Closing the AI generalisation gap by adjusting for dermatology condition distribution differences across clinical settings.

ABSTRACT:

Background

Generalisation of artificial intelligence (AI) models to a new setting is challenging. In this study, we seek to understand the robustness of a dermatology (AI) model and whether it generalises from telemedicine cases to a new setting including both patient-submitted photographs ("PAT") and clinician-taken photographs in-clinic ("CLIN").

Methods

A retrospective cohort study involving 2500 cases previously unseen by the AI model, including both PAT and CLIN cases, from 22 clinics in the San Francisco Bay Area, spanning November 2015 to January 2021. The primary outcome measure for the AI model and dermatologists was the top-3 accuracy, defined as whether their top 3 differential diagnoses contained the top reference diagnosis from a panel of dermatologists per case

SUBMITTER: Rikhye RV 

PROVIDER: S-EPMC12167068 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature