Study finds no real-world multilingual benchmarks for clinical AI scribes in India
DrDatta_AIIMS · x · 2026-10-28
A paper accepted to the ML4H 2026 Findings track, "Evaluating Ambient Clinical Scribes in India," warns that ambient clinical scribes (ACS) spreading rapidly across Global South healthcare lack adequate safety evaluation.
- Most ACS are built or distilled from models trained on Global North speech, languages, and consultation styles, while Indian clinical encounters are brief, triadic, multilingual, code-mixed with low-resource languages, and held in noisy, resource-constrained settings — multiplying ASR and note-generation error risks.
- The mixed-methods study combines a systematic survey of public patient-clinician conversational datasets, quantitative comparison against markers from Indian clinical-communication literature, and interviews with five organizations deploying ACS in India and Africa.
- Conclusion: no publicly available large-scale real-world ACS benchmarks exist for India; standardized evaluation infrastructure is urgently needed.
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