Attitudes towards medical artificial intelligence talent cultivation: an online survey study.

ABSTRACT

BACKGROUND: To investigate the attitude and formal suggestions on talent cultivation in the field of medical artificial intelligence (AI). METHODS: An electronic questionnaire was sent to both medical-related field or non-medical field population using the WenJuanXing web-application via social media. The questionnaire was designed to collect (I) demographic information; (II) perception of medical AI; (III) willingness to participate in the medical AI related teaching activities; (IV) teaching content of medical AI; (V) the role of medical AI teaching; (VI) future career planning. Respondents' anonymity was ensured. RESULTS: A total of 710 respondents provided valid answers to the questionnaire (57.75% medical related, 42.25% non-medical). About 73.8% of respondents acquired related information from network and social platform. More than half the respondents had basic perception of AI applicational scenarios and specialties in medicine, meanwhile were willing to participate in related general science activities (conference and lectures). Respondents from medical healthcare related fields, with high academic qualifications of male ones demonstrated showed significant better understanding and stronger willingness (P<0.05). The majority agreed medical AI courses should be set as major elective (42.82%) during undergraduate stages (89.58%) involving medical and computer science contents. An overwhelming majority of respondents (>80%) acknowledged the potential roles of medical AI teaching. Surgeon, ophthalmologist, physicians and researchers are the top tier considerations for ideal career regardless of AI influence. Radiology and clinical laboratory subjects are more preferred considering the development of medical AI (P>0.05). CONCLUSIONS: The potential role of medical AI talent cultivation is widely acknowledged by public. Medical related professions demonstrated higher level of perception and stronger willingness for medical AI educational events. Merging subjects as radiology and clinical laboratory subjects are preferred with broad talents demands and bright prospects.