Medical Expectations Survey on Artificial Intelligence Solutions in daily practice
ABSTRACT
Background:
Artificial intelligence (AI) applied to Medicine has become one of the hottest topics for the past years. Although scarcely used in real practice, it brings along many expectations, doubts and fears for physicians. Surveys can help to understand this situation.
Objective:
To explore the degree of knowledge, expectations, fears and daily practice questions on AI use by physicians.
Methods:
an electronic survey was sent to physicians of a large hospital in Brazil, from August to September 2022.
Results:
171 physicians responded to our survey. 54% considered themselves to have an intermediate knowledge of AI. 79% believe AI should be regulated by a Governmental Agency. If AI were reliable and available, 78% intend to use AI frequently/always for diagnosis (87%) and/or management (83%), but they were unsure about the use of AI by other health professionals (50%) or by the patients (51%). The main benefit would be increasing the speed for diagnosis and management (64%), and the worst issue, to over rely on AI and lose medical skills (71%). Physicians believe AI would be useful (94%), facilitate the work (87%), increase the number of appointments (54%), not interfere in the financial gain (58%) and not replace their jobs, but, rather, be utilized as an additional source of information (65%). In case of disagreement between AI and physicians, most answered that a third opinion should be requested (86%). There were no significant differences between the physicians answers according to time since graduation.
Conclusions:
physicians showed to have good expectations regarding the use of AI in Medicine when applied by themselves, but not so much by others. They also have intention to use it, as long as it was approved by a Regulatory Agency. Although there was hope for the beneficial impact of AI on healthcare, it also brings specific concerns.
Citation
Per the author's request the PDF is not available.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.