Public articles linked to the same research event.
Journal of Medical Internet Research This cross-sectional survey, distributed online through a health care news platform mailing list, gathered responses from 335 health care professionals (including 230 attending physicians, 68.7%) and found that 62.7% reported current or contemplated large language model use, users reported significantly higher self-reported knowledge than nonusers (P < .001), the most valued applications were literature review (73.4%), decision support (57%), and patient communication (54.9%), leading concerns were decision errors (75.5%) and algorithmic bias (73.1%), 96.4% expressed concern about bias with those who had observed bias reporting higher concern (P < .001), and respondents favored regulation by professional associations (65.4%) over technology companies (29%), with 87.
This cross-sectional survey, distributed online through a health care news platform mailing list, gathered responses from 335 health care professionals (including 230 attending physicians, 68.7%) and found that 62.7% reported current or contemplated large language model use, users reported significantly higher self-reported knowledge than nonusers (P < .001), the most valued applications were literature review (73.4%), decision support (57%), and patient communication (54.9%), leading concerns were decision errors (75.5%) and algorithmic bias (73.1%), 96.4% expressed concern about bias with those who had observed bias reporting higher concern (P < .001), and respondents favored regulation by professional associations (65.4%) over technology companies (29%), with 87.
This cross-sectional survey, distributed online through a health care news platform mailing list, gathered responses from 335 health care professionals (including 230 attending physicians, 68.7%) and found that 62.7% reported current or contemplated large language model use, users reported significantly higher self-reported knowledge than nonusers (P < .001), the most valued applications were literature review (73.4%), decision support (57%), and patient communication (54.9%), leading concerns were decision errors (75.5%) and algorithmic bias (73.1%), 96.4% expressed concern about bias with those who had observed bias reporting higher concern (P < .001), and respondents favored regulation by professional associations (65.4%) over technology companies (29%), with 87.
This cross-sectional survey, distributed online through a health care news platform mailing list, gathered responses from 335 health care professionals (including 230 attending physicians, 68.7%) and found that 62.7% reported current or contemplated large language model use, users reported significantly higher self-reported knowledge than nonusers (P < .001), the most valued applications were literature review (73.4%), decision support (57%), and patient communication (54.9%), leading concerns were decision errors (75.5%) and algorithmic bias (73.1%), 96.4% expressed concern about bias with those who had observed bias reporting higher concern (P < .001), and respondents favored regulation by professional associations (65.4%) over technology companies (29%), with 87.