Artificial intelligence (AI) is increasingly finding its way into medicine, and it is not yet clear how it will change the practice of medicine and the way doctors see themselves. This article explores the ethical limits of AI by (1) discussing the reductionistic elements inherent in AI, (2) working out the problematic implications of algorithmisation and (3) highlighting the lack of human control as an ethical problem of AI. The conclusion is that although AI is a useful tool to support medical judgement, it is absolutely dependent on human decision-making authority in order to actually prove beneficial for medicine.
Die künstliche Intelligenz (KI) hält zunehmend Einzug in die Medizin, und es ist noch nicht absehbar, wie sie die Praxis der Medizin und auch das Selbstverständnis der Ärztinnen und Ärzte verändern wird. In dem Beitrag werden die ethischen Grenzen der KI erarbeitet, indem (1.) der in der KI schon enthaltende Reduktionismus des Zugangs auf die Welt erörtert wird, (2.) die problematischen Implikationen einer Algorithmisierung herausgearbeitet werden und (3.) die mangelnde menschliche Kontrolle als ethische Grenze markiert wird. Als Schlussfolgerung ergibt sich daraus, dass die KI zwar ein nützliches Werkzeug zur Unterstützung ärztlicher Beurteilung darstellt, dass sie aber unabdingbar auf menschliche Entscheidungshoheit angewiesen ist, um sich tatsächlich als segensreich für die Medizin zu erweisen.

With over 16 years of experience in clinical, research, and educational activities related to transcranial magnetic stimulation (TMS), I have written this article exploring the ethical dimensions of TMS. This article aims to provide valuable and informative content for those unfamiliar with TMS as well as those just starting in the field. Specifically, this article elaborates on four principles of medical ethics, including those applicable to TMS therapy, the disparity between public medical insurance coverage and medical indications in private practice for TMS therapy, and issues concerning research ethics in practice. I also provide recommendations regarding roles and strategies for adoption by academia and those in this field dedicated to making TMS therapy accessible to a larger patient population in a suitable manner. Lastly, it is my hope that this article will serve as a contemporary \"Ethics of TMS Neuromodulation\", resonating with the inherent human pursuit of \"truth, goodness, and beauty\" for a sound mind and spirit.

BACKGROUND: The existence of a valid instrument to evaluate the attitude of mothers towards compliance with medical ethics during childbirth can lead to appropriate interventions to create a positive attitude. The purpose of this study is to determine the construct validity of the MEAVDQ (Medical Ethics Attitude in Vaginal Delivery Questionnaire).
METHODS: The study was carried out with 350 women. The main research instrument was MEAVDQ. This 59-item questionnaire comprises three parts A, B, J. Part A is concerned with the first principles. Part B deals with the second and third principles and part J addresses the fourth principle of medical ethics. Structural Equations Modeling (SEM) was used to determine the construct validity of MEAVDQ.
RESULTS: The results of SEM revealed that there was a positive correlation between structures A and B. The relationship between structures B and J was also positive and significant. On the other hand, there was a direct and indirect relationship between structures A and J. One-unit increase in structure A led to 0.16 (95% CI: 0.01, 0.33) direct increase in structure J. Also, one-unit increase score increases in structure A caused 0.39 indirect rise (95% CI: 0.26, 0.53) in structure J with the mediating role of the structure B.
CONCLUSIONS: It can be suggested to midwifery policy maker and midwives that respect for the first principle of medical ethics and autonomy is the most important principle of medical ethics in childbirth. By respecting the autonomy of mothers, a positive birth experience can be created for them.

暂无摘要。

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.

BACKGROUND: Medical students and doctors face various challenges in clinical practice. Some of these challenges are related to ethical issues. Therefore, teaching ethics respectively building moral competences has become an integral part of the medical curriculum in Germany and many other countries. To date, there is little evidence on moral competence of medical students.
METHODS: Self-administered survey among medical students from one German medical school in the first (cohort 1) and fifth semester (cohort 2) in the winter term 2019/20 (T0). Both cohorts received the same questionnaire one year later in winter term 2020/21 (T1). Assessment was performed with Lind\'s Moral Competence Test. We performed convenience sampling. We analyzed the data with descriptive statistics and C-Scores as a measure of moral competence (higher scores = higher competence, ≥ 30 points = high competence).
RESULTS: A total of 613 students participated in the study (response rate 67.5%, n = 288 with data on both time points). 69.6% of the participants were female, the mean age was 21.3 years. Mean C-Score for both cohorts for T0 (first and fifth semester) is 32.5 ± 18.0 and for T1 (third and seventh semester) is 30.4 ± 17.9. Overall, 6.6% (T0) and 6.7% (T1) of respondents showed some but very low moral competence. 3.3% (T0) and 3.0% (T1) showed no moral competence. Additionally, students without prior experience in the healthcare system scored 3.0 points higher.
CONCLUSIONS: Improvement of assessment of moral competence as well effective interventions are particular needed for supporting those students which have been identified to demonstrate little moral competences.

BACKGROUND: Studies from different countries report a stagnation or regression of moral competence in medical students between the first and the last year of their studies, and the value of various educational interventions remains uncertain.
METHODS: We used Moral Competence Test (MCT) to measure C-scores of moral competence to determine the change in the MCT C-scores between the first- and the fifth-year medical students from two medical schools in the Czech Republic in the academic year 2022/2023 and to analyze factors associated with the C-scores (observational study). In addition, for the first-year students, we compared the results of the MCT before and after an intervention in medical ethics curriculum (interventional study). We used a cross-sectional and descriptive design for the observational study. Students completed the MCT, consisting of two moral dilemmas (Worker´s Dilemma and Doctor´s Dilemma), the results measured by the C-score, which represents moral competence.
RESULTS: In total, 685 students participated in the observational study. Objective 1: based on the analysis of the C-score, we observed a decrease in moral competence between the first and the fifth-year medical students (p < .001). Objective 2: we did not observe a statistically significant effect of gender (p = .278), or self-rated religiosity (p = .163). Objective 3: in the interventional study, 440 students participated in the pretest and 422 students participated in the posttest. The test of statistical significance found no improvement in students\' moral competence after the intervention (p = .253).
CONCLUSIONS: Medical students show a regression in moral competence during medical education; it was lower in medical students in their fifth year, compared to the first-year medical students without the effect of gender, or self-rated religiosity. Although educational intervention consisting of multiple tools of medical ethics teaching (PBL, CBL, KMDD and StorED) did not lead to increase in moral competence, the longitudinal effect of such intervention remains to be seen.

暂无摘要。

THE ETHICS OF IA IN MEDICINE MUST BE BASED ON THE PRACTICAL ETHICS OF THE HEALTHCARE RELATIONSHIP. Artificial intelligence (AI) offers more and more applications on the Internet, smartphones, computers, telemedicine. AI is growing rapidly in health. Transdisciplinary, AI must respect software engineering (reliability, robustness, security), knowledge obsolescence, law, ethics because a wide variety of algorithms, more or less opaque, process personal data help clinical decision. Hospital or city doctors and caregivers question the benefits/risks/costs of AI for the patient, the care relationship, deontology and medical ethics. Drawing on 30 years of experience in AI and medical ethics, the author proposes a first indicator of the ethical risks of AI (axis 1) evaluated by the surface of a radar diagram defined on the other 6 axes: Semantics, Opacity and acceptability, Complexity and autonomy, Target population, Actors (roles and motivations). Highly autonomous strong AI carries the most ethic risks.
L’ÉTHIQUE DE L’IA EN MÉDECINE DOIT REPOSER SUR L’ÉTHIQUE PRATIQUE DE LA RELATION DE SOIN. L’intelligence artificielle (IA) offre de plus en plus d’applications de santé sur smartphones, ordinateurs, télémédecine, internet des objets. Transdisciplinaire, l’IA doit respecter l’ingénierie logicielle (fiabilité, robustesse, sécurité), l’obsolescence des connaissances, le droit, l’éthique, car une grande variété d’algorithmes, plus ou moins opaques, traitent des données personnelles dans l’aide à la décision clinique. Médecins et soignants hospitaliers ou libéraux s’interrogent sur les bénéfices, risques, coûts de l’IA pour le patient, la relation de soin, la déontologie et l’éthique médicale. S’appuyant sur trente ans d’expérience en IA et en éthique médicale, cet article propose un premier indicateur des risques éthiques de l’IA (premier axe) défini par la surface du diagramme radar des autres axes (sémantique ; opacité et acceptabilité ; complexité et autonomie ; population cible ; acteurs [rôles et motivations]). L’IA forte autonome est celle qui comporte le plus de risques éthiques.

暂无摘要。