By selecting the "Allow All" option, you are giving your consent to storing cookies on your device. These cookies are used to improve site navigation, analyze site usage, and support our efforts to provide free open access scientific content. For more information, please refer to our privacy policy. In a recent study shared on the medRxiv* server, a team of researchers investigated medical students' understanding, perceptions, and educational preferences regarding artificial intelligence (AI) in order to equip them with the necessary knowledge and skills to ethically and effectively utilize AI in the field of medicine. AI is widely utilized in medicine, employing algorithms and software to analyze digital data for diagnosis and treatment recommendations. Additionally, AI plays a crucial role in the evaluation of diagnostic images such as CT scans and skin images, offering decision support to doctors during disease diagnoses. Furthermore, AI can contribute to various other medical areas such as drug design and personalized treatment. However, there is limited knowledge regarding medical students' understanding of AI and its applications in medicine. To address this gap, the researchers conducted an online survey among medical students from a German university, focusing on their knowledge and association of AI with medicine. The survey questionnaire was distributed to the students via email in November 2022, with participation being voluntary and requiring written informed consent, without any compensation or rewards. The survey took approximately 10 minutes to complete and began by assessing the students' knowledge of AI implementation in the medical field and their general understanding of AI. The participants were then provided with a neutral definition of AI in medicine to ensure a basic understanding of the topic. Subsequently, using Likert Scales and semantic differential scales, the researchers collected data on how participants perceived various dimensions of AI in the medical context, including reliability, technical competence, credibility, trustworthiness, intelligence, and anthropomorphism. These assessments provided insights into the participants' attitudes and beliefs towards AI, aiding in the understanding of human-AI interaction and acceptance within the studied context. The selected scales were based on established research methodologies to ensure the validity and comparability of the study with existing literature. Finally, the participants were asked to express their opinions regarding the inclusion of AI in university courses and the specific aspects that should be incorporated into medical education.

The results indicated that out of 84 individuals who initiated the survey, 26 discontinued, leaving a total of 58 medical students (average age 24. 51 years, standard deviation [SD]=3. 56 years) who completed the survey. Notably, 94. 83% of the participants were aware of the role of AI in medicine and exhibited a solid understanding by identifying algorithms (58. 62%), machine learning (48. 28%), and neural networks (8. 62%) as key components. The majority associated AI in medicine with diagnostics (86. 21%) and surgeries (27. 59%). Participants considered AI in medicine to be trustworthy (M=3. 58, SD=0. 71), fairly reliable (M=3. 30, SD=0. 69), and technically proficient (M=3. 26, SD=0. 71), but less credible (M=2. 34, SD=0. 71). They also recognized AI as being intelligent (M=3. 75, SD=0. 66), yet lacking anthropomorphic attributes (M=1. 99, SD=0. 64). Their experience with AI was moderate (M=2. 85, SD=1. 41), including exposure to AI in an educational (M=2. 67, SD=1. 47) or medical context (M=2. 69, SD=1. 43). Nevertheless, they expressed strong interest in the medical potential of AI (M=4. 52, SD=0. 71) and a desire to learn more about AI in general (M=4. 38, SD=0. 83), as well as advocating for more comprehensive AI coverage in medical education (M=4. 17, SD=0. 92). Furthermore, a significant 86. 21% of participants agreed that basic AI knowledge should be integrated into medical studies. Specific areas they endorsed for inclusion were AI operation modes (77. 59%), ethics (75. 86%), application areas (75. 86%), reliability (94. 83%), and potential risks (89. 66%). However, they showed less support for teaching future developments (46. 55%) and legal aspects (46. 55%) of AI. The participants highlighted potential issues associated with AI in medicine, including ethical concerns (53. 45%), loss of control (43. 10%), and possible reliability issues (34. 48%). In summary, this study's findings indicated that the participants displayed a strong interest in the application of AI in the field of medicine and expressed a desire to acquire more knowledge throughout their academic journey. Consequently, it is crucial for medical schools to incorporate AI education into their curricula, ensuring that students acquire the necessary knowledge and skills to effectively and ethically utilize AI in their future medical practices. Additionally, addressing the potential risks linked to AI in medicine includes teaching the significance of human oversight and providing strategies for monitoring and rectifying errors in AI algorithms, ultimately ensuring that final decisions are made by human medical practitioners.