One Size Doesn't Fit All Exams: Why Specialty-Specific AI Scribes Are Gaining Ground
Kayla Perlstein
Photo: Pexels
A dermatology note, an oncology plan and a well-child visit are not the same document. A growing number of vendors argue that AI scribes should not be the same tool either.
A dermatologist describing a lesion, an oncologist documenting a treatment plan and a pediatrician recording a well-child visit are not producing the same note. Yet many early ambient AI scribes were built as general-purpose tools. A growing number of vendors are now arguing that the next stage of the market belongs to scribes tuned to a specialty.
What "specialty-specific" is promising
Several vendors now market scribes built around a single specialty or a short list:
- Dermatology: ModMed has introduced ModMed Scribe for dermatology, which it describes as built by dermatologists and trained on specialty-specific data, with structured data outputs.
- Orthopedics: DeepScribe markets an ambient scribe trained on orthopedic care.
- Oncology and neurology: Marvix.ai offers tools aimed at the vocabulary and note requirements of those fields.
- Pediatrics: vendors point to needs unique to the specialty, such as age-specific documentation, vaccination tracking, developmental milestones and caregiver input.
These are vendor claims, and few have been independently validated, so treat them as hypotheses to test rather than facts.
Why it matters
Specialty practices work with their own terminology, templates and reporting requirements. A scribe that does not know a specialty's conventions may produce a note that reads well but needs heavy rework: the wrong structure, missed required elements or generic phrasing where precise language is expected. The promise of a specialty-tuned tool is fewer edits and notes that fit directly into existing workflows. Structured output matters too; if a note's data lands in the right EHR fields, it can support downstream tasks such as coding and quality reporting.
The tradeoffs
- Narrower scope. A tool tuned for one specialty may not serve a multispecialty group, and managing several vendors adds overhead.
- Customization versus training. Some general-purpose scribes let clinicians build custom templates for their own specialty. For some practices that flexibility may be enough.
- Evidence is thin. Claims that specialty tuning improves quality should be tested against your own notes.
How to evaluate one
- Bring your hardest visits. Test on complex, real encounters, not clean demos.
- Compare structure. Check whether the output matches your templates and the fields your EHR expects.
- Measure edits. Track how many corrections each note needs, specialty-tuned or general.
- Ask about the training data. Find out what the model was trained on and how patient data is handled.
- Check EHR integration. A great note you cannot file efficiently saves little time.
The takeaway
The ambient AI market is maturing from "one scribe for everyone" toward tools that understand how each specialty documents care. For practice administrators, the practical step is to define what a good note looks like in your specialty, then test candidates, general or specialized, against that standard before you buy.
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Sources
- ModMed, "ModMed Unveils Dermatology-Specific AI with ModMed Scribe": streetinsider.com
- DeepScribe, orthopedics: deepscribe.ai
- Vendor specialty overviews (Marvix.ai, Empathia)