KMC Manipal Opens India's First AI in Healthcare Department, Making Algorithms Core to Medical Education

Kasturba Medical College Opens India's First Department of AI in Healthcare

Kasturba Medical College, Manipal has formally opened its Department of Artificial Intelligence in Healthcare on August 29, confirmed by the institution's announcement. According to The Times of India, it is the first such department in India, embedding AI directly into medical education.

Why this matters for clinicians

New graduates will be expected to read algorithms with the same confidence they read ECGs. That expectation forces care teams, hospitals, and vendors to provide safe, validated AI tools-or risk falling behind patient needs and peer standards.

What the program includes

• A curriculum that blends clinical training with engineering and data science to build interdisciplinary skills.
• Radiology models that prioritize urgent scans to speed critical decisions.
• Public health tools that predict outbreaks at district hospitals.

Administrators project enrollment in the hundreds at first, scaling to thousands. For India-where physician shortages are common-AI can help extend expertise beyond urban centers through triage, decision support, and public health surveillance.

Implications for hospitals and departments

• Audit your AI readiness: data quality, IT infrastructure, governance, and clinical workflows.
• Select high-yield use cases first: imaging triage, sepsis alerts, antimicrobial stewardship, and bed-capacity forecasting.
• Establish validation and monitoring: compare model outputs against gold standards, track drift, and define action thresholds.
• Update credentialing and SOPs: clarify accountability, escalation paths, and documentation for AI-assisted decisions.
• Train the workforce: clinicians, nurses, and technicians need shared terminology and safety practices.

Regulatory and safety checkpoints

As AI education moves from electives to permanent structures, regulators will be tested on accuracy, bias, and safety standards. Clinicians should insist on transparent performance metrics, bias audits, provenance of training data, and post-deployment surveillance. See guidance from the World Health Organization on ethics and governance of AI in health for a practical baseline.

WHO guidance: Ethics and governance of AI for health

How to prepare your team now

• Set a baseline: quick primers on model types, evaluation metrics (AUROC, sensitivity/specificity), and common failure modes.
• Run simulations: practice edge cases and handoff protocols before live deployment.
• Use procurement checklists: require evidence of clinical validation, external testing, and human factors design.
• Measure outcomes: time-to-diagnosis, turnaround times, false alert rates, clinician workload, and patient safety events.
• Close the loop: capture feedback, audit usage, and recalibrate or decommission models that underperform.

For clinicians, the signal is clear: AI literacy is no longer optional. It is becoming a core part of medical practice, from training to bedside care.

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