Automated Evaluation can Distinguish the Good and Bad AI Responses to Patient Questions about Hospitalization
This work addresses the scalability issue in evaluating AI health systems for patients and clinicians, though it is incremental in automating existing evaluation methods.
The study tackled the challenge of evaluating AI responses to patient questions about hospitalization by comparing automated metrics with expert ratings across 100 patient cases and 28 AI systems, finding that automated rankings closely matched expert judgments.
Automated approaches to answer patient-posed health questions are rising, but selecting among systems requires reliable evaluation. The current gold standard for evaluating the free-text artificial intelligence (AI) responses--human expert review--is labor-intensive and slow, limiting scalability. Automated metrics are promising yet variably aligned with human judgments and often context-dependent. To address the feasibility of automating the evaluation of AI responses to hospitalization-related questions posed by patients, we conducted a large systematic study of evaluation approaches. Across 100 patient cases, we collected responses from 28 AI systems (2800 total) and assessed them along three dimensions: whether a system response (1) answers the question, (2) appropriately uses clinical note evidence, and (3) uses general medical knowledge. Using clinician-authored reference answers to anchor metrics, automated rankings closely matched expert ratings. Our findings suggest that carefully designed automated evaluation can scale comparative assessment of AI systems and support patient-clinician communication.