Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Chatbots for medical students exploring medical students’ attitudes and concerns towards artificial intelligence and medical chatbots
1
Zitationen
4
Autoren
2023
Jahr
Abstract
Introduction: artificial intelligence (AI) encompasses the concept of automated machines that can perform tasks typically carried out by humans, doctor-patient communication will increasingly rely on the integration of artificial intelligence (AI) in healthcare, especially in medicine and digital assistant systems like chatbots. The objective of this study is to explore the understanding, utilization, and apprehensions of future doctors at the Faculty of Medicine in Casablanca regarding the adoption of artificial intelligence, particularly intelligent chatbots. Methods: a cross-sectional study was conducted among students from the 1st to 5th year at the Faculty of Medicine and Pharmacy in Casablanca. Probability sampling was implemented using a clustered and stratified approach based on the year of study. Electronic forms were distributed to randomly selected groups of students. Results: among the participants, 52 % of students fully agreed to utilize chatbots capable of answering health-related queries, while 39 % partially agreed to use chatbots for providing diagnoses regarding health conditions. About concerns, 77 % of the respondents expressed fear regarding reduced transparency regarding the utilization of personal data, and 66 % expressed concerns about diminished professional autonomy. Conclusion: Moroccan Medical students are open to embracing AI in the field of medicine. The study highlights their ability to grasp the fundamental aspects of how AI and chatbots will impact their daily work, while the overall attitude towards the use of clinical AI was positive, participants also expressed certain concerns
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.549 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.443 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.941 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.792 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.