Language models are susceptible to incorrect patient self-diagnosis in
medical applications
- URL: http://arxiv.org/abs/2309.09362v1
- Date: Sun, 17 Sep 2023 19:56:39 GMT
- Title: Language models are susceptible to incorrect patient self-diagnosis in
medical applications
- Authors: Rojin Ziaei and Samuel Schmidgall
- Abstract summary: We present a variety of LLMs with multiple-choice questions from U.S. medical board exams modified to include self-diagnostic reports from patients.
Our findings highlight that when a patient proposes incorrect bias-validating information, the diagnostic accuracy of LLMs drop dramatically.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models (LLMs) are becoming increasingly relevant as a
potential tool for healthcare, aiding communication between clinicians,
researchers, and patients. However, traditional evaluations of LLMs on medical
exam questions do not reflect the complexity of real patient-doctor
interactions. An example of this complexity is the introduction of patient
self-diagnosis, where a patient attempts to diagnose their own medical
conditions from various sources. While the patient sometimes arrives at an
accurate conclusion, they more often are led toward misdiagnosis due to the
patient's over-emphasis on bias validating information. In this work we present
a variety of LLMs with multiple-choice questions from United States medical
board exams which are modified to include self-diagnostic reports from
patients. Our findings highlight that when a patient proposes incorrect
bias-validating information, the diagnostic accuracy of LLMs drop dramatically,
revealing a high susceptibility to errors in self-diagnosis.
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