Healthcare AI
AI for healthcare. Privacy-first. Federated.
AI agent consulting for hospitals, payers, pharma, and digital health. The author of Fed-Focal Loss (93 citations, FL-IJCAI 2020) and the Substrate Pattern. Privacy-by-design: federated learning, differential privacy, and runtime policy gates.
Use cases
Clinical workflow automation
AI agents that automate clinical workflows: appointment scheduling, prior authorisation, discharge planning, follow-up.
Decision support
AI agents that assist clinicians with diagnosis, treatment planning, and risk stratification. Tier-1 advise with clinician sign-off.
AI for diagnostics
AI for medical imaging, pathology, and other diagnostic tasks. High-stakes; FDA / EU MDR regulated.
Federated learning across hospitals
Train AI models across hospitals without moving patient data. Dipankar is the author of Fed-Focal Loss.
Patient-facing AI
AI agents that assist patients with medication adherence, lifestyle, and chronic disease management. Tier-1 advise with clinician escalation.
FAQ
What does AI consulting for healthcare look like?
AI consulting for healthcare is the practice of building production AI systems for hospitals, payers, pharma, and digital health companies. The engagement covers: clinical workflow automation, decision support, AI for diagnostics, federated learning across hospitals, and HIPAA / GDPR / DPDP compliance. Typical engagements: 4-12 weeks, USD 50K-200K.
What are the AI compliance regulations for healthcare?
The main regulations: (1) HIPAA (US) — for protected health information. (2) GDPR (EU) + DPDP Act (India) — for personal data. (3) FDA 21 CFR Part 11 (US) — for AI used in clinical decision support. (4) EU MDR (Medical Device Regulation) — for AI classified as a medical device. (5) EU AI Act — for high-risk AI in healthcare. (6) ISO/IEC 27001 + 27799 — for information security. The Tiered Governance Model maps all of these to the 4 tiers.
How do you handle patient data privacy in AI systems?
Three primitives: (1) Federated learning — train across hospitals without moving patient data. Dipankar is the author of Fed-Focal Loss (93 citations, FL-IJCAI 2020) and CatFedAvg. (2) Differential privacy — mathematical guarantees on individual privacy. (3) Substrate Pattern — runtime policy gates that prevent the model from seeing data it should not see. Together, these three primitives enable AI systems that respect patient privacy by design.
Engage on Healthcare AI
A 30-minute call, free, no obligation. NDAs are standard. If there's a fit, we scope a 4-week audit.
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