ხელოვნური ინტელექტის როლი სამედიცინო მომსახურების განვითარებაში - ჯანდაცვის სისტემის კონტექსტი

The aim of the work is to provide an overview of the potential application of artificial intelligence in forensic medicine and related sciences, and to identify concerns related to providing medico-legal opinions and legal liability in cases in which possible harm in terms of diagnosis and/or treatment is likely to occur when using an advanced system of computer-based information processing and analysis. The material for the study comprised scientific literature related to the issue of artificial intelligence in forensic medicine and related sciences. For this purpose, Google Scholar, PubMed and ScienceDirect databases were searched. To identify useful articles, such terms as "artificial intelligence," "deep learning," "machine learning," "forensic medicine," "legal medicine," "forensic pathology" and "medicine" were used. In some cases, articles were identified based on the semantic proximity of the introduced terms. Dynamic development of the computing power and the ability of artificial intelligence to analyze vast data volumes made it possible to transfer artificial intelligence methods to forensic medicine and related sciences. Artificial intelligence has numerous applications in forensic medicine and related sciences and can be helpful in thanatology, forensic traumatology, post-mortem identification examinations, as well as post-mortem microscopic and toxicological diagnostics. Analyzing the legal and medico-legal aspects, artificial intelligence in medicine should be treated as an auxiliary tool, whereas the final diagnostic and therapeutic decisions and the extent to which they are implemented should be the responsibility of humans.

The coming of age of artificial intelligence in medicine.

The integration of artificial intelligence into clinical medicine: Trends, challenges, and future directions.

Given the rapidity with which artificial intelligence is gaining momentum in clinical medicine, current physician leaders have called for more incorporation of artificial intelligence topics into undergraduate medical education. This is to prepare future physicians to better work together with artificial intelligence technology. However, the first step in curriculum development is to survey the needs of end users. There has not been a study to determine which media and which topics are most preferred by US medical students to learn about the topic of artificial intelligence in medicine. We aimed to survey US medical students on the need to incorporate artificial intelligence in undergraduate medical education and their preferred means to do so to assist with future education initiatives. A mixed methods survey comprising both specific questions and a write-in response section was sent through Qualtrics to US medical students in May 2021. Likert scale questions were used to first assess various perceptions of artificial intelligence in medicine. Specific questions were posed regarding learning format and topics in artificial intelligence. We surveyed 390 US medical students with an average age of 26 (SD 3) years from 17 different medical programs (the estimated response rate was 3.5%). A majority (355/388, 91.5%) of respondents agreed that training in artificial intelligence concepts during medical school would be useful for their future. While 79.4% (308/388) were excited to use artificial intelligence technologies, 91.2% (353/387) either reported that their medical schools did not offer resources or were unsure if they did so. Short lectures (264/378, 69.8%), formal electives (180/378, 47.6%), and Q and A panels (167/378, 44.2%) were identified as preferred formats, while fundamental concepts of artificial intelligence (247/379, 65.2%), when to use artificial intelligence in medicine (227/379, 59.9%), and pros and cons of using artificial intelligence (224/379, 59.1%) were the most preferred topics for enhancing their training. The results of this study indicate that current US medical students recognize the importance of artificial intelligence in medicine and acknowledge that current formal education and resources to study artificial intelligence-related topics are limited in most US medical schools. Respondents also indicated that a hybrid formal/flexible format would be most appropriate for incorporating artificial intelligence as a topic in US medical schools. Based on these data, we conclude that there is a definitive knowledge gap in artificial intelligence education within current medical education in the US. Further, the results suggest there is a disparity in opinions on the specific format and topics to be introduced.

BackgroundThis review aims to comprehensively explore the integration of artificial intelligence (AI) in the prevention, diagnosis, and treatment of metabolic syndrome (MetS). MetS is characterized by a cluster of conditions, posing a growing public health threat globally. Recognizing the limitations of traditional management approaches, we emphasize the potential of AI in transforming the management of MetS, focusing on recent advancements and applications in risk prediction and diagnosis.Body and conclusion.The integration of artificial intelligence in medicine is expanding, particularly in managing MetS, involving conditions like hypertension and dyslipidemia. Diagnosis and treatment challenges stem from addressing multiple conditions simultaneously. AI tools prove essential in monitoring indices such as blood pressure and glucose, and identifying trends for treatment adjustments. Lifestyle modifications are crucial, and AI can facilitate these changes through user-friendly interfaces and positive reinforcement. Standardization and successful implementation of AI tools in medical practices are necessary for revolutionizing MetS management, requiring focused future research efforts.

In the 21st century, humanity has made a significant leap in the development of information and computational technologies, leading to the active advancement of artificial intelligence (AI). Initially, AI had primarily an entertainment character, but now technologies based on AI are actively being integrated into various professional fields. The Russian Federation is also increasing its research volume, including the application of these technologies in medical activities. This article examines some of the issues related to the active implementation and use of AI and AI-based products in medical practice in Russia. Literature data on existing legislation and legal practice are presented, along with discussions on ethical and deontological issues of AI use in medicine. Problems of information security and material-technical support are also highlighted. Based on the analysis conducted, we have formulated conclusions pointing to the controversial aspects of AI-based technologies' application in current medical activities. It is important to note that the authors do not oppose the integration of AI-based technologies into medical activities but have merely analyzed the problematic issues. The formulated conclusions and identified problems can help form a unified trajectory for the development and integration of AI into practical healthcare in Russia.

In the era of advancements in artificial intelligence (AI) and machine learning, the healthcare industry has become one of the major areas where such technologies are being actively adopted and utilized. The global health care sector generated more than 2.3 zettabytes of data worldwide in 2020. Analysts estimate that the global market for artificial intelligence (AI) in medicine will grow to $13 billion by 2025, with a significant increase in newly established companies. Artificial intelligence in medicine is used to predict, detect and diagnose various diseases and pathologies. The sources of data can be various results of medical research (EEG, X-ray images, laboratory tests, e.g. tissues, etc.). At the same time, there are understandable concerns that AI will undermine the patient-provider relationship, contribute to the deskilling of providers, undermine transparency, misdiagnose or inappropriately treat because of errors within AI decision-making that are hard to detect, exacerbate existing racial or societal biases, or introduce algorithmic bias that will be hard to detect. Traditional research methods, general and special ones, with an emphasis on the comparative legal method, were chosen. For the AI to work it needs to be trained, and it’s learning from all sorts of information given to it. The main part of the information on which AI is trained is health data, which is sensitive personal data. The fact that personal data is qualified as sensitive personal data indicates the significance of the information contained, the high risks in case it’s leaking, and hence the need for stricter control and regulation. The article offers a detailed exploration of the legal implications of AI in medicine, highlighting existing challenges, the current state of regulation, and proposes future perspectives and recommendations for legislation adapted to the era of medical AI. Given the above, the study is divided into three parts: international framework, that will focus primarily on applicable WHO documents; risks and possible ways to minimize them, where the authors have tried to consider various issues related to the use of AI in medicine and find options to address them; and relevant case-study.

The rapid adoption and implementation of artificial intelligence in medicine creates an ontologically distinct situation from prior care models. There are both potential advantages and disadvantages with such technology in advancing the interests of patients, with resultant ontological and epistemic concerns for physicians and patients relating to the instatiation of AI as a dependent, semi- or fully-autonomous agent in the encounter. The concept of libertarian paternalism potentially exercised by AI (and those who control it) has created challenges to conventional assessments of patient and physician autonomy. The unclear legal relationship between AI and its users cannot be settled presently, an progress in AI and its implementation in patient care will necessitate an iterative discourse to preserve humanitarian concerns in future models of care. This paper proposes that physicians should neither uncritically accept nor unreasonably resist developments in AI but must actively engage and contribute to the discourse, since AI will affect their roles and the nature of their work. One’s moral imaginative capacity must be engaged in the questions of beneficence, autonomy, and justice of AI and whether its integration in healthcare has the potential to augment or interfere with the ends of medical practice.

Chapter 24 - Industry perspectives and commercial opportunities of artificial intelligence in medicine

The article researches the problem of ethical support of the application of artificial intelligence (AI) in medicine and healthcare, which is topical for modern sci­ence. Despite a significant number of foreign and domestic publications devoted to the topic of AI, the conceptual justification of the ethics of AI application in medicine and healthcare remains poorly developed. Relying on international recommendations and articles, as well as on their own experience of research activities, work in research ethics committees, the results of a pilot survey of health care workers, etc., the authors define and analyze the basic ethical principles of using AI in medicine and health care. The proposed principles are considered in the context of their practical application to protect human and natural rights and interests, which includes preservation of patient confidentiality, prevention of discrimination, protection from AI errors, respect for in­formed consent, as well as compliance with the norms of “open science”, mutual trust of developers and users, etc. The proposed principles are analyzed in the context of their practical application. The application of the proposed principles will orient scientists, AI developers, ethical committees conducting expert review of research, society as a whole to the priorities of humanization of healthcare, respect for human beings and nature, as well as to educate society, create a regulatory framework, ethical recommen­dations and codes of ethics for the use of AI in medicine and healthcare.

The integration of artificial intelligence (AI) into medicine has ushered an era of unprecedented innovation, with substantial impacts on healthcare delivery and patient outcomes. Understanding the current development, primary research focuses, and key contributors in AI applications in medicine through bibliometric analysis is essential. For this research, we utilized the Web of Science Core Collection as our main database and performed a review of literature covering the period from January 2019 to December 2023. VOSviewer and R-bibliometrix were performed to conduct bibliometric analysis and network visualization, including the number of publications, countries, journals, citations, authors, and keywords. A total of 1,811 publications on research for AI in medicine were released across 565 journals by 12,376 authors affiliated with 3,583 institutions from 97 countries. The United States became the foremost producer of scholarly works, significantly impacting the field. Harvard Medical School exhibited the highest publication count among all institutions. The Journal of Medical Internet Research achieved the highest H-index (19), publication count (76), and total citations (1,495). Four keyword clusters were identified, covering AI applications in digital health, COVID-19 and ChatGPT, precision medicine, and public health epidemiology. "Outcomes" and "Risk" demonstrated a notable upward trend, indicating the utilization of AI in engaging with clinicians and patients to discuss patients' health condition risks, foreshadowing future research focal points. Analyzing our bibliometric data allowed us to identify progress, focus areas, and emerging fields in AI for medicine, pointing to potential future research directions. Since 2019, there has been a steady rise in publications related to AI in medicine, indicating its rapid growth. In addition, we reviewed journals and significant publications to pinpoint prominent countries, institutions, and academics. Researchers will gain important insights into the current landscape, collaborative frameworks, and key research topics in the field from this study. The findings suggest directions for future research.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

Artificial Intelligence (AI) has been utilised in many settings, including in the film industry where it is already changing several aspects of how storylines are conceived (1). Similarly, medical advancements are now seeing a major shift in how AI might influence patient treatments, sometimes controversially so. We explore the transformative role of AI in the field of medicine, highlighting its potential to improve diagnostic accuracy, treatment efficacy, and patient outcomes. We highlight various applications of AI in medical imaging, diagnosis, drug discovery, personalised medicine, decision support systems, prevention, and patient engagement. Furthermore, we discuss the ethical and legal considerations and challenges associated with AI integration in healthcare. Drawing on relevant research and case studies, this paper provides insights into the future implications of AI in medicine.

Chapter 11 - The Future of Artificial Intelligence in Medicine

The rate of development in artificial intelligence (AI) technologies has brought revolutionary transformationsacross the healthcare sector. The advancements especially impacted the manner in which the diagnosis,treatment, and management of diseases are undertaken with a growing emphasis laid on optimizing clinicalworkflow and patient care. Machine learning (ML) and deep learning (DL) technologies have proved invaluablefor the analysis of complex datasets, supporting early diagnosis and optimizing treatment protocols. AIshowcased tremendous promise in a plethora of applications, such as the accurate analysis of medical imagery,the personalization of therapies through the tailor-made dosing of medicines, predictive analysis in forecastingthe course of the disease, and the assistance of patients and clinicians by deploying virtual health tools.Moreover, the increasing application of AI in healthcare has also boosted access to quality healthcare,particularly in underserved populations. The following review also explores the ethical, legislative, andregulatory issues related to the application of AI in medicine, reiterating the need for protection of the data,transparency, and equitable access. With careful implementation that respects these boundaries, AI holds thepotential to significantly enhance global healthcare delivery. Despite these benefits, integrating AI intohealthcare systems raises important ethical, legal, and regulatory concerns. Ensuring data privacy, promotingalgorithm transparency, and maintaining fairness in AI-driven healthcare solutions are key concerns.Addressing these challenges is vital to building trust and ensuring that AI technologies benefit all populationsfairly. With responsible and ethical implementation, AI holds immense potential to transform global healthcaredelivery and improve patient outcomes on a large scale. How to cite this: Jan TA, Naz S. Artificial Intelligence in Medicine: Transforming The Future of Healthcare. Life and Science. 2025; 6(2): 284-291. doi: http://doi.org/10.37185/LnS.1.1.904