Artificial Intelligence in Health Care - Applications, Possible Legal Implications and Challenges of Regulation

The integration of artificial intelligence (AI) in healthcare has the potential to revolutionize the industry by improving patient outcomes and increasing efficiency. However, the rapid development and implementation of AI technologies raise complex legal issues and challenges. This article explores the key legal aspects of AI integration in healthcare, including data privacy and security, liability and accountability, intellectual property, and regulatory compliance. It examines relevant international and national legal instruments, regulations, and guidelines, as well as industry-specific standards that apply to AI in healthcare. The study also analyzes case studies and practical applications to highlight legal challenges and resolutions, lessons learned, and best practices. The discussion addresses the implications of the results, comparing the legal landscape for AI in healthcare to other industries and countries and highlighting potential future legal developments and challenges. The conclusion summarizes key findings, offers recommendations for integrating AI in healthcare systems while addressing legal concerns, and proposes future directions for legal research and policy development in the context of AI and healthcare. This comprehensive analysis aims to inform healthcare providers, AI developers, and policymakers on the legal landscape surrounding AI in healthcare, providing valuable insights to navigate this complex domain and harness the potential of AI to transform healthcare delivery.

This article examines the potential implications of the implementation of artificial intelligence (AI) in health care for both its delivery and the medical profession. To this end, the first section explores the basic features of AI and the yet theoretical concept of autonomous AI followed by an overview of current and developing AI applications. Against this background, the second section discusses the transforming roles of physicians and changes in the patient-physician relationship that could be a consequence of gradual expansion of AI in health care. Subsequently, an examination of the responsibilities physicians should assume in this process is explored. The third section describes conceivable practical and ethical challenges that implementation of a single all-encompassing AI healthcare system would pose. The fourth section presents arguments for regulation of AI in health care to ensure that these applications do not violate basic ethical principles and that human control of AI will be preserved in the future. In the final section, fundamental components of a moral framework from which such regulation may be derived are brought forward, and some possible strategies for building a moral framework are discussed.

This study focuses on the potential application of Artificial Intelligence (AI) in healthcare and hospitals to improve the quality of services for patients. The research objectives include the investigation of existing AI use cases in healthcare, exploration of potential areas in which AI can best be applied, and identification of the challenges to successful AI application. This research utilizes both primary and secondary data sources to investigate the potential of AI in healthcare and hospitals. The primary data is collected through published research papers, technical reports, and industry news to gain an understanding of the current state of AI applications in healthcare. The secondary data is gathered from expert opinions with experienced healthcare professionals such as physicians, hospital administrators, and IT experts to gain insights into existing use cases and potential applications of AI in healthcare and hospitals. The results of the study demonstrate that AI has a significant potential to deliver enhanced outcomes in various aspects of healthcare and hospitals, including diagnosis, treatment, and management. However, the successful integration of AI requires overcoming numerous challenges such as regulatory standardization, privacy protection, and data availability. To foster a positive development of AI in healthcare, it is recommended that healthcare organizations enhance their digital capabilities, enable secure data sharing and collaboration, and use AI tools to deliver a more comprehensive and personalized patient care experience.

The development of Artificial Intelligence (AI) in healthcare has had a significant impact on healthcare. AI in healthcare can provide more accurate diagnoses and interventions for patients. AI can predict, diagnose, and treat diseases, facilitate the maximum use of healthcare resources by integrating medical information, increase efficiency, and reduce overcrowding of healthcare resources. However, the application of AI in healthcare also faces challenges such as accountability, algorithmic security, and data privacy. This paper discusses the application of AI in healthcare and explores the challenges faced by AI, in-cluding accountability traceability, algorithmic safety, data security, and ethical issues, and makes targeted recommendations. This study provides an in-depth exploration of the application of AI in healthcare, helping to improve the accuracy and efficiency of AI ap-plications in healthcare, as well as providing necessary guidance and references for opti-mizing and enhancing AI technologies.

Purpose (stating the main purposes and research question) Anthropogenic resource use contributes to pollution, violent conflict over scarce resources, loss of biodiversity, and diminished quality of life for humans. Moreover, the “safe” amount of carbon dioxide—350 parts per million—has been exceeded. The health care industry is responsible for 4–5% of total world emissions,[i] which is similar to the global food sector.[ii] Health care carbon emissions come from health care infrastructures, supply chains and health care delivery. Increasingly, health care delivery is reliant on technologies which require the use of artificial intelligence to provide supportive care, such as triage algorithms, electronic patient records, and robotics.[iii] While these technological innovations have advanced health care significantly, they also contribute to the negative effects on the environment, among others, through carbon emissions. The environmental impacts of artificial intelligence (AI) in health care—in particular—are understudied. This research seeks to fill this gap. Methods Our team ran an exploratory search in Scopus and PubMed to identify studies that integrate environmental sustainability, artificial intelligence, and health. Results Our research initially yielded 735 studies. 77 of these studies focused on an environmental concern of a health technology or AI-application in a health care setting, but most of the articles in this subset addressed lowering energy consumption of a specific technology, such as a sensor or monitoring technology. Conclusions While there have been studies looking at AI in health care; sustainability in AI; and sustainability in health care, little attention has been paid to the interface between all three. [i] Karliner, J., Slotterback, S., Boyd, R., Ashby, B., & Steele, K. 2019. Health Care’s Climate Footprint: How the Health Sector Contributes to the Global Climate Crisis and Opportunities for Action Healthcare Without HarmARUP; September. [ii] Pichler, P. P., Jaccard, I. S., Weisz, U., & Weisz, H. 2019 International Comparison of Health Care Carbon Footprints, Environmental Research Letters 14, no. 6: 064004. [iii] Khaliq, Abdul, Ali Waqas, Qasim Ali Nisar, Shahbaz Haider, and Zunaina Asghar. 2022. Application of AI and robotics in hospitality sector: A resource gain and resource loss perspective. Technology in Society 68: 101807.

BackgroundWhile use of artificial intelligence (AI) in healthcare is increasing, little is known about how patients view healthcare AI. Characterizing patient attitudes and beliefs about healthcare AI and the factors that lead to these attitudes can help ensure patient values are in close alignment with the implementation of these new technologies.MethodsWe conducted 15 focus groups with adult patients who had a recent primary care visit at a large academic health center. Using modified grounded theory, focus-group data was analyzed for themes related to the formation of attitudes and beliefs about healthcare AI.ResultsWhen evaluating AI in healthcare, we found that patients draw on a variety of factors to contextualize these new technologies including previous experiences of illness, interactions with health systems and established health technologies, comfort with other information technology, and other personal experiences. We found that these experiences informed normative and cultural beliefs about the values and goals of healthcare technologies that patients applied when engaging with AI. The results of this study form the basis for a theoretical framework for understanding patient orientation to applications of AI in healthcare, highlighting a number of specific social, health, and technological experiences that will likely shape patient opinions about future healthcare AI applications.ConclusionsUnderstanding the basis of patient attitudes and beliefs about healthcare AI is a crucial first step in effective patient engagement and education. The theoretical framework we present provides a foundation for future studies examining patient opinions about applications of AI in healthcare.

Artificial intelligence technology can be applied in several aspects of healthcare delivery and its integration into the Nigerian healthcare value chain is expected to bring about new opportunities. This study aimed at assessing the knowledge and perception of healthcare professionals in Nigeria regarding the application of artificial intelligence and machine learning in the health sector. A cross-sectional study was undertaken amongst healthcare professionals in Nigeria with the use of a questionnaire. Data were collected across the six geopolitical zones in the Country using a stratified multistage sampling method. Descriptive and inferential statistical analyses were undertaken for the data obtained. Female participants (55.7%) were slightly higher in proportion compared to the male respondents (44.3%). Pharmacists accounted for 27.7% of the participants, and this was closely followed by medical doctors (24.5%) and nurses (19.3%). The majority of the respondents (57.2%) reported good knowledge regarding artificial intelligence and machine learning, about a third of the participants (32.2%) were of average knowledge, and 10.6% of the sample had poor knowledge. More than half of the respondents (57.8%) disagreed with the notion that the adoption of artificial intelligence in the Nigerian healthcare sector could result in job losses. Two-thirds of the participants (66.7%) were of the view that the integration of artificial intelligence in healthcare will augment human intelligence. Three-quarters (77%) of the respondents agreed that the use of machine learning in Nigerian healthcare could facilitate efficient service delivery. This study provides novel insights regarding healthcare professionals' knowledge and perception with respect to the application of artificial intelligence and machine learning in healthcare. The emergent findings from this study can guide government and policymakers in decision-making as regards deployment of artificial intelligence and machine learning for healthcare delivery.

Objectives The use of artificial intelligence (AI) in healthcare is a growing field of research and clinical application. The views of the general public, ie future healthcare users, need to be surveyed and interpreted so that researchers and the public have a shared understanding of the appropriate use of AI. Currently, there is only limited data on the public’s views. Methods An anonymous, quantitative questionnaire was administered as part of a public exhibition on AI. The questionnaire was based on previously validated questions designed to assess respondents’ views on the use of AI in healthcare. Brief demographic data were also collected. Results The population surveyed was more diverse and younger than the general UK population (65% white, 45% aged 18-29). Respondents were largely comfortable with the application of AI in healthcare: 80% felt positively about its use, 56% thought it would be safe. 70% did not feel that it would replace doctors, and most would not be happy for AI to make decisions without considering their feelings. Conclusions Our study shows that the population we surveyed, particularly young future healthcare users, are comfortable with the use of AI in healthcare, but do not see it as a replacement for doctors. Advances in knowledge This paper highlights views from the general public on the use of AI in healthcare, which is largely under researched.

Background: With the availability of extensive health data, artificial intelligence has an inordinate capability to expedite medical explorations and revamp healthcare.Artificial intelligence is set to reform the practice of medicine soon. Despite the mammoth advantages of artificial intelligence in the medical field, there exists inconsistency in the ethical and legal framework for the application of AI in healthcare. Although research has been conducted by various medical disciplines investigating the ethical implications of artificial intelligence in the healthcare setting, the literature lacks a holistic approach. Objective: The purpose of this review is to ascertain the ethical concerns of AI applications in healthcare, to identify the knowledge gaps and provide recommendations for an ethical and legal framework. Methodology: Electronic databases Pub Med and Google Scholar were extensively searched based on the search strategy pertaining to the purpose of this review. Further screening of the included articles was done on the grounds of the inclusion and exclusion criteria. Results: The search yielded a total of 1238 articles, out of which 16 articles were identified to be eligible for this review. The selection was strictly based on the inclusion and exclusion criteria mentioned in the manuscript. Conclusion: Artificial intelligence (AI) is an exceedingly puissant technology, with the prospect of advancing medical practice in the years to come. Nevertheless, AI brings with it a colossally abundant number of ethical and legal problems associated with its application in healthcare. There are manifold stakeholders in the legal and ethical issues revolving around AI and medicine. Thus, a multifaceted approach involving policymakers, developers, healthcare providers and patients is crucial to arrive at a feasible solution for mitigating the legal and ethical problems pertaining to AI in healthcare.

It is widely accepted that explainability is a requirement for the ethical use of artificial intelligence (AI) in health care. I challenge this Explainability Imperative (EI) by considering the following question: does the use of epistemically opaque medical AI systems violate existing legal standards for informed consent? If yes, and if the failure to meet such standards can be attributed to epistemic opacity, then explainability is a requirement for AI in healthcare. If not, then based on at least one metric of ethical medical practice (informed consent), explainability is not required for the ethical use of AI in healthcare. First, I show that the use of epistemically opaque AI applications is compatible with meeting accepted legal criteria for informed consent. Second, I argue that human experts are also black boxes with respect to the criteria by which they arrive at a diagnosis. Human experts can nonetheless meet established requirements for informed consent. I conclude that the use of black-box AI systems does not violate patients’ rights to informed consent, and thus, with respect to informed consent, explainability is not required for medical AI.

ObjectivesApplications of Artificial intelligence (AI) in health care are strikingly advancing and revolutionising the patient care in the recent years. We wanted to evaluate the perception of AI by our...

Artificial intelligence (AI) is increasingly being used in the field of healthcare to improve the efficiency and accuracy of medical diagnoses, treatment plans, and decisionmaking.1 It has the potential to transform the way healthcare is delivered and improve patient outcomes. There are many examples of the use of artificial intelligence (AI) in healthcare. One way that AI is being used in healthcare is by developing machine learning algorithms that can analyze vast amounts of patient data and identify patterns and trends that may not be immediately apparent to humans.2 This can be particularly useful in identifying early signs of diseases or conditions, allowing for earlier diagnosis, treatment, prognosis evaluation, and more. Another application of AI in healthcare is through the use of natural language processing (NLP). NLP systems can analyze electronic medical records (EMR) and extract important information, allowing healthcare providers to access and interpret patient data conveniently.3 This can help healthcare providers access and interpret patient data more easily, leading to more informed decision-making and better patient care. AI can also be used to assist with tasks such as image analysis, allowing for more accurate analysis and efficient diagnosis of medical images such as CT scans or X-rays.4 In addition, AI can be used to help automate routine tasks, freeing up healthcare providers to focus on more complex and higher-level tasks that require human expertise.5 Machine learning algorithms can analyze large amounts of patient data and identify patterns and trends that may not be immediately apparent to humans.6 This can be useful in identifying early signs of diseases or conditions, leading to earlier diagnosis and treatment. AI-powered chatbots or virtual assistants can help with tasks such as appointment scheduling and medication reminders.7 Systems that can assist with the automation of routine tasks, freeing up healthcare providers to focus on more complex and higher-level tasks that require human expertise. Predictive analytics systems can forecast patient outcomes and help healthcare providers make informed decisions about treatment plans.8 Personalized medicine systems can help tailor treatment plans to individual patients based on their specific needs and characteristics. These are just a few examples of the many ways in which AI is being used in healthcare. It is important to note that AI in healthcare is still in its early stages, and the potential applications of the technology are likely to expand significantly in the coming years.

BackgroundThe uptake of artificial intelligence (AI) in health care is at an early stage. Recent studies have shown a lack of AI-specific implementation theories, models, or frameworks that could provide guidance for how to translate the potential of AI into daily health care practices. This protocol provides an outline for the first 5 years of a research program seeking to address this knowledge-practice gap through collaboration and co-design between researchers, health care professionals, patients, and industry stakeholders.ObjectiveThe first part of the program focuses on two specific objectives. The first objective is to develop a theoretically informed framework for AI implementation in health care that can be applied to facilitate such implementation in routine health care practice. The second objective is to carry out empirical AI implementation studies, guided by the framework for AI implementation, and to generate learning for enhanced knowledge and operational insights to guide further refinement of the framework. The second part of the program addresses a third objective, which is to apply the developed framework in clinical practice in order to develop regional capacity to provide the practical resources, competencies, and organizational structure required for AI implementation; however, this objective is beyond the scope of this protocol.MethodsThis research program will use a logic model to structure the development of a methodological framework for planning and evaluating implementation of AI systems in health care and to support capacity building for its use in practice. The logic model is divided into time-separated stages, with a focus on theory-driven and coproduced framework development. The activities are based on both knowledge development, using existing theory and literature reviews, and method development by means of co-design and empirical investigations. The activities will involve researchers, health care professionals, and other stakeholders to create a multi-perspective understanding.ResultsThe project started on July 1, 2021, with the Stage 1 activities, including model overview, literature reviews, stakeholder mapping, and impact cases; we will then proceed with Stage 2 activities. Stage 1 and 2 activities will continue until June 30, 2026.ConclusionsThere is a need to advance theory and empirical evidence on the implementation requirements of AI systems in health care, as well as an opportunity to bring together insights from research on the development, introduction, and evaluation of AI systems and existing knowledge from implementation research literature. Therefore, with this research program, we intend to build an understanding, using both theoretical and empirical approaches, of how the implementation of AI systems should be approached in order to increase the likelihood of successful and widespread application in clinical practice.International Registered Report Identifier (IRRID)PRR1-10.2196/34920

This study thoroughly reviews the status of applications of artificial intelligence (AI) in healthcare, trends regarding AI usage for different disease types and problems that hamper their further progress. The study used a literature review and data analysis by locating relevant current articles on AI in healthcare through the PubMed database. The work analyzes AI use in cancer, cardiovascular diseases and neurological disorders as well as the bottlenecks in the real-world deployment of healthcare. The findings of the study indicate that, while AI has demonstrated the potential to improve diagnostic precision, several obstacles persist in relation to data privacy, ethical considerations, and model interpretability. In conclusion, this review offers an assessment of the current state of AI applications in healthcare and identifies key areas of concern that necessitate further investigation. By addressing these challenges, future innovations can be more effectively developed and broadly implemented, ultimately contributing to the advancement and optimization of AI-driven healthcare solutions

The application of artificial intelligence (AI) in healthcare has increased due to rapid digitization and integration of computer science in all fields. However, the outcome in relation to patient treatment and healthcare delivery is not that visible. The reasons could be non-availability of data, lack of computerization and financial constraints. Besides this, the lack of appropriate teaching at undergraduate level about AI and its medical applications could be an obstacle. Including AI in medical school curriculum and collaboration with faculties of computer science can augment the knowledge of medical students about AI at the graduate level for better application in the real world. This will help the medical profession to prepare their younger fraternity for the future in AI.