Use Of Artificial Intelligence In Medicine Research Articles

Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine

BackgroundThe current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students’ attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula.MethodsThis study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022–2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively.ResultsFrom a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields.ConclusionMedical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.

Ethical Dilemmas of Using Artificial Intelligence in Medicine.

Artificial intelligence (AI) is considered the fourth industrial revolution that will change the evolution of humanity technically and relationally. Although the term has been around since 1956, it has only recently become apparent that AI can revolutionize technologies and has many applications in the medical field. The ethical dilemmas posed by the use of AI in medicine revolve around issues related to informed consent, respect for confidentiality, protection of personal data, and last but not least the accuracy of the information it uses. A literature search was conducted through PubMed, MEDLINE, Plus, Scopus, and Web of Science (2015-2022) using combinations of keywords, including: AI, future in medicine, and machine learning plus ethical dilemma. The ethical analysis of the issues raised by AI used in medicine must mainly address nonmaleficence and beneficence, both in correlation with patient safety risks, ability versus inability to detect correct information from inadequate or even incorrect information. The development of AI tools that can support medical practice can increase people's access to medical information, to obtain a second opinion, for example, but it is also a source of concern among health care professionals and especially bioethicists about how confidentiality is maintained and how to maintain cybersecurity. Another major risk may be related to the dehumanization of the medical act, given that, at least for now, empathy and compassion are accessible only to human beings. AI has not yet managed to overcome certain limits, lacking moral subjectivity, empathy, the level of critical thinking is still insufficient, but no matter who will practice preventive or curative medicine in the next period, they will not be able to ignore AI, which under human control can be an important tool in medical practice.

Medical Expectations of Physicians on AI Solutions in Daily Practice: Cross-Sectional Survey Study.

The use of artificial intelligence (AI) in medicine has been a trending subject in the past few years. Although not frequently used in daily practice yet, it brings along many expectations, doubts, and fears for physicians. Surveys can be used to help understand this situation. This study aimed to explore the degree of knowledge, expectations, and fears on possible AI use by physicians in daily practice, according to sex and time since graduation. An electronic survey was sent to physicians of a large hospital in Brazil, from August to September 2022. A total of 164 physicians responded to our survey. Overall, 54.3% (89/164) of physicians considered themselves to have an intermediate knowledge of AI, and 78.5% (128/163) believed that AI should be regulated by a governmental agency. If AI solutions were reliable, fast, and available, 77.9% (127/163) intended to frequently or always use AI for diagnosis (143/164, 87.2%), management (140/164, 85.4%), or exams interpretation (150/164, 91.5%), but their approvals for AI when used by other health professionals (85/163, 52.1%) or directly by patients (82/162, 50.6%) were not as high. The main benefit would be increasing the speed for diagnosis and management (106/163, 61.3%), and the worst issue would be to over rely on AI and lose medical skills (118/163, 72.4%). Physicians believed that AI would be useful (106/163, 65%), facilitate their work (140/153, 91.5%), not alter the number of appointments (80/162, 49.4%), not interfere in their financial gain (94/162, 58%), and not replace their jobs but be an additional source of information (104/162, 64.2%). In case of disagreement between AI and physicians, most (108/159, 67.9%) answered that a third opinion should be requested. Physicians with ≤10 years since graduation would adopt AI solutions more frequently than those with >20 years since graduation (P=.04), and female physicians were more receptive to other hospital staff using AI than male physicians (P=.008). Physicians were shown to have good expectations regarding the use of AI in medicine when they apply it themselves, but not when used by others. They also intend to use it, as long as it was approved by a regulatory agency. Although there was hope for a beneficial impact of AI on health care, it also brings specific concerns.

Hierarchy of Ethical Principles for the use of Artificial Intelligence in Medicine and Healthcare

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.

Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education.

The use of artificial intelligence (AI) in medicine, potentially leading to substantial advancements such as improved diagnostics, has been of increasing scientific and societal interest in recent years. However, the use of AI raises new ethical challenges, such as an increased risk of bias and potential discrimination against patients, as well as misdiagnoses potentially leading to over- or underdiagnosis with substantial consequences for patients. Recognizing these challenges, current research underscores the importance of integrating AI ethics into medical education. This viewpoint paper aims to introduce a comprehensive set of ethical principles for teaching AI ethics in medical education. This dynamic and principle-based approach is designed to be adaptive and comprehensive, addressing not only the current but also emerging ethical challenges associated with the use of AI in medicine. This study conducts a theoretical analysis of the current academic discourse on AI ethics in medical education, identifying potential gaps and limitations. The inherent interconnectivity and interdisciplinary nature of these anticipated challenges are illustrated through a focused discussion on "informed consent" in the context of AI in medicine and medical education. This paper proposes a principle-based approach to AI ethics education, building on the 4 principles of medical ethics-autonomy, beneficence, nonmaleficence, and justice-and extending them by integrating 3 public health ethics principles-efficiency, common good orientation, and proportionality. The principle-based approach to teaching AI ethics in medical education proposed in this study offers a foundational framework for addressing the anticipated ethical challenges of using AI in medicine, recommended in the current academic discourse. By incorporating the 3 principles of public health ethics, this principle-based approach ensures that medical ethics education remains relevant and responsive to the dynamic landscape of AI integration in medicine. As the advancement of AI technologies in medicine is expected to increase, medical ethics education must adapt and evolve accordingly. The proposed principle-based approach for teaching AI ethics in medical education provides an important foundation to ensure that future medical professionals are not only aware of the ethical dimensions of AI in medicine but also equipped to make informed ethical decisions in their practice. Future research is required to develop problem-based and competency-oriented learning objectives and educational content for the proposed principle-based approach to teaching AI ethics in medical education.

The Knowledge of Students at Bursa Faculty of Medicine towards Artificial Intelligence: A Survey Study

Objectives: Artificial intelligence (AI) is being increasingly widely employed in medicine. Various artificial intelligence software solutions will most certainly be available to medical students when they begin their jobs after completing school. The goal of this study was to assess medical faculty students' knowledge and attitudes about AI in medicine.&#x0D; Methods: In 2020-2021, students at the University of Health Sciences, Bursa Training and Research Hospital participated in an online survey using Google Forms. The survey included sections assessing demographic features and attitudes toward AI in medicine.&#x0D; Results: The questionnaire was completed by 85 students (57 females and 28 males). 50 medical school students and 35 pediatric medical specialty students completed the questionnaire. The mean time spent on the internet per day was 7.5400 ±3.67123 hours in MS and 3.1143 ± 2.0547 hours in MA (p &lt; 0.001). All of the participants had heard AI before (p = 0.500). 62% of MS and 65.7% of MA worried about AI doing many things today; no statistically significant difference was found between the two groups (p = 0.453). 56.5% of participants felt “curiosity”, 16.4% felt “excited”, 11.8% felt “look positive”, 15.3% felt “frightened” about the increased use of artificial intelligence in our lives. 24% of the MS and 37.1% of the MA knew AI applications used in medicine; there was no statistically significant difference between the two groups (p = 0.191). The most commonly known applications of AI in medicine were in the fields of robotic surgery (10.5%) and radiology (3.6%). While 8% of the MS participated in the project / study related to AI; none of the MA participated, there was a statistically significant difference between the two groups (p = 0.036). On the contrary, the use of AI in medicine was 5.7% in MA; it was never possible in MS, there was no statistically significant difference between the two groups (p = 0.057). 91.8% of the participants would like to use AI in their future careers as physicians. 68% of MS and 94.3% of MA thought AI could not replace doctors in the future; there was a statistically significant difference between the two groups (p = 0.004).&#x0D; Conclusion: Medical students, who will be the physicians of the future, regarded AI applications positively, and they wanted to gain education and experience in this subject with deep curiosity. Since the knowledge level of future physicians in this field is not sufficient, it is important to provide more AI training in medicine, to participate in more projects in this field, and to increase the use of AI by medical students in the field of medicine.

Artificial Intelligence in Medicine: Cross-Sectional Study Among Medical Students on Application, Education, and Ethical Aspects.

The use of artificial intelligence (AI) in medicine not only directly impacts the medical profession but is also increasingly associated with various potential ethical aspects. In addition, the expanding use of AI and AI-based applications such as ChatGPT demands a corresponding shift in medical education to adequately prepare future practitioners for the effective use of these tools and address the associated ethical challenges they present. This study aims to explore how medical students from Germany, Austria, and Switzerland perceive the use of AI in medicine and the teaching of AI and AI ethics in medical education in accordance with their use of AI-based chat applications, such as ChatGPT. This cross-sectional study, conducted from June 15 to July 15, 2023, surveyed medical students across Germany, Austria, and Switzerland using a web-based survey. This study aimed to assess students' perceptions of AI in medicine and the integration of AI and AI ethics into medical education. The survey, which included 53 items across 6 sections, was developed and pretested. Data analysis used descriptive statistics (median, mode, IQR, total number, and percentages) and either the chi-square or Mann-Whitney U tests, as appropriate. Surveying 487 medical students across Germany, Austria, and Switzerland revealed limited formal education on AI or AI ethics within medical curricula, although 38.8% (189/487) had prior experience with AI-based chat applications, such as ChatGPT. Despite varied prior exposures, 71.7% (349/487) anticipated a positive impact of AI on medicine. There was widespread consensus (385/487, 74.9%) on the need for AI and AI ethics instruction in medical education, although the current offerings were deemed inadequate. Regarding the AI ethics education content, all proposed topics were rated as highly relevant. This study revealed a pronounced discrepancy between the use of AI-based (chat) applications, such as ChatGPT, among medical students in Germany, Austria, and Switzerland and the teaching of AI in medical education. To adequately prepare future medical professionals, there is an urgent need to integrate the teaching of AI and AI ethics into the medical curricula.

Artificial intelligence in medicine: Ethical, social and legal perspectives

Artificial intelligence (AI) has permeated into every aspect of medicine and promises to provide accurate diagnosis, better management decision and improved outcome for patients and healthcare system. However, ethical, social and legal issues need to be resolved for successful implementation of AI tools in clinical practice. In order to gain trust and acceptance, AI algorithms should offer maximum explainability and inclusiveness. Robust evidence of benefit to patients and healthcare services has to be provided to gain justification of using these tools. Doctor–patient relationship needs to be maintained in order to gain trust and acceptance of users. Autonomy of decisions and dignity of patients need to be preserved while using machine in healthcare. Responsibility and accountability in the use of AI in medicine should be deliberated and defined before mishaps and damage occur. A new role of healthcare providers will emerge with the advancement of technology and changes are inevitable. This manuscript is based on the Gordon Arthur Ransome Lecture 2022 entitled “Artificial Intelligence in Medicine: Ethical, Social and Legal Perspective”. It represents the opinion of the orator.

Artificial Intelligence in Medicine: Progress, Challenges, and Promises

This article discusses the potential of artificial intelligence (AI) in medicine to address complex challenges, from personalized treatment to difficult diagnoses. The use of AI in medicine has exhibited remarkable performance in various fields, including evidence-based clinical decision support, AI-assisted surgery, and electronic health records analysis. However, challenges remain, such as data silos and potential biases in AI training data. Despite these challenges, AI technology has the potential to transform healthcare in significant ways, and ongoing research and development, collaborations among stakeholders, and innovative initiatives can help ensure its safe and ethical use in medicine and healthcare.

The Potential of ChatGPT as a Self-Diagnostic Tool in Common Orthopedic Diseases: Exploratory Study.

Artificial intelligence (AI) has gained tremendous popularity recently, especially the use of natural language processing (NLP). ChatGPT is a state-of-the-art chatbot capable of creating natural conversations using NLP. The use of AI in medicine can have a tremendous impact on health care delivery. Although some studies have evaluated ChatGPT's accuracy in self-diagnosis, there is no research regarding its precision and the degree to which it recommends medical consultations. The aim of this study was to evaluate ChatGPT's ability to accurately and precisely self-diagnose common orthopedic diseases, as well as the degree of recommendation it provides for medical consultations. Over a 5-day course, each of the study authors submitted the same questions to ChatGPT. The conditions evaluated were carpal tunnel syndrome (CTS), cervical myelopathy (CM), lumbar spinal stenosis (LSS), knee osteoarthritis (KOA), and hip osteoarthritis (HOA). Answers were categorized as either correct, partially correct, incorrect, or a differential diagnosis. The percentage of correct answers and reproducibility were calculated. The reproducibility between days and raters were calculated using the Fleiss κ coefficient. Answers that recommended that the patient seek medical attention were recategorized according to the strength of the recommendation as defined by the study. The ratios of correct answers were 25/25, 1/25, 24/25, 16/25, and 17/25 for CTS, CM, LSS, KOA, and HOA, respectively. The ratios of incorrect answers were 23/25 for CM and 0/25 for all other conditions. The reproducibility between days was 1.0, 0.15, 0.7, 0.6, and 0.6 for CTS, CM, LSS, KOA, and HOA, respectively. The reproducibility between raters was 1.0, 0.1, 0.64, -0.12, and 0.04 for CTS, CM, LSS, KOA, and HOA, respectively. Among the answers recommending medical attention, the phrases "essential," "recommended," "best," and "important" were used. Specifically, "essential" occurred in 4 out of 125, "recommended" in 12 out of 125, "best" in 6 out of 125, and "important" in 94 out of 125 answers. Additionally, 7 out of the 125 answers did not include a recommendation to seek medical attention. The accuracy and reproducibility of ChatGPT to self-diagnose five common orthopedic conditions were inconsistent. The accuracy could potentially be improved by adding symptoms that could easily identify a specific location. Only a few answers were accompanied by a strong recommendation to seek medical attention according to our study standards. Although ChatGPT could serve as a potential first step in accessing care, we found variability in accurate self-diagnosis. Given the risk of harm with self-diagnosis without medical follow-up, it would be prudent for an NLP to include clear language alerting patients to seek expert medical opinions. We hope to shed further light on the use of AI in a future clinical study.

Evaluation of acceptance, attitude, and knowledge towards artificial intelligence and its application from the point of view of physicians and nurses: A provincial survey study in Iran: A cross-sectional descriptive-analytical study.

The prospect of using artificial intelligence (AI) in healthcare is bright and promising, and its use can have a significant impact on cost reduction and decrease the possibility of error and negligence among healthcare workers. This study aims to investigate the level of knowledge, attitude, and acceptance among Iranian physicians and nurses. This cross-sectional descriptive-analytical study was conducted in eight public university hospitals located in Tehran on 400 physicians and nurses. To conduct the study, convenient sampling was used with the help of researcher-made questionnaires. Statistical analysis was done by SPSS 21 The mean and standard deviation and Chi-square and Fisher's exact tests were used. In this study, the level of knowledge among the research subjects was average (14.66 ± 4.53), the level of their attitude toward AI was relatively favorable (47.81 ± 6.74), and their level of acceptance of AI was average (103.19 ± 13.70). Moreover, from the participant's perspective, AI in medicine is most widely used in increasing the accuracy of diagnostic tests (86.5%), identifying drug interactions (82.75%), and helping to analyze medical tests and imaging (80%). There was a statistically significant relationship between the variable of acceptance of AI and the participant's level of education (p = 0.028), participation in an AI training course (p = 0.022), and the hospital department where they worked (p < 0.001). In this study, both the knowledge and the acceptance of the participants towards AI were proved to be at an average level and the attitude towards AI was relatively favorable, which is in contrast with the very rapid and inevitable expansion of AI. Although our participants were aware of the growing use of AI in medicine, they had a cautious attitude toward this.

Proč bychom měli a neměli využívat ChatGPT v gastroenterologii?

Summary: Artificial intelligence (AI) is increasingly being incorporated into medicine, including gastroenterology, opening new possibilities for the diagnosis and treatment of digestive tract diseases. ChatGPT, an AI model based on the GPT-4 architecture, has the potential to accelerate diagnosis and treatment, personalize care, educate, and train healthcare professionals, support decision-making, and improve communication with patients. However, with the use of AI come challenges such as the limited ability of AI to replace human judgment, data errors, issues related to security and personal data protection, and implementation costs. The future of ChatGPT in gastroenterology depends on its ability to process and analyze large amounts of data to identify patterns and create individual treatment plans. Thanks to advancements in AI and machine learning, ChatGPT is becoming more accurate and efficient, enabling faster diagnosis and treatment of gastroenterological diseases. In the field of education, ChatGPT will serve as an invaluable source of information on the latest research articles and procedures. Despite the benefits of AI in gastroenterology, it is essential to address issues of ethics, data protection, and collaboration between AI and healthcare professionals. Ensuring proper protocols and procedures will enable the safe and ethical use of AI in medicine. Although AI offers significant potential for improving the quality of care, it is necessary to address challenges associated with data protection, security, and ethics. Key words: artificial intelligence – AI – ChatGPT – gastroenterology

What Is Machine Learning, Artificial Neural Networks and Deep Learning?-Examples of Practical Applications in Medicine.

Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future.

Comparison of awareness and attitudes toward artificial intelligence among Russian- and English-speaking students at Orenburg State Medical University

BACKGROUND: Artificial Intelligence (AI) is actively implemented in medicine. Since medical students are future physicians, an assessment of their awareness and attitudes toward AI is important.&#x0D; AIM: To compare the degree of awareness and attitudes toward AI among Russian-speaking students of the Orenburg State Medical University (OrSMU) from the Russian Federation and English-speaking students from the Republic of India.&#x0D; METHODS: From March 12 to 25, 2023, a voluntary anonymous survey (28 questions) was offered to OrSMU students using the Google Forms platform. An English-language version was prepared for foreign students. All responses were analyzed statistically (calculation of mean values using Likert scale, Students t-test, and Pearsons chi-square test).&#x0D; RESULTS: A total of 331 students participated in the survey, including 214 Russian-speaking and 117 English-speaking participants (127 males, 202 females, and two did not indicate gender). All participants were divided into 2 subgroups: junior (13 years, 200 participants) and senior year (46 years, 131 participants) students. The vast majority of respondents (92.3%) knew what AI is, with a higher percentage (p0.001) among Russian-speaking students (95.8%) versus English-speaking students (84.6%). Only 34.1% of Russian-speaking and 46.2% of English-speaking students (p=0.032) were aware of the possibility of using AI in medicine. A total of 28.5% and 23.4% of Russian-speaking and 44.4% and 38.5% of English-speaking respondents, respectively, were aware of the use of AI in diagnostic radiology and pathological anatomy (p=0.004). Students expressed the greatest agreement with the statement that AI will play a significant role in the development and support of medicine in the future (mean Likert scale value of 4.23). Students were the least likely to agree with the statement that AIs diagnostic abilities are superior to the human physicians clinical experience (mean of 2.84). In the case of a disagreement between the AI and the physician, 76.7% of respondents would trust the latter to make the final decision. Most respondents considered diagnostic radiology, electrocardiogram analysis, and pathological anatomy to be promising areas for the use of AI (91.3%, 71.3%, and 70.4%, respectively). For the rest of the statements about attitudes toward AI, the average values ranged from 3.63 to 4.33. Among the disadvantages of using AI, the threat of data leakage was reported. The advantages were quick data analysis and assistance in diagnosis.&#x0D; CONCLUSIONS: English-speaking students were more aware of the use of AI in medicine, whereas students from Russia showed a more positive attitude toward AI. However, in the case of a disagreement between the physician and the AI, both groups of respondents would trust the physician to make the decision.

The concept of responsible artificial intelligence as the future of artificial intelligence in medicine

Active deployment of artificial intelligence (AI) systems in medicine creates many challenges. Recently, the concept of responsible artificial intelligence (RAI) was widely discussed, which is aimed at solving the inevitable ethical, legal, and social problems. The scientific literature was analyzed and the possibility of applying the RAI concept to overcome the existing AI problems in medicine was considered. Studies of possible AI applications in medicine showed that current algorithms are unable to meet the basic enduring needs of society, particularly, fairness, transparency, and reliability. The RAI concept based on three principles accountability for AI activities and responsibility and transparency of findings (ART) was proposed to address ethical issues. Further evolution, without the development and application of the ART concept, turns dangerous and impossible the use of AI in such areas as medicine and public administration. The requirements for accountability and transparency of conclusions are based on the identified epistemological (erroneous, non-transparent, and incomplete conclusions) and regulatory (data confidentiality and discrimination of certain groups) problems of using AI in digital medicine [2]. Epistemological errors committed by AI are not limited to omissions related to the volume and representativeness of the original databases analyzed. In addition, these include the well-known black box problem, i.e. the inability to look into the process of forming AI outputs when processing input data. Along with epistemological errors, normative problems inevitably arise, including patient confidentiality and discrimination of some social groups due to the refusal of some patients to provide medical data for training algorithms and as part of the analyzed databases, which will lead to inaccurate AI conclusions in cases of certain gender, race, and age. Importantly, the methodology of the AI data analysis depends on the program code set by the programmer, whose epistemological and logical errors are projected onto the AI. Hence the problem of determining responsibility in the case of erroneous conclusions, i.e. its distribution between the program itself, the developer, and the executor. Numerous professional associations design ethical standards for developers and a statutory framework to regulate responsibility between the links described. However, the state must play the greatest role in the development and approval of such legislation. The use of AI in medicine, despite its advantages, is accompanied by many ethical, legal, and social challenges. The development of RAI has the potential both to solve these challenges and to further the active and secure deployment of AI systems in digital medicine and healthcare.

On the Introduction of Artificial Intelligence into the Daily Practice of Breast Pathology Diagnosis: has Everything been Taken into Account?

Artificial Intelligence (AI) has the potential to improve the diagnosis and effectiveness of breast cancer treatment. However, the use of AI in medicine in general, and in mammology in particular, requires regulation by legislative and legal acts to ensure the protection of patients’ rights, safety and quality of medical care. Special attention should be paid to the training of medical personnel and the development of new technologies and methods of working with AI. There are laws and regulations governing the use of AI in mammology around the world. This article reviews the laws governing the use of AI in breast radiology in Western Europe, Asia, and the United States.

Physicians’ attitudes and knowledge toward artificial intelligence in medicine: Benefits and drawbacks

The use of artificial intelligence (AI) in the medical field is increasing and is expected to shape future clinical practice and job security. Therefore, this study aimed to assess the opinions and attitudes of practicing physicians in Bahrain regarding the benefits and drawbacks of AI for their future daily practice. A cross-sectional survey of practicing physicians with a minimum of five years' experience across the main secondary and tertiary care hospitals in Bahrain was conducted. An online questionnaire was used to collect data on demographics, knowledge of AI, attitudes towards the use of AI in 10 tasks of daily clinical practice, and opinions on the benefits and drawbacks of AI. A total of 114 physicians participated in the survey. Among them, 43 (37.7%) were registered psychiatrists, 15 (13.2%) were pathologists, 17 (14.9%) were radiologists, and 39 (34.2%) were surgical specialists. The participants' attitudes were overall positive towards AI. Pathologists were particularly in favor of using AI to “Formulate personalized medication and/or treatment plans for patients” and to “Interview patients in a range of settings to obtain medical history.” Most participants agreed that AI would reduce the time needed to establish a diagnosis and negatively affect employment rates. There were no correlations between the responses and the participants’ age, gender, years of experience, or AI knowledge. This study demonstrates that the attitudes towards the use of AI in medicine among practicing physicians in Bahrain are similar to those of physicians in developed countries in that they are positive and welcoming of AI implementation in practice. However, the potential effects of AI on job security are a major concern.

Artificial Intelligence in Medicine. Ethical reflections from the thought of Edmund Pellegrino

The current scenario of health sciences, and of Medicine in particular, is characterized by the growing role of Artificial Intelligence (AI), thus projecting towards the consolidation of a new model of Medicine. Along with the undeniable advantages offered by the application of AI for the diagnosis and treatment of complex clinical problems, some ethical questions arise, which demand careful reflection. However, most of the literature that addresses the ethical issues associated with the use of AI in medicine does so from the poiesis' perspective. Indeed, a large part of that evidence is related to the design, programming, training and operation of algorithms, problems that exceed the competences of the health professionals that use them. Our proposal is framed in the ethics of virtue outlined by Edmund Pellegrino, which is a valuable epistemological perspective to trace a path of reflection on the ethical problems arising from the use of AI in Medicine. This perspective -based on a sound philosophy of Medicine- adopts the praxis' point of view, that is, of the acting subject. Since the health professional is a moral agent who uses AI as a tool to achieve an end -the good of the patient-,from Pellegrino's perspective it is possible to ask how the use of AI might influence the achievement of the goal of medical work and thus, be considered a criterion of ethical orientation.

Attitudes Towards Artificial Intelligence Among Dermatologists Working in Saudi Arabia.

Artificial intelligence (AI) and its applications are among the most discussed modern technologies today. Despite the rapidly expanding use of AI in medicine, and specifically in dermatology, only a few studies have studied the attitude of physicians toward AI. To recognize the attitudes towards AI among dermatologists in the Kingdom of Saudi Arabia. A cross-sectional survey was done among dermatologists in Saudi Arabia. Questionnaires were distributed through several online channels. Overall, 103 dermatologists filled out the survey. The majority saw very strong or strong potential for AI in the automated detection of skin diseases based on dermatological clinical images (50.9%), dermoscopic images (66.6%) and within dermatopathology (66.6%). In regard to results of attitudes towards AI, 56.6% and 52. 8% agreed that AI will revolutionize medicine and dermatology, respectively. However, many of the respondents disagreed that AI will replace physicians (41.5%) and human dermatologists (39.6%) in the future. Age did not impact the overall attitude of dermatologists. Dermatologists in Saudi Arabia showed an optimistic attitude towards AI in dermatology and medicine. However, dermatologists believe that AI will not replace humans in the future.

Building Trust in Medical Use of Artificial Intelligence – The Swarm Learning Principle

ABSTRACTAn avalanche of medical data is starting to be build up. With the digitalisation of medicine and novel approaches such as the omics technologies, we are conquering ever bigger data spaces to be used to describe pathophysiology of diseases, define biomarkers for diagnostic purposes or identify novel drug targets. Utilising this growing lake of medical data will only be possible, if we make use of machine learning, in particular artificial intelligence (AI)-based algorithms. While the technological developments and chances of the data and information sciences are enormous, the use of AI in medicine also bears challenges and many of the current information technologies (IT) do not follow established medical traditions of mentoring, learning together, sharing insights, while preserving patient’s data privacy by patient physician privilege. Other challenges to the medical sector are demands from the scientific community such as “Open Science”, “Open Data”, “Open Access” principles. A major question to be solved is how to guide technological developments in the IT sector to serve well-established medical traditions and processes, yet allow medicine to benefit from the many advantages of state-of-the-art IT. Here, I provide the Swarm Learning (SL) principle as a conceptual framework designed to foster medical standards, processes and traditions. A major difference to current IT solutions is the inherent property of SL to appreciate and acknowledge existing regulations in medicine that have been proven beneficial for patients and medical personal alike for centuries.