Clinical AI's Next Step: Safety, Trust, and Real-World Use
Every clinical decision carries weight. That's why healthcare AI has to put safety, accuracy, and clinician trust ahead of everything else.
Nabla takes a focused path: build purpose-built models for care, keep clinicians in the loop for continuous review, and be transparent about how the tech works. The company supports the Coalition for Health AI, pushing for clear standards that healthcare teams can actually use.
Why purpose-built models matter
Healthcare isn't a generic use case. Clinical language is dense, context-heavy, and risk-sensitive. Models trained and fine-tuned for medicine handle abbreviations, comorbidities, and edge cases better than general systems.
Owning the model path also improves control over updates, safety thresholds, and data governance. That level of control is hard to match with off-the-shelf models.
Safeguards that earn clinician trust
- Human-in-the-loop: licensed clinicians continuously review outputs and guide updates.
- Clear provenance: versioning, model documentation, and change logs you can audit.
- Safety rails: calibrated uncertainty, graceful "abstain" behavior, and easy escalation to a human.
- Bias and performance monitoring across specialties, demographics, and settings.
- Privacy-first integration with EHR/EMR workflows and full audit trails for compliance.
- Alignment with transparency standards advocated by the Coalition for Health AI.
Lessons from reaching 85,000+ clinicians
Adoption sticks when AI reduces clicks and cognitive load inside the EHR, not outside it. If it saves time and avoids rework, clinicians use it.
Training is required but should be lightweight: quick patterns, clear limits, and examples. Treat AI like a junior assistant-useful, fast, and always reviewable.
Measure what matters: documentation quality, time saved, error rates, and patient experience. Share those results openly to build team trust.
What's next for clinical AI
Stronger guardrails and clearer regulatory guidance will push AI into more frontline tasks-ambient documentation, order suggestions, and patient messaging-without adding risk. Expect multimodal inputs (notes, labs, voice, imaging context) to improve relevance and reduce back-and-forth.
On the IT side, vendor-neutral integrations and pre-validated pathways will matter as much as model quality. Reliable analytics and EHR alignment will separate helpful tools from noise.
Hear it from the source
Martin Raison, co-founder and CTO of Nabla, will break down the technical playbook: why they build their own models, the safeguards behind clinical safety and trust, and lessons from scaling to more than 85,000 clinicians-plus what's coming next.
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