagnosis process of diseases. In addition to this, machine learning and robotics have essentially enhanced the ability of professionals to analyse the changes happening in a patient’s body. Moreover, it has enabled the industry to protect big data concerning patients which have essentially helped them in improving their services. For instance, the American Cancer Society has been using AI to review the translation of mammograms with 90% accuracy and 30 times faster compared to traditional systems (Pwc, 2021). This example sheds light on an understanding that the impact of AI has been positive in the health sector reflecting the essential benefits. Furthermore, it has been observed that in laboratories, robotics is used to prepare the medicine whereas machine learning is being used to analyse the day to day pattern and safeguard patient information.
This research aims at evaluating the impact of artificial intelligence in healthcare.
The objectives for this research are as follows:
- To analyse the significance of digital technologies in healthcare.
- To evaluate the potential role of AI in healthcare for increasing the accuracy and safety of diagnosis.
- To investigate the impact of AI in enhancing patient care and reducing costs of healthcare.
The questions for this research are formulated below:
- What is the significance of digital technologies in healthcare?
- What is the potential of AI in healthcare for increasing the accuracy and safety of diagnosis?
- In what ways does AI impact enhancing patient care and reducing costs of healthcare?
A statistical technique like machine learning is used in healthcare for over years as its common application is precision medicine or prediction for treatment protocols about what may succeed for patients’ wellbeing (Williams, et al., 2018). A neural network is a complex form of machine learning, which is used in health research over decades. Deep learning is the most complex form of machine learning and it is also used in the healthcare sector and helps in the faster processing of graphics and cloud architectures.
Physical robots are popular forms of AI as over 200,000 industrial robots are installed every year worldwide. Head, neck surgery, prostate surgery, and gynaecologic surgery are some usual surgical procedures, where robotic surgery is followed. Even though these robots are used but vital decisions are made by human surgeons.
RPA does not involve robots but computer programs on servers and it depends on business rules, presentation layer integration with information and workflow. In the healthcare sector, it is used for doing repetitive tasks such as prior authorisation, billing, and updating patients’ records. In comparison to other AI technology, it is inexpensive and transparent, and easy to program. Combining RPA with technology like image recognition assists in extracting data from faxed images (Davenport & Kalakota, 2019).
AI technologies improve overall surgical performance as the usual outcome of surgery depends on the surgeon’s skill while the use of these technologies can eliminate case-to-case variations. AI-enabled robots can ensure 3-dimensional magnification to articulate and perform with more miniaturisation and precision. Basic acts like stitching and precision cutting are performed by AI-driven robots. In 2017, in University Medical Centre, Netherlands, these robotics were used to suture quite narrow blood vessels (–.03 to .08 millimetres) (MAYA RAGHUNANDAN, 2018).
AI technologies can enhance the early detection of diseases like retinopathies, cancer and others. Radiology and mammogram images are analysed and reviewed with these technologies and almost 99% accuracy is ensured. In 2017, at Stanford University, a study was published that described the successful application of AI algorithms for detecting skin cancer contradicting the diagnosis by 21 dermatologists. Moreover, there was a competition in International Symposium on Biomedical Imaging, where AI technology was used in computational systems, which were programmed for detecting metastatic breast cancer by analysing biopsy images (MAYA RAGHUNANDAN, 2018).
For drug discovery, the use of AI helps to avoid traditional error thereby embracing patient-driven biology by using more data-derived systems. In 2016, a drug development company, Atomwise used AI for analysing if the available medicines could be modified and redesigned for targeting the Ebola virus.
A deep learning algorithm has been developed by Intel collaborating with the Scripps Research Institute, California, USA. This AI technology has the ability of detecting 23 patients with increased risk of cardiovascular disease with almost 85% accuracy (MAYA RAGHUNANDAN, 2018). For mining medical records, Google DeepMind, IBM Watson are others are the key leaders as these technologies aim to create cognitive assistant, which is equipped with a vast range of reasoning and analytical capabilities along with clinical knowledge.
Artificial Intelligence has been developing potential influence over the healthcare industry over the years with high assistance with computer-based programs. As opined by Marr (2018), AI-based technology such as robotics, machine learning has developed the healthcare industry with their surgery, administrative work, diagnosis, and clinical judgments. In relation to this view, it can be stated that the services provided by the healthcare industry in recent times have essentially improved due to the usage of mentioned technologies. Moreover, it has enabled systematic and fast-paced work with potential accuracy. The present research has enlightened the role of artificial intelligence in the field of healthcare. It has increased the level of quality of this study thereby analysing secondary sources of study which are articles, online websites, and journals. Moreover, it has enlightened the researcher about the present condition of the healthcare sector with the incorporation of AI. Therefore, concerning the thesis statement that whether AI has impacted the healthcare industry in the world positively or not, it has emerged from the study that there has been an immensely positive influence of AI technology. Robotics, machine learning, and other tools have assisted the development of the industry. Moreover, it has essentially increased safety and security with the help of digital technology tools.
In the modern world, technology has made an entry in every sphere thereby spreading a message to individuals about its potential power in becoming the epitome in the coming future. Digital technology, in this aspect refers to the system, electronic tools that assist individuals in storing data and generate desired results. As stated by Danielsson et al. (2017), digital technology has become an integral part of the concerned industry and is driving improvement regarding the development of services. In addition to this, digital technology has essentially increased operational efficiency and has enhanced the medical care service in the healthcare industry. The view of the author sheds light on the emerging demand for digital technology in the most essential sector which is healthcare. Furthermore, it also reflects the development and changes taking place in the industry showcasing the improved services and newly developed medicines.
(Source: Statista, 2021)
As depicted above, a forecast has been done on the developing market size of the global healthcare industry with the implementation of digital technologies. In 2020, investors have increased their funding in digital healthcare, showcasing a value of $21 billion (Statista, 2021). On the other hand, it has been projected that by 2025 thereby increasing the estimating the worth of the market by 660 billion dollars. This essentially represents the demand for digital tools in the industry reflecting the high spending and market size. Moreover, with digital technology, it has been possible for healthcare professionals to retrieve data of patients that essentially help them in the treatment process thereby gaining information about past health history (Health management, 2020). In addition, it has also been observed that video conferencing facilities with digital devices such as computers or smartphones have enabled doctors around the world to connect with each other. Moreover, it has also helped the professionals to treat people where the healthcare industry has not developed much yet.
Contrasting the views, Laurenza et al. (2018) argued that digital technology has essentially helped patients to have control over their health conditions. Health check-up applications have enabled individuals to regularly check their blood pressure, sugar level, and other essential tests. In this respect, it can be said that digitalization in the discussed sector has essentially assisted the people in being less dependent on professionals for ordinary issues. Moreover, it has improved their knowledge and efficiency and assisted them in understanding their health conditions well. In the article, it has emerged that digital technology has ensured security for individuals concerning their health as they are able to connect with healthcare professionals with the use of technology. Moreover, it has been found that it has improved the skills of individuals and has enabled them in understanding their health structure on their own (WHO, 2021). In other words, it has been seen that with the usage of smartphones, and other digital devices, it has been possible for people to connect with professionals at their time of requirement and can even consult with them online for discussing ordinary issues. Therefore, this reflects that digital technology has highly structured the whole system and enhanced the skills of both ordinary as well as professionals. Furthermore, the incorporation of digital technology has essentially reduced inefficiencies of the health sector and helped them to analyse better. However, in the times of Covid-19, digital technology has played a significant role in helping professionals keep a track of data (Househ, 2021). It has effectively controlled the situation of pandemics thereby assisting the industry in delivering high-end services to the patients.
3.1.2 Evaluating the potential impact of AI in increasing the accuracy level and safety for diagnosis
Artificial intelligence has been effective enough within the chosen industry to reflect its potential in developing services. Data privacy, mismanagement, lack of potential decision-making ability has been essentially removed with the help of AI. As explained by Macrae (2019), artificial intelligence has enormously increased safety in the discussed sector thereby bringing accuracy in surgery, forecasting outcomes to proper care, and enhancing treatment planning. Additionally, machine learning with the development of algorithms has been able to gather large data and enabled professionals to have access. Moreover, it has assisted them in driving out desired results through the use of this AI-based technology. In this regard, it can be stated that the accuracy level has increased as data are now protected. On the other hand, it has emerged that machine learning has helped to have outcomes with increased accuracy within a short period (Davenpor and Kalakota, 2019). This essentially reflects the positive impact of AI on the healthcare industry.
Based on the security of diagnosis, it’s emerged from the literature that AI-based technologies are effective in driving out safety measures for patients also as professionals within the industry. As stated by Marr (2018), the effective use of robotics has essentially developed the surgery procedure. it’s been found that almost $40 billion has been invested within the industry which has enabled robots to pre-analyze data thereby assisting surgeons with appropriate surgical instruments. Moreover, it’s emerged from the study that the utilization of robotics has essentially reduced the stay of patients within the hospital by 21%. This reflects the increased safety of the patients that are developed with the implementation of robots.
(Source: Markets and markets, 2021)
As showcased above, the use of machine learning and robotics are analysed to grow more in the healthcare sector with the highest CAGR reflecting a value of 44.9% (Markets and markets, 2021). Moreover, it has been found that radiology has been specially developed to improve the diagnosis and treatment of diseases. In contrast, Challen et al. (2019) said that the use of AI has exceptionally developed the process of manufacturing medicines. Furthermore, enhanced study and availability of information, has enabled researchers to study more and gain knowledge. This, in turn, has essentially been effective for the professionals to understand the importance of each element and even discover new developments that have remained un-investigated.
As discussed previously, AI has been ready to enhance the extent of diagnosis thereby tracing the diseases beforehand. as an example , algorithms are a kind of AI technology that examines habits, genetic information, medical records and detect cancer once they are at an early stage (Marr, 2018). During this respect, it’s been found that the United Kingdom Prime Minister has announced plans to use AI-based technology to develop the UK’s healthcare system and predict cancer in order that deaths are often prevented by 2033. This, in turn, highlights the power of AI in safeguarding patients thereby saving their life with an accurate diagnosis. Furthermore, it’s been analysed that AI has essentially helped in image analysis thereby using the radiology tool. it’s effectively helped people in remote villages as there are fewer doctors .
3.1.3 Investigating the impact of AI in reducing cost and enhancing patient care
The use of artificial intelligence essentially develops internal policies and strategies thereby assisting in the decision-making process. Moreover, it also helps in reducing the administrative burden thereby increasing the accuracy for estimating cost with the help of technology. As per the views of Kelly et al. (2019), the cost is an essential element for an organization, and especially in the healthcare industry, it is essential to keep a track of the cost of the industry involved in the purchase of several pieces of equipment. The view reflects an understanding that with the incorporation of artificial intelligence-based technology it is possible to reduce the cost of healthcare companies. However, in this regard, it has emerged that cost reduction does not involve compromising the quality of services or goods. In this context, the technology has been beneficial for the healthcare sector as they have been able to improve their service quality with minimal cost. Moreover, artificial intelligence effectively identifies tools and techniques that can be used to drive efficiency in the industry thereby maintaining quality and expense (Health It analytics, 2019). It eliminates waste of system and extra expenses by identifying cost-effective tools used for treating patients.
In accordance with the patient care system, according to recent research, AI has enough potential to improve patients’ satisfaction levels with the implementation of machine learning and algorithms (Bailey, 2021). In this regard, it has been analysed that with the availability of information, it is possible to analyse the trends and patterns of patients. This, in turn, can effectively enhance the efficiency level of health workers and assist them in treating the patients with enough care. Moreover, with the analysis of images with the help of AI technology, professionals can identify the actual problems associated with patients. In addition to this, it increases the level of treatment thereby helping the doctors in selecting relevant techniques to control the problem. It has emerged from the article that with the incorporation of AI, clinical judgments have been clearer for the doctors as they have been able to identify the patient’s health history and diagnose them with 100% accuracy. Moreover, with the use of algorithms and interaction with training data, it is feasible for the doctors to understand the patient and have a piece of knowledge about their habits.
Social Cognitive Theory or SCT has been established by Albert Bandura in the 1960s. SCT is certainly relevant in the context of this study because this theory emphasises the importance of influencing individual experiences, individual health behaviours, and the actions of others (Bandura, 2002). This theory delivers complete opportunities for social support by instilling self-efficacy, expectations, and using specific observational learning. SCT is followed as a theoretical framework in diverse populations and settings and it is applied for guiding behaviour change interventions. Moreover, the unique feature of this theory is that it emphasises social influence as well as internal and external social reinforcement. Thus, this theory considers past experiences of a person and in the case of healthcare service management in the NHS, individual experience is considered as an important factor. Technological advancement especially the use of AI technologies is about driving the improved patients’ experiences. SCT also prioritises the importance of goal-directed behaviour and how people’s behaviour can be regulated. The implementation of advanced technologies like AI is not only about expanding the scope of better healthcare services but also enabling the workforce to offer improved services. AI technologies do not only engage the patients to easily access healthcare services as required but also play an important role to ensure gaining improved knowledge and understanding of the changing aspects of the global healthcare system.
In this way the mixing of AI technology within the existing healthcare management system may cause the advancement of each single activity and stage of service management. Multiple positive consequences are observed after an interesting growth within the use of machine-learning algorithms and software for managing complex healthcare and medical dataset, which eventually determines the standard of decision-making and conducting meaningful conversations (Kelly et al., 2019). Implementation of this technology isn’t restricted to a specific area of patients’ experiences, but these are drastically changing the daily practices and clinicians’ roles within the healthcare sector. The matter regarding the shortage of skilled workforce is often addressed by AI-assisted robotic surgery and therefore the overall diagnosis system has also been improved with the assistance of AI technologies.
3.1.5 Relevance of using digital technologies in healthcare
The earlier section of the literature review section has delivered a coherent analysis of the digital technologies and their applications in the healthcare sector for obtaining improved consumer health services. Integration of digital technologies with the traditional healthcare system and techniques is becoming a new trend and immensely prevalent in modern-age healthcare practices worldwide. The key areas, where common use of digital technologies is observed, include facilitation of clinical support, observation of the spread of infectious diseases, monitoring the quality of healthcare services, looking for resources of medical knowledge and tracking supplies of vaccines and drugs (World Health Organisation, 2018). Advancement of healthcare technologies like virtual reality (VR) or augmented reality (AR), AI, nanotechnology, robotics and others in shaping the future of healthcare thereby exploring a wide range of opportunities. A comprehensive analysis of information from relevant sources may help in an in-depth understanding of the modern-age improved digital tools and technologies that assist in healthcare service enhancement thereby changing the dimensions of global health systems. The following analysis leads to an elaboration of the advantageous effects of these technologies alongside describing the changing trends.
1. Sustainable and responsive healthcare
Increasing health-related complications are creating pressure on the overall healthcare system and in this context, digital health platforms are allowing patients to access services easily (Inofab Health, 2019). By promoting the idea of self-care, these platforms also minimise burdens on the healthcare system.
2. Prevention before treatment
Digital health technologies assist in the self-management of patients’ health conditions by tracking symptoms and monitoring regularly. Early detection of changes in disease progression is also possible by these technologies.
3. Easy reach to healthcare professionals
The administrative burden of healthcare professionals is reduced with the help of such digital health innovations, thus actual time for patient monitoring and contact is expanded. Patients can also provide information regarding their health condition to the physicians at any time (Inofab Health, 2019).
3.1.7 Analysis of the potential impact of AI in increasing the accuracy level and safety for diagnosis
The research has fallen important in recent times as technology has advanced and digitalization has taken place. It has been evident from the discussion that technology is being adopted by organizations from across the world. However, in this context, it can be said that with each passing day, the health conditions of people are getting complexed and it is getting impossible to deal with them in a faster manner. Thus, the use of technology such as artificial intelligence falls important and relevant. As seen in 2020, the increased active cases of “Novel coronavirus” have affected a lot of people crossing lacs and billions all over the world. Therefore, it is essential to understand the impact of technology in tackling such a situation and thereby analysing its effectiveness.
From this state of dialogue, it’s emerged that technology has developed itself highly reflecting its emerging significance within the healthcare industry. As per the author, it are often said that AI has essentially increased the accuracy level concerning the services provided by the healthcare sector. Moreover, the incorporation of algorithms has incredibly increased the info analysis which successively resulted in driving out desired outcomes. Upon closer check out the market graph, it’s been analysed that there’s an opportunity that the implementation of machine learning and robotics can grow more in future years. Additionally, it’s been expected that the market features a chance of growing within the coming years showcasing its full control over the planet and particularly within the healthcare sector. However, with the contrasting views of other authors, it’s emerged that AI has essentially developed knowledge of healthcare workers especially doctors and researchers within the field. This, in turn, has effectively helped within the discovery of latest medicines as technology has helped to spot the new elements of science.
3.1.8 Analysis of investigating the impact of AI in reducing cost and enhancing patient care
Cost is an integral part of organizations without which financing operations cannot be performed. As emerged from the discussion and views of authors, it has arisen that cost reduction has been possible with the application of artificial intelligence and its associated technologies. Upon closer analysis, it has emerged that cost-effective equipment has been identified with the use of AI. Moreover, it can be stated that it has helped in improving the operational efficiency of the industry thereby removing the extra expenses that essentially lowered the performance. On the other hand, it has been observed that although costs have been reduced, yet artificial intelligence technology has assisted the sector in maintaining its quality. Hence, it can be stated that with the reduction in cost, it has been an advantage for the industry to incorporate new equipment and technologies that have the potential of developing the sector in a wider manner. Furthermore, in this discussion, the opinion of the author reflected that with the cost reduction effect, doctors have been able to avail better treatment options with the suitable techniques that AI has invented.
In accordance with the patient care system, it has been seen that with the analysis of big data and learning the relationship between them with the help of algorithms and machine learning, it has been possible to secure patients. In this context, it has emerged that workers have been able to identify the health conditions of the patients by analysing their past reports. Moreover, the identification of trends and patterns relating to the habits of patients has been effectively evaluated with the help of algorithms. In this respect, it can be said that the identification system has enabled the professionals in understanding the situation with an in-depth analysis. It has also associated them in understanding the treatment structure applicable for each patient. Furthermore, it has arisen that the efficiency level of workers has increased as they have been able to treat their patients rightly. This indeed has reduced the patients’ stay at the hospital reflecting the success of patient care. This, in turn, reflected the enhanced patient care at the hospitals that have emerged because of artificial technology.
On the opposite hand, as per the views of the authors, it’s been found that in remote villages, where doctors aren’t present, technology-driven tools have essentially helped in treating such patients. During this regard, it are often said that with image analysis technology, it’s been possible for doctors to ascertain the pictures of patients virtually and understand their problems. Moreover, with the assistance of digital technologies like smartphones, it’s been possible for workers to elucidate to patients about their problems and therefore the medicines that are needed for them. Therefore, concerning the discussion, it’s arisen that the impact of AI has been positive within the concerned sector and it’s effectively helped the industry in purchasing required equipment at a lower cost. Moreover, it’s also developed the medical services showcasing the increased patient care system at the hospitals.
This research has been different as the researcher has followed a mono method resembling qualitative data analysis. In this context, it can be stated that with the implementation of themes and patterns, it has been possible to develop knowledge and gather the information that is not usually gained through quantitative data or statistical analysis. The improvement in specific sectors in the healthcare sector has been acknowledged with the help of qualitative data. Furthermore, it can be said that this study has enlightened the different AI-based technologies that emerged recently in the market. Moreover, it has reflected the improvement that the healthcare sector has been making with the use of such technologies. In this case, as emerged from the discussion, it can be stated that with the evidence from the UK and other countries, artificial intelligence has gained a substantial place in the world.
Upon closer analysis of this research, it’s been analysed that within the healthcare industry machine learning has been introduced to analyse big data thereby improving the services. Moreover, it emerged from the sooner discussion that with cost-effective treatment systems, professionals within the industry are ready to treat patients from remote villages also. During this context, it’s been observed that with a well-equipped system, workers within the sector are ready to understand the habits of patients during a better way which essentially helped them find better treatment solutions. As stated by Puaschunder (2019), there has been hierarchical modelling thanks to the utilization of AI-based technologies within the medical field. Additionally to the present, scientific discoveries have essentially developed the healthcare system with enhanced solutions for critical diseases. AI technologies are getting popular alongside the growing application of the “Internet of Medical Things or IoMT” in several consumer health applications.
As per the study of past works of literature, it has arisen that in the USA, surgical robots have been already acknowledged that can create, see specific and minimally invasive incisions thereby stitching wounds and much more. In this respect, it can be said that by analysing different pieces of literature, evidence and relevance of artificial intelligence have been gathered. This has exceptionally taken the research to an enhanced level and made it different from other researches. Furthermore, with the incorporation of discussed research tools, the present research has given enough opportunity to explore the scenario related to the topic. In other words, it has provided a chance to explore areas of artificial intelligence and its prevalence in the healthcare system across the globe.
According to the study conducted by Bartoletti, (2019) it has been analysed that deployment of Artificial Intelligence in the healthcare field has not only provided effective security and safeguard towards crucial healthcare patient-centric data and ethical boundaries, but its actual impact on the patients has also created some effective privacy and ethical challenges. In order to overcome these challenges it is highly required to build trust within algorithm deployment within the healthcare sectors and in this context, it is highly required to access the data privacy impact assessments to analyse the way, privacy has enhanced technological deployment in safeguarding patient-centric data. The audit trials are required to be accurate through which the hospitals can track the changes made within the system. The healthcare sectors will need to focus on implementing procurement laws for managing the healthcare setting for adhering to third-party needs. On the other hand, the application of mobile apps, chatbots, wearable devices, connected devices has shown that the way the patient-centric data have been collected through using the devices, there is a significant probability of raising potential issues associated with code of ethics and privacy. The major analysis on the issues associated with the AI-related challenges the dichotomy associated with the core challenges reconciles privacy and in this context, the audits into the algorithms can be acted as potential regulators for ensuring the proper implementation of the healthcare operations. Mainly the deployment of Artificial Intelligence within the healthcare sector has established effective cooperation between machines and doctors and it can be represented as a significant turning point to deal with different diseases and for promoting the wellbeing of the patients. Considering the targeted and precise medicinal learner processes and back-office operations the application of AI can support independent living to achieve significant diagnostic ability. However, the application of AI tools is highly evident in raising potential boundaries and challenges within the contemporary regulatory systems of the medical and healthcare infrastructure and the privacy principles. That is the main reason, it is important for the healthcare sectors to adopt AI systems with a cautious approach for maximizing the positive outcomes towards patient-centric care by minimizing the risk associated with bias, privacy, and ethical harms.
4.1.2 Increasing accuracy and safety of diagnosis with the implementation of the AI to develop healthcare infrastructure
Based on the study conducted by Zhang et al., (2021), it has been analysed that the application of AI tools has significantly impacted healthcare functionalities through promoting multi-functional machine learning approaches. It has helped in developing health intelligence, resource management, and medical precisions. Considering enhancement of the accuracy of health intelligence, the use of AI tools can deliver better preventive, detective, diagnosis, and treatment approach towards controlling diseases. This study has indicated the role of AI in enhancing the physician and patient-centric relationship through enhancing empathy and emotional intelligence. It has been analysed that implementation of deep learning algorithms would increase the flow of data and may allow the technical equipment to manage the complex functionalities through developing the predictive nature. For instance, the development of deep convoluted neural networks for detecting skin cancer, image analysis for evaluating diabetic retinopathy can be quite beneficial. On the other hand, the use of mobile-based AI platforms can be used for measuring direct oral anticoagulants, and visit length reduction of the patients. On the other hand, it can provide better patient-centric interactions by detecting the necessary structural changes associated with the healthcare and medicinal systems. It has been emphasized that the development of AI concepts within medicine can promote personalized treatment and diagnosis through gathering effective collective and patient-centric experiences. However, the application of the AI models can establish both complex and general associations for accurate data interpretation.
The application of AI within the healthcare sector has significantly increased the availability of healthcare information through the rapid development of different data analytic approaches. Though the conducted by Genc et al., (2021) has addressed that the AI-based implementation within the healthcare sectors has mainly focused on limited health conditions, such as neural diseases, cancer, as well as cardiovascular diseases. Therefore, it is quite clear that the healthcare system has not engaged in delivering incentives for sharing data. Moreover, the application of AI has consisted of different algorithms for clustering and extracting the crucial patient-centric data to make real-time interferences for health outcome predictions, health risk alerts, reduced therapeutic and diagnostic errors, and perform principal component analysis. On the other hand, the SVM tool can be used for determining the model parameters as well as identification of the imaging biomarkers; and NLP can be adopted for text processing classification for promoting deep learning in imaging and electronic diagnosis. Based on the research conducted by Zhang et al., (2021), it has been evaluated that AI data analytics and Machine Learning can play an effective role in the identification of different diseases. For instance, the application of different AI-based algorithms can be beneficial for detecting the potential predictors for analysing the presence of PFX or Pseudoexfoliation Syndrome, and in this case, the use of NLP would help in evaluating both the predictive positive and negative values. These can be validated with glaucoma specialists.
In case AI-based precision medicine approaches it has been analysed that the collaboration of EMR analysis can help in preventing and potential diseases and solving different healthcare issues by promoting advanced analytics, point-of-care and personalized treatment. According to the study conducted by Ahmed et al., (2020) it has identified the existence of different healthcare institutions which have used EMR system for tracking the overall medical history of the patients the research has shown the significance of patient perspectives and the requirement of foundation plot for precision medicine which can be supported through integration of large scale clinical data as well as communication among the different accessible EMRs for the patients by different healthcare sectors. This research has addressed that universal EMR platform which has developed from the perspectives of integrated populations in order to establish the protocols associated with subgroups regarding distinct clinical phenotypes related to complex diseases and deliver avenues to differentiate the treatment methods. Precision Medicine refers to the effective and individual medical treatments which are mainly provided by the clinicians based on different characteristics and susceptibilities associated with different diseases among diversified patients. For instance, the use of trastuzumab for HER21-positive breast cancer. On the other hand, AI has played a major role in delivering advanced analytics point-of-care to patients. Here, the study has evaluated the importance of different innovative technologies, such as health sensors, genome sequencing, advanced biotech, and so on. One of the most essential roles associated with AI in the implementation of Precision medicine, the research has addressed three-factor-based approaches, such as large-scale clinical dataset, point of care, for building foundations for precision medicine. According to the research conducted by Mohamed et al., (2020) it has been analysed that the combination of deep learning predictions associated with the diagnosis of human pathologists has resulted in a significant success rate of 99.5% through which the human errors have been minimized by 85%. This research has addressed effective guidelines of AI implementation within precision medicine, such as gradual and incremental development of AI, development of ethical standards for the application of AI, and conducting training on AI to the medical professionals.
The AI-based Machine Learning procedure has significantly classified cancer through visual assessment of the tumour cells. This study has reflected major technological advancement where microscope-based analysis has been used for the diagnosis of brain tumours. Considering the visual restrictions like different classifications of provided samples by the individuals have addressed precise solutions for accurate diagnosis through evaluating the molecular information. According to another study conducted by (), it has been identified that the use of AI classifiers at an intense rate can classify the images of tumours for analysing the CNS or Central Nervous System to collect the information associated with tumour-type genome-wide methylation. It has helped in analysing the methylation patterns. The implementation of AI within healthcare resource management can help in mitigating ethical challenges, such as resource poor-setting, laboratory medicines, developing a workflow for reducing medical errors, and missing links regarding big-biomedical data. Based on the views of Alhashmi et al., (2019) it has been analysed that in the case of implementing AI within the healthcare sectors, there are some crucial challenges associated with the context. The major obstacles have been identified in pathology, radiology, ophthalmology, dermatology, cardiology, mental health, as well as gastroenterology. However, this research has adapted the predictive AI adaptation for the clinicians, which includes deep neural networks for recognition and analysis of different medical image patterns, such as pathology slides, medical scans, retinal images, skin lesions, endoscopy, electrocardiograms, dances, and vital signs. The application of the dep learning concept towards EHR may help in estimating potential risks associated with the readmission of the patients in hospitals supporting the doctors in making effective decisions related to determining and resuscitation of the patients for risk developing diseases, as well as critical diseases which led towards death and for predicting biological age.
According to the study conducted by Sun and Medaglia, (2019) it has been identified that adopting and implementing an AI-powered approach within the patient care healthcare system may significantly increase the efficiency of the healthcare services through promoting cost reduction. In this research, it has been analysed that text-based diagnosis radiology reports have played a crucial role in analysing the different types of medical data. This study has reflected effective findings associated with different types of imaging studies, such as CT or Computerized Tomography Scan, MRI or Magnetic Resonance Imaging, and so on. Considering the effectiveness and perceived usefulness of the AI Predictions, it has been analysed that high transparency local explanation has negligible impact on the perceived usefulness related to AI predictions. It has been observed that the use of predictive AI-based approach has revealed that model performance can significantly impact the human trust towards health AI systems, in case of higher performance the rate of AI predictions have become more high, useful, and trustworthy for the participants and an effective study has addressed that the patients are highly concerned about the accuracy and reliability of the AI system. There is another argument being identified regarding disagree participants, when the prediction of AI has become inaccurate, it has created a negative impact.
Considering the effectiveness of the perceived sufficiency of AI information, it has been analysed that, while the participants may disagree with the AI prediction, it may become worthless, and based on the equal information transparency level the participants have provided their positive interpretation towards AI model conditions. The AI-based prediction has become a transformational force within the healthcare industry through which the patients can be guided with different conceptual health benefits, such as disease pattern analysis, real-time patient monitoring, patient-centric care provisions, disease diagnosis, and medicinal prescriptions. It has included real-time patient monitoring, diagnosis of disease and medicinal prescriptions, disease pattern analysis, provision associated with patient-centric care for enhanced treatment. Prognostic scoring, clinical error reduction, therapeutic decision making, high risk for medical emergence and sepsis identification, screening claim data, outcomes from the clinical codes associated with autopsy reports, and death certificates. In the case of using the AI algorithms, it has been utilized the concepts of deep learning, SVM, DA, logistic regression, random forest, DA, linear regression, decision tree, KNN or K-nearest Neighbour, Naive Bayes, HMM, or Hidden Markov Model for detecting cancer, heart failure, and other crucial chronic diseases symptoms, such as interventions, risk predictions, clinical decision making, resourcing, and panelling. The effective use of SVM has helped in a vast variety of contributions within the medicinal field.
In this section, for comparing the effective approaches for the development of the impact of AI within the healthcare industry and the individual criteria, such as privacy, compatibility, skill level, cost, efficiency, complexity, and maintenance will be addressed below with the values.
Considering all the five selected papers, there is certain privacy level, and based on the facts associated with this research Privacy can be an effective and important criterion and it can be approached from different angles. The privacy level associated with the addressed AI solutions will be compared and it can be determined through the way the security of the individual AI tools and solutions may help in supporting the patient-centric information. The values refer to High, Moderate, and Low, where High refers to most secure, Moderate refers to medium secure, and Low refers to minimum privacy.
Compatibility mainly refers to the fact which can be defined by the approaches implemented and adapted on any platform (here the platform refers to AI tools and algorithms). In the current business environment, it is quite important to stay compatible with the available platforms. Compatibility can be measured with the values referring to Yes, No, and somewhat compatible. Considering the applicability of the approach associated with the criterion, different scenarios related to the application of the AI-based tools and algorithms within the healthcare sectors.
The skill level is an important criterion that will help in exploring the level at which the skills of the clinicians will be measured in the implementation of the AI solutions referred to in different selected papers. While measuring the criterion level, those clinicians who have been involved with implementing the AI tools have different expertise levels and for identifying those approaches which are suitable for the clinicians.
Cost is quite an effective factor associated with any kind of project and this criterion will state the level of the individual selected papers in terms of cost, how high or low will be the solutions provided by the researchers. It is considered as a defining factor and it will assist in recommending the accurate ascertain of the research paper. There will be some possible values, such as Scalable, Medium, and High. here, High refers to the capability of those healthcare sectors which may be able to invest for adopting the AI-based solutions, Medium refers to those healthcare sectors which are applicable for AI-based tools and algorithms implemented within the operations, and the Scalable refers to the capability of those clinicians that can manage its AI infrastructure based on the needs and capabilities of the healthcare organizations.
The criterion, Efficiency refers to the time duration which is mainly required for completion of different tasks. However, this research has required the significance of measuring the way of efficiency implementing different AI-based tools and algorithms. The more effective approaches will be, the more efficient the application of the AI tools and algorithms will become in defining and mitigating the challenges associated with providing patient-centric care. The values or levels associated with the Efficiency criterion include Ultra-efficient, Efficient, and Inefficient. Addressing the levels may help in defining the efficiency of AI-based solutions in managing healthcare services.
Though all the five selected papers have not been addressed the effectiveness of different AI-based tools and algorithms, there will be some sort of complexities that are required to be measured for analysing the approaches associated with different implementation processes of AI tools. In order to measure the complexities, the values include High, Medium, and Low. The High level refers to the most complicated approach and it can be used for mitigating the challenging issues within different AI-based scenarios within healthcare sectors.
Maintenance refers to the amount and types of resources required for operating the different AI-based tools and algorithms which are addressed in the selected papers. The low maintenance ascertains are those AI tools and algorithms which do not require a lot of resources for operating the healthcare services, medium refers to the moderate resource amount and high refers to the most operated resources for delivering the healthcare services.
Considering the compatibility it has been analysed that there are some AI-based tools that have been implemented by the clinicians for securing the healthcare and patient-centred data reporting systems. However, in the case of privacy, these tools are quite moderately maintained and their cost level has been medium due to the ineffective efficiency of the tools. The high required skill level and complexity associated with the AI-based tools have rated the moderate cost amount and maintenance criterion. Therefore, it is quite usual that the high complexity of the AI-based tools may require a high skill level of the clinicians for operating as well as filtering the overall system. On the other hand, involvement of the trustworthy clinicians may develop the privacy associated with securing healthcare information and patient-centric data.
Considering the low value of the privacy criterion it has been analysed that a significant amount of patients have not satisfied with the use of AI-based tools and algorithms and those individuals are not compatible with the AI-associated healthcare services. Moreover, the involvement of the clinicians with novice skill level and high maintenance criteria. How the low complexities within the selected AI-based tools may reduce the value of cost and the efficiency level of the tools has become enhanced in delivering accurate outcomes of the healthcare services.
There are different types of AI-based algorithms that have been adopted by the healthcare sectors where privacy is not applicable and that is the main reason for which the clinicians with intermediate skill levels are compatible with operating those tools. However, the medium range of cost and high efficiency in managing the clinical resources has influenced the healthcare sectors to adopt those AI tools. Furthermore, the moderate level of complexity criterion has set the maintenance of the tools to a medium rating.
In order to reduce the errors in the medical performance and healthcare services, the healthcare sectors have been identified to adapt several high performances AI-based medicinal treatment approaches which have highly secured privacy level but the main thing is that those approaches have somewhat or little amount of compatibility which has required expert level of skilled clinicians for using those with significant efficiency level. However, the existence of highly complex procedures has helped in giving accurate medical solutions with a moderate level of maintenance criterion.
Considering the concept of privacy and ethics associated with the implementation of the AI-based tools and algorithms within the healthcare sectors, it has been analysed that low levels of privacy and high complexity offered by the AI systems have created potential privacy and ethical challenges related to waste reduction, inefficient patient care, and so on. However, the existence of compatibility among the healthcare professionals in the adoption of the technology has required a moderate skill level and a high level of cost investment. From the standpoint of privacy and ethics, safeguarding the patient-centric data may have a high level of complexity.
Overall strengths and weaknesses of the approaches
|Securing data reporting and healthcare resource management||· Smart tracking of the patient centric data and clinical requirements
· Enhanced compatibility of utilizing the clinical information for giving better treatment to the patients
|· Existence of high complexity operations of the patient-centred data reporting system is a major weakness which may create potential challenge for the clinicians in operating and securing the healthcare data|
|Behavioural prediction of the patients and selection of the tools||· Developing the efficiencies for better operational management in the healthcare sectors
· Increasing accuracy of treatment and diagnosis in personal medical treatment
|· Lack of algorithm and regulatory bias
· Human intervention and moral hazard have mainly pointed the architectural dilemmas within the machine
|Resource management and deployment of AI-based algorithms||· Low complexity in the application of AI based algorithms for measuring the behavioural predictions of the patients reduces the requirement of the high skilled clinicians for delivering accurate outcomes.||· The low compatibility rate within the deployment of AI-based algorithms in managing resources may impact the clinicians to operate the AI-enabled healthcare services because it enhances the operating cost.|
|High performance AI-based medicinal treatment procedures and reduction of the medical errors||· Highly secured privacy level of the AI-based medicinal treatment procedure is an effective strength because it enhances the potential of the healthcare operational efficiency through reducing medical errors.||· Low levels of privacy and high complexity offered by the AI systems have created potential privacy and ethical challenges related to waste reduction, inefficient patient care, and so on.|
|Managing ethical and privacy challenges||· Moderate skills requited for managing the challenges
· Safeguarding the patient-centric data
|· In effective waste reduction, inefficient patient care, and so on
· Significant level of cost investment
4.3.6 Comparison of the individual approaches
The first paper mainly refers moderate privacy because of addressing the compatibility of the data security reporting system. In order to manage this reporting system properly the workers associated with this AI system will need expert level of technical skill; however, if the employees do not have the practical experience then proper training can help the individuals in becoming efficient users. The second paper mainly refers the AI tools, which are mainly used for behavioural prediction of the patients. In this context, the existence of the multiple usage of AI algorithms have made the system less compatible and lack of knowledge on multiple algorithms have drastically impacted the security of the system. It is the main reason, for which the maintenance charge of the system remains quite high. Even, the measurement of the accuracy has addressed the involvement of highly efficient employees engaged with the system. In third paper, the AI-based algorithms have been used for enhancing the resource management processes where being transparent about the resources is beneficial for the clinical sectors and that is the reason, here, no privacy criteria has been applied. Due to the existence of straight forward operations the complexity level of the operations are quite low and that is the reason, the involvement of intermediate level employees can easily manage this department. However, based on the forth and fifth paper where it has been addressed the high performance AI based operations for error reduction and managing ethical and privacy issues maintaining highly secured platform is quite a necessity for securing the important data associated with the different medicinal and clinical procedures and regulations for maintaining flexible environment within the organizations. Both the areas are associated with highly complex operations and the management procedures have highly cost consuming.
|Criteria||Paper 1||Paper 2||Paper 3||Paper 4||Paper 5|
|Privacy||Moderate||Low||Not Applicable||Highly Secure||Low|
Artificial intelligence is widely used in healthcare as it is an advanced technology having the huge potential to collect data and conduct independent analysis which ensures users and healthcare professionals receive reliable and arranged output. AI’s significance includes providing results or insights after analysing the vast amount of data (Bini, 2018). The technology integration has overall encouraged better treatment process and quick health services to the patients. The major improvement brought to the healthcare sector by AI is that automation. However, the medical professionals and patients have been able to provide and receive medication properly due to the development of radiology tools which are also the outcome of the integration of AI. Such improvements in radiology have been helping to pace up the treatment process of cancer patients. The care, in general, can be more effective as the rate of appropriate diagnosis has been improved (Gordon & Catalini, 2018). The rate of flaws has dropped to a great extent over time. For example, MRI machines, CT scanners and X-ray and more are the results of the strong integration of AI to having non-invasive visibility inside the human body.
From the report, it is concluded that artificial intelligence has the capacity of improving the Quality of diagnosis and treatments of patients. It is also concluded that virtual assistance powered with artificial intelligence algorithms has the capacity to deliver assistance to patients in real-time. These applications have the ability to reduce costs and create autonomous treatment plans. There is immense potential for artificial intelligence to reduce the cost of healthcare and minimising the frequency of human errors and visits to the doctor. The technology has the capacity of advising the doctors based on the information gathered from the patient data and reduce the errors in dosage and chances of fraud. Automated image diagnosis capacity of the assistance has the potential to replace the radiologist in the future along with delivering the benefit of virtual nursing assistants to mitigate the shortage of staff.
Based on the different behavioural patterns of the patients towards the application of AI systems within the healthcare operations it has become quite essential to recognize the distinction between both the sensitive and personal patient-centric data where data privacy, algorithmic effect, audit trials, procurement laws, and governance framework have significantly influenced the patients in gaining the trust attributes towards the deployment of AI system algorithms within the healthcare operations. The cultural norms and malicious use of patient-centric data have significantly decreased the public trust towards the AI application by online manipulation and behavioural advertising. The Cambridge Analytical Scandal can be considered as one of the most prominent examples in this context.
Considering the above description, it has been identified that the deployment of the AI system algorithms has significantly impacted trust management within the healthcare sector through rising potential ethical and privacy issues. In order to mitigate this limitation and challenging circumstance, it is quite important to establish greater cooperation between the clinicians and machines for developing the potential to tackle different diseases and promoting wellbeing. Based on targeted and precision medicines within the healthcare back-office operations will promote the freedom for elderly patients in greater diagnostic ability.
According to the adoption and acceptance of technological development, human trust has been considered as a critical factor in the adoption of AI systems within the healthcare sector. However, it has been analysed that the existence of different AI-powered healthcare systems have engaged in developing heterogeneous information and it has exploited the demand for healthcare data for patient satisfaction, resource optimization, and improved healthcare quality outcomes. However, the use of AI-powered applications has influenced machine learning for the advancement of health information analytics with proper arrangement of healthcare processes and it has mainly required the support of the therapeutic decision, ubiquitous services, and integration of crucial health-related data sources, health surveillance, drawing causal relationships between different inherent data elements, predictive and personalized medicines through analysing nonlinear associations. The Neurological Research Institute of Birmingham is an ideal example where a team of Medical Scientists has collaboratively designed an AI-powered Health Data analytics process by using an effective methodological order associated with data modelling, data processing, and data analysis which are mainly categorized as potential knowledge-driven methods for predictive, decision, descriptive, comparative, optimization, and prescriptive, as well as semantic analysis. The implementation of AI-powered machine learning-based analysis has helped in pre-processing of the health information, algorithm selection, development of analytical models, and interpreting the outcomes with patient-centric data.
Based on the above discussion it has been analysed that the development and implementation of AI-powered Healthcare Systems within the healthcare centres have raised potential accessibility and reliability issues and in order to mitigate these issues, it is important for the clinicians and healthcare managements to focus on acquiring more augmented interoperability and networking data for integrating the clinical public health systems through which it will be quite easy to address the social and ethical issues for protecting and maintaining an effective balance of the diversified healthcare data. It is highly required for the clinicians to skip the time-consuming manual data handling process and will need to focus on extracting the healthcare data by using operational clinical systems. Thus, the individuals will be able to distinguish the difference between both the rare and common functional variants and the use of metabolite penetrance will help in listing the abnormalities of the patients and examining its relations between metabolite levels and genomic variations. The effective analysis of biochemical roots within different metabolite patterns may help in multinomial distributions of genes and healthcare assimilation developing the transition and quality of the healthcare operations.
5.2.3 The scenario on better healthcare and precision medicinal infrastructure with multi-functional AI platforms
In order to deliver better healthcare services and for promoting the precision medicinal infrastructure the clinical scientific community throughout the globe has been involved in developing a better healthcare and precision medicinal infrastructure through transitioning several trials and error reduction systems by using the concept of multi-functional AI systems and the individuals have the ability to develop some of the innovative technologies, such as health sensors, genome sequencing, advanced biotech, and so on. However, the application of these innovative technologies has created potential accessibility and privacy issues within the healthcare system and the consequences that happened in California Medical Institute of Cardiac Research is quite an effective example where the Digital Imaging and Radiology department has faced DICOM related challenges for the generation of unstructured reports. The pathology department has faced the same issues.
Considering the above analysis, it is quite clear that the use of multi-functional AI platforms in the development of better healthcare and precision medicinal infrastructure potential accuracy and security issues have been raised and in order to mitigate these issues, it is quite important for the healthcare sectors to adopt universal data models for promoting the personalized healthcare delivery services through tracking different patient-specific patterns associated with progression of different diseases. Moreover, it will also help in determining precise therapies.
Ahmed, Z., Mohamed, K., Zeeshan, S. and Dong, X., 2020. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020.
Alhashmi, S.F., Salloum, S.A. and Abdallah, S., 2019, October. Critical success factors for implementing artificial intelligence (AI) projects in Dubai Government United Arab Emirates (UAE) health sector: applying the extended technology acceptance model (TAM). In International Conference on Advanced Intelligent Systems and Informatics (pp. 393-405). Springer, Cham.
Bandura, A., 2002. Social cognitive theory in cultural context. Applied psychology, 51(2), pp. 269-290.
Bartoletti, I., 2019, June. AI in healthcare: Ethical and privacy challenges. In Conference on Artificial Intelligence in Medicine in Europe (pp. 7-10). Springer, Cham.
Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T. and Tsaneva-Atanasova, K., (2019). Artificial intelligence, bias, and clinical safety. BMJ Quality & Safety, 28(3), pp.231-237.
Danielsson, K., Lindgren, H., Mulvenna, M., Nilsson, I. and Waterworth, J., (2017), September. Digital technology in healthcare and elderly care. In Proceedings of the European Conference on Cognitive Ergonomics 2017 (pp. 188-190).
Davenport, T, and Kalakota, R (2019), The potential for artificial intelligence in healthcare, Future Healthcare Journal 2019 Vol 6, No 2: 94–8
Davenport, T. & Kalakota, R., 2019. The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), p. 94.
Deloitte, 2021. 2021 Global Health Care Outlook. [Online]
Available at: https://www2.deloitte.com/global/en/pages/life-sciences-and-healthcare/articles/global-health-care-sector-outlook.html
[Accessed 21 August 2021].
Health Management, (2020), The Impact of Digital Technology on Healthcare, Available at https://healthmanagement.org/c/cardio/news/the-impact-of-digital-technology-on-healthcare [Accessed on 17/08/21]
Househ, Dr. M, (2021), The Importance of Digital Health Technologies in a Pandemic, Available at https://www.hbku.edu.qa/en/news/importance-digital-health [Accessed on 17/08/21]
Inofab Health, 2019. 5 Benefits of Digital Health Systems. [Online]
Available at: https://www.inofab.health/blogs/articles/5-benefits-of-digital-health-systems
[Accessed 18 August 2021].
Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G. and King, D., (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC medicine, 17(1), pp.1-9.
Khanna, D., (2018). Use of Artificial Intelligence in Healthcare and Medicine. International Journal Of Innovations in Engineering Research And Technology.
Laurenza, E., Quintano, M., Schiavone, F. and Vrontis, D., (2018). The effect of digital technologies adoption in the healthcare industry: a case-based analysis. Business process management journal.
Lobe, B., Morgan, D. and Hoffman, K.A., (2020). Qualitative data collection in an era of social distancing. International Journal of Qualitative Methods, 19, p.1609406920937875.
Macrae, C., (2019). Governing the safety of artificial intelligence in healthcare. BMJ quality & safety, 28(6), pp.495-498.
Markets and markets, (2020), Artificial Intelligence in Healthcare Market, Available at https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-healthcare-market-54679303.html [Accessed on 17/08/21]
Marr, B, (2018), How Is AI Used In Healthcare – 5 Powerful Real-World Examples That Show The Latest Advances, Available at https://www.forbes.com/sites/bernardmarr/2018/07/27/how-is-ai-used-in-healthcare-5-powerful-real-world-examples-that-show-the-latest-advances/?sh=7399bca35dfb [Accessed on 17/08/21]
MAYA RAGHUNANDAN, 2018. 5 Real-World Examples of AI in Healthcare. [Online]
Available at: https://www.kolabtree.com/blog/5-real-world-examples-of-ai-in-healthcare/
[Accessed 18 August 2021].
Mckinsey, 2020. Transforming healthcare with AI: The impact on the workforce and organizations. [Online]
Available at: https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/transforming-healthcare-with-ai
[Accessed 18 August 2021].
Michael Reddy, 2021. Digital Transformation in Healthcare in 2021: 7 Key Trends. [Online]
Available at: https://www.digitalauthority.me/resources/state-of-digital-transformation-healthcare/
[Accessed 18 August 2021].
Nabi, M.K., (2019). The impact of artificial intelligence (AI) on workforce in emerging economies. Global Journal of Management And Business Research.
NHS, 2021. Technology to improve service. [Online]
Available at: https://www.england.nhs.uk/improvement-hub/wp-content/uploads/sites/44/2017/11/ILG-1.6-Use-of-Technology-to-Improve-Services.pdf
[Accessed 21 August 2021].
Puaschunder, J.M., (2019). Artificial Intelligence in the Healthcare Sector. Scientia Moralitas-International Journal of Multidisciplinary Research, 4(2), pp.1-14.
Pwc, (2021), No longer science fiction, AI and robotics are transforming healthcare, Available at https://www.pwc.com/gx/en/industries/healthcare/publications/ai-robotics-new-health/transforming-healthcare.html [Accessed on 17/08/21]
Reddy, S., Fox, J. and Purohit, M.P., (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), pp.22-28.
Ryan, G., (2018). Introduction to positivism, interpretivism and critical theory. Nurse researcher, 25(4), pp.41-49.
Statista, (2021), Projected global digital health market size from 2019 to 2025*, Available at https://www.statista.com/statistics/1092869/global-digital-health-market-size-forecast/ [Accessed on 17/08/21]
Sun, T.Q. and Medaglia, R., (2019). Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2), pp.368-383.
Sun, T.Q. and Medaglia, R., 2019. Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2), pp.368-383.
The King’s Fund, 2018. The health care workforce in England: make or break?. [Online]
Available at: https://www.kingsfund.org.uk/publications/health-care-workforce-england
[Accessed 21 August 2021].
Vaishya, R., Javaid, M., Khan, I.H. and Haleem, A., (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), pp.337-339.
Who, (2021), Digital technologies: shaping the future of primary health care, Available at https://www.who.int/docs/default-source/primary-health-care-conference/digital-technologies.pdf?sfvrsn=3efc47e0_2(accessed [Accessed on 17/08/21]
Williams, A. et al., 2018. Artificial intelligence, physiological genomics, and precision medicine. Physiological Genomics, 50(4), pp. 237-243.
World Health Organisation, 2018. Digital technologies: shaping the future of primary health care. [Online]
Available at: https://www.who.int/docs/default-source/primary-health-care-conference/digital-technologies.pdf?sfvrsn=3efc47e0_2(accessed#:~:text=In%20this%20context%2C%20common%20uses,supplies%20of%20drugs%20and%20vaccines
[Accessed 18 August 2021].
Zhang, Z., Genc, Y., Wang, D., Ahsen, M.E. and Fan, X., 2021. Effect of AI Explanations on Human Perceptions of Patient-Facing AI-Powered Healthcare Systems. Journal of Medical Systems, 45(6), pp.1-10.
|Task Name||Duration||Start||Finish||Resource Names|
|Introduction||Week6||23/08/2021||29/08/2021||Gathering secondary sources|
|Research Significance and Analysis||Week9||13/09/2021||19/09/2021||“|
|Findings and Discussions||Week10||20/09/2021||26/09/2021||“|
|Meeting and getting feedback from the tutor||Week11||27/09/2021||03/10/2021||“|
|Conclusion and final submission||Week 12||04/10/2021||10/10/2021||“|