ET NOW GBS 2026: AI can cut healthcare costs and close the talent gap
At ET NOW's Global Business Summit, industry leaders pressed a simple point with big consequences for healthcare: partner with academia, upskill the workforce for AI-first roles, and use AI to extend expert care beyond major hospitals. Done well, this shift can lower costs, improve diagnostic accuracy, and spread high-quality care to district-level facilities.
Why this matters to hospitals and clinicians
- AI decision support can standardize quality at the point of care, especially where specialist coverage is thin.
- Imaging AI can triage and pre-read MRI/CT, reduce turnaround times, and help allocate radiologist time to the toughest cases.
- Robotics and tele-mentored procedures can raise consistency in surgical outcomes and enable city specialists to support teams in smaller towns.
- Teacher and preceptor capacity can scale through AI-driven content, simulations, and case-based training.
Voices from the summit
Rakesh Bharti Mittal called for industry to sit with academia-beyond curriculum design-to define what the next generation should be able to do on day one. His view: AI belongs in classrooms and clinical training to build capacity and job readiness, across services and manufacturing.
Shobana Kamineni shared how an "AI layer" is already helping train doctors from district colleges so most core decisions happen confidently in front of them. She noted AI is being used to read MRIs and other diagnostics to cut costs, and that surgical robots can help young doctors deliver dependable results under guidance. She also pointed to remote expertise, where specialists support procedures in smaller towns.
Risks you should plan for
Two concerns came up: whether heavy AI use blunts clinical thinking in early-career doctors, and how to ensure ethical, safe deployment. Both demand clear guardrails.
- Keep clinicians in the loop: AI as assist, final judgment stays with the human.
- Track model performance: error rates, bias, drift, and real-world outcomes by site and cohort.
- Explainability and documentation: know indications, contraindications, and confidence ranges.
- Privacy and security: PHI protection, audit trails, and vendor risk reviews.
- Informed consent: tell patients when AI is used and how it affects decisions.
For reference, see the WHO's guidance on ethics and governance for AI in health (WHO).
What this means for India's workforce
With aging populations in countries like China and Japan, there's a clear opening for skilled Indian clinicians, data specialists, and biomedical engineers. If academia and industry align on competencies-clinical AI literacy, data quality, validation, and workflow redesign-India can export capability, not just services.
A practical 90-day plan for hospital leaders
- Weeks 1-2: Pick one high-ROI use case (e.g., MRI triage, ER chest X-ray prioritization, sepsis early warning). Define success metrics and governance.
- Weeks 3-6: Run a shadow-mode pilot. Compare AI vs. clinician reads, calibration, and time saved. Document edge cases and escalation paths.
- Weeks 7-10: Integrate into PACS/RIS/EHR with role-based access. Train radiologists, surgeons, and nurses on when to trust, when to override.
- Weeks 11-12: Review outcomes with QA and ethics boards. Decide on scale-up, model updates, and post-market surveillance cadence.
Building the talent pipeline
- Co-create modules with medical colleges: imaging AI fundamentals, prompt-based clinical documentation, and safety cases.
- Stand up simulation clinics: AI-assisted triage, OR checklists with robotics, and tele-mentoring drills for district hospitals.
- Upskill preceptors: use AI tools to generate case libraries, grading rubrics, and feedback loops.
If you're structuring role-based upskilling tracks for clinicians, admins, and IT, these curated pathways can help: AI courses by job and the latest AI courses.
Bottom line
AI can compress costs, widen access, and raise baseline quality across India's health system-if leaders pair deployment with training, oversight, and tight integration into clinical workflows. Build the skills, validate the tools, and keep clinicians in charge. That's how you scale expert care everywhere.
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