Use Of Artificial Intelligence In Medicine Research Articles

Potential Applications and Ethical Considerations for Artificial Intelligence in Traumatic Brain Injury Management

Artificial intelligence (AI) systems have emerged as promising tools for rapidly identifying patterns in large amounts of healthcare data to help guide clinical decision making, as well as to assist with medical education and the planning of research studies. Accumulating evidence suggests AI techniques may be particularly useful for aiding the diagnosis and clinical management of traumatic brain injury (TBI)—a considerably heterogeneous neurologic condition that can be challenging to detect and treat. However, important methodological and ethical concerns with the use of AI in medicine necessitate close monitoring and regulation of these techniques as advancements continue. The purpose of this narrative review is to provide an overview of common AI techniques in medical research and describe recent studies on the possible clinical applications of AI in the context of TBI. Finally, the review describes the ethical challenges with the use of AI in medicine, as well as guidelines from the White House, the Department of Defense (DOD), the National Academies of Sciences, Engineering, and Medicine (NASEM), and other organizations on the appropriate uses of AI in research.

Accuracy and Readability of Artificial Intelligence Chatbot Responses to Vasectomy-Related Questions: Public Beware.

Purpose Artificial intelligence (AI) has rapidly gained popularity with the growth of ChatGPT (OpenAI, San Francisco, USA) and other large-language model chatbots, and these programs have tremendous potential to impact medicine. One important area of consequence in medicine and public health is that patients may use these programs in search of answers to medical questions. Despite the increased utilization of AI chatbots by the public, there is little research to assess the reliability of ChatGPT and alternative programs when queried for medical information. This study seeks to elucidate the accuracy and readability of AI chatbots in answering patient questions regarding urology. As vasectomy is one of the most common urologic procedures, this study investigates AI-generated responses to frequently asked vasectomy-related questions. For this study, five popular and free-to-access AI platforms were utilized to undertake this investigation. Methods Fifteen vasectomy-related questions were individually queried to five AI chatbots from November-December 2023: ChatGPT (OpenAI, San Francisco, USA), Bard (Google Inc., Mountainview, USA) Bing (Microsoft, Redmond, USA) Perplexity (Perplexity AI Inc., San Francisco, USA), and Claude (Anthropic, San Francisco, USA). Responses from each platform were graded by two attending urologists, two urology research faculty, and one urological resident physician using a Likert (1-6) scale: (1-completely inaccurate, 6-completely accurate) based on comparison to existing American Urological Association guidelines. Flesch-Kincaid Grade levels (FKGL) and Flesch Reading Ease scores (FRES) (1-100) were calculated for each response. To assess differences in Likert, FRES, and FKGL, Kruskal-Wallis tests were performed using GraphPad Prism V10.1.0 (GraphPad,San Diego,USA) with Alpha set at 0.05. Results Analysis shows that ChatGPT provided the most accurate responses across the five AI chatbots with an average score of 5.04 on the Likert scale. Subsequently, Microsoft Bing (4.91), Anthropic Claude (4.65), Google Bard (4.43), and Perplexity (4.41) followed. All five chatbots were found to score, on average, higher than 4.41 corresponding to a score of at least "somewhat accurate." Google Bard received the highest Flesch Reading Ease score (49.67) and lowest Grade level (10.1) when compared to the other chatbots. Anthropic Claude scored 46.7 on the FRES and 10.55 on the FKGL. Microsoft Bing scored 45.57 on the FRES and 11.56 on the FKGL. Perplexity scored 36.4 on the FRES and 13.29 on the FKGL. ChatGPT had the lowest FRES of 30.4 and highest FKGL of 14.2. Conclusion This study investigates the use of AI in medicine, specifically urology, and it helps to determine whether large-language model chatbots can be reliable sources of freely available medical information. All five AI chatbots on average were able to achieve at least "somewhat accurate" on a 6-point Likert scale. In terms of readability, all five AI chatbots on average had Flesch Reading Ease scores of less than 50 and were higher than a 10th-grade level. In this small-scale study, there were several significant differences identified between the readability scores of each AI chatbot. However, there were no significant differences found among their accuracies. Thus, our study suggests that major AI chatbots may perform similarly in their ability to be correct but differ in their ease of being comprehended by the general public.

Physicians' Attitudes Towards Artificial Intelligence: Results of the PEAK Project.

Artificial intelligence (AI) is rapidly reshaping the medical field. This study aimed to investigate the attitudes of physicians towards AI in medical care using focus groups. Most participants seemed to be open to the use of AI in medicine if transparency in AI systems is ensured, regulatory barriers are addressed and personal contact to the patient is maintained. This qualitative study allows insights into how German physicians perceive the use of AI in medical care. Gaining input from physicians is important when designing future applications of AI for the practical use in medical care.

Этические и культурные вызовы внедрения искусственного интеллекта в медицинских практиках: мультикультуральный анализ

The use of AI in medical practice offers a number of significant and visible advantages. The problem of the presented research is relevant due to the growing integration of technologies into healthcare. The purpose of this study is to analyze the ethical and cultural challenges associated with integration of artificial intelligence (AI) in medical practice in various cultural contexts. The main objective of the study is to identify specific problems and suggest possible solutions to ensure effective and justifiable use of AI. To achieve this goal, a literary review, a case study, an expert interview, and a questionnaire were used. The main areas of ethical and cultural challenges include the issues of confidentiality and data protection; culturally specific attitudes towards automation of medical decision-making; the impact of algorithm bias on the diagnosis and treatment of various ethnic groups; ethical dilemmas related to access and fairness in the use of medical AI systems. The study highlights the need to develop ethical standards for the use of AI in medicine that will take into account cultural differences.

Knowledge and attitude of medical students towards artificial intelligence in ophthalmology in Riyadh, Saudi Arabia: a cross-sectional study.

The use of artificial intelligence (AI) in ophthalmology represents a transformative leap in healthcare. AI-powered technologies, such as machine learning and computer vision, enhance the accuracy and efficiency of ophthalmic diagnosis and treatment. This study aimed to determine medical students' awareness and attitudes towards the use of artificial intelligence in ophthalmology. This cross-sectional, questionnaire-based study was conducted between November 2022 and January 2023 using online questionnaires. Data collection was carried out using convenience sampling among medical students at the University. IBM SPSS version 23 was used to analyze the data. The current finding shows that most of the participants N=309 (89.6%) had heard of the use of AI in medicine, and N=294 (85.2%) heard of the use of AI in ophthalmology. 98.6% (n=340) of respondents believed AI would be a helpful tool in ophthalmology. Along this line of questioning, a significant majority of respondents, 332 (96.2%) selected screening, 332 (96.2%) selected diagnosis, and 293 (84.9%) selected prevention as a usage of AI ophthalmology. However, the majority, 76.5%) of students had little understanding of the development of AI in ophthalmology. In addition, a significant relationship between sex, academic year, cumulative GPA (cGPA), and awareness of AI in ophthalmology (P<0.001) was found in this study. Overall, medical students in Saudi Arabia appear to have favorable thoughts about AI and positive perceptions towards AI in ophthalmology. However, the findings of this study emphasize the limited understanding and low confidence levels of medical students in Saudi Arabia regarding the use of AI in ophthalmology. As a result, early exposure to AI-related materials in medical curricula is crucial for addressing these challenges through comprehensive AI education and practical exposure to prepare future ophthalmologists.

The Use of AI in Medicine: Health Data, Privacy Risks and More

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.

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.

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.

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: 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.