Tamás A Martinek, Ferenc Otvös, Máté Dervarics, Géza Tóth, Ferenc Fülöp
Ligand-based prediction of active conformation by 3D-QSAR flexibility descriptors and their application in 3+3D-QSAR models
Journal of medicinal chemistry
large language models llms gpt4 hold great promise transformative tools health care ranging automating administrative tasks augmenting clinical decision making however models also pose danger perpetuating biases delivering incorrect medical diagnoses direct harmful impact medical care aimed assess whether gpt4 encodes racial gender biases impact use health care using azure openai application interface model evaluation study tested whether gpt4 encodes racial gender biases examined impact biases four potential applications llms clinical domainnamely medical education diagnostic reasoning clinical plan generation subjective patient assessment conducted experiments prompts designed resemble typical use gpt4 within clinical medical education applications used clinical vignettes nejm healer published research implicit bias health care gpt4 estimates demographic distribution medical conditions compared true us prevalence estimates differential diagnosis treatment planning evaluated across demographic groups using standard statistical tests significance groups found gpt4 appropriately model demographic diversity medical conditions consistently producing clinical vignettes stereotype demographic presentations differential diagnoses created gpt4 standardised clinical vignettes likely include diagnoses stereotype certain races ethnicities genders assessment plans created model showed significant association demographic attributes recommendations expensive procedures well differences patient perception findings highlight urgent need comprehensive transparent bias assessments llm tools gpt4 intended use cases integrated clinical care discuss potential sources biases potential mitigation strategies clinical implementation priscilla chan mark zuckerberg