2 research outputs found
Addressing cognitive bias in medical language models
There is increasing interest in the application large language models (LLMs)
to the medical field, in part because of their impressive performance on
medical exam questions. While promising, exam questions do not reflect the
complexity of real patient-doctor interactions. In reality, physicians'
decisions are shaped by many complex factors, such as patient compliance,
personal experience, ethical beliefs, and cognitive bias. Taking a step toward
understanding this, our hypothesis posits that when LLMs are confronted with
clinical questions containing cognitive biases, they will yield significantly
less accurate responses compared to the same questions presented without such
biases. In this study, we developed BiasMedQA, a benchmark for evaluating
cognitive biases in LLMs applied to medical tasks. Using BiasMedQA we evaluated
six LLMs, namely GPT-4, Mixtral-8x70B, GPT-3.5, PaLM-2, Llama 2 70B-chat, and
the medically specialized PMC Llama 13B. We tested these models on 1,273
questions from the US Medical Licensing Exam (USMLE) Steps 1, 2, and 3,
modified to replicate common clinically-relevant cognitive biases. Our analysis
revealed varying effects for biases on these LLMs, with GPT-4 standing out for
its resilience to bias, in contrast to Llama 2 70B-chat and PMC Llama 13B,
which were disproportionately affected by cognitive bias. Our findings
highlight the critical need for bias mitigation in the development of medical
LLMs, pointing towards safer and more reliable applications in healthcare
NHANES compiled data
NHANES data is publicly available. The collated input data for the manuscript 'Predictive modeling of lean body mass, appendicular lean mass, and appendicular skeletal muscle mass using machine learning techniques: a comprehensive analysis utilizing NHANES data and the Look AHEAD study' is available here.</p