Archive · 4 Sep 2026
Clinical AI Must Go Beyond Accuracy to Earn Trust
Clinical AI uses computer models to help doctors find diseases earlier.
Many people judge these models mainly by how accurate they are.
Mansi Goel says a high score is not enough.
A model might work well for most people but make more mistakes for one group of patients.
Doctors also need to understand the model and know how to use its predictions.
Goel says developers should study clinical workflows before building the model.
Real medical records can be messy, incomplete, and different between hospitals.
Models should be tested with real-world data and checked for fairness across patient groups.
This can help make AI safer and more useful in healthcare.
Mansi Goel says clinical AI models should be judged on more than benchmark accuracy or area-under-the-curve scores.
Models can appear accurate overall while performing poorly for specific patient subgroups.
Goel’s clinical-first approach emphasizes clinician understanding, real workflows, and actionable predictions.
She supports evaluating models throughout their lifecycle, from electronic health record data to live deployment.
Goel argues that data quality and subgroup accountability are essential for models to generalize safely.
- Who
- Mansi Goel, a data scientist at Lucem Health, and healthcare organizations adopting predictive models.
- What
- The article examines why clinical AI should be evaluated for fairness, interpretability, data quality, clinical usefulness, and deployment performance—not only accuracy.
- When
- As hospitals and health systems increasingly adopt predictive models.
- Where
- Across hospitals and health systems, including live clinical settings where electronic health record data are used.
- Why
- Because models with strong overall scores can fail for specific patient groups, become difficult for clinicians to use, or work only on idealized research data.
- Main advocate
- Mansi Goel, a data scientist at Lucem Health
- Core argument
- Clinical AI requires more than benchmark accuracy
- Approach
- A clinical-first philosophy focused on end users and clinical workflows
- Data challenge
- Electronic health record data can be noisy, incomplete, and coded differently across institutions
- Conditions mentioned
- Cardiac arrhythmia, liver disease, and type 1 diabetes
- Evaluation scope
- The full lifecycle from raw data and feature engineering through training, validation, and deployment
- Accountability concern
- Poorly tested models may disproportionately harm underserved or underrepresented patient groups