Research
AI's potential in healthcare must be matched by equally rigorous safety research. At Sane, we explore the boundaries of model behavior, interpretability, and risk, ensuring that every advancement is paired with robust understanding and practical mitigation strategies.
We publish findings, contribute to global safety standards, and work with partners to ensure clinical AI improves lives responsibly, not recklessly.
Our Research Pillars
Clinical Alignment
Fine-tuning models to reflect clinical reasoning and evidence-based practice.
Interpretability
& Transparency
Understanding how AI arrives at decisions.
Safety Engineering
Techniques and safeguards that prevent harmful outcomes.
Societal Impact
Assessing the broader effects of clinical AI in healthcare systems.
Publications
| Date | Category | Title |
|---|---|---|
| Dec, 2025 | Socio-Technical Alignment | Context is not optional in the future of mental healthcare |
| Jan, 2025 | Clinical AI Governance | Evaluating AI-Driven Mental Health Solutions: A Hybrid Fuzzy Multi-Criteria Decision-Making Approach |
| Dec, 2024 | Clinical AI Implementation | Integrating AI in Mental Health Services: Challenges and Innovations for Information Professionals |