AI for Interoperability in Health Care: Philips’s Carla Goulart Peron

2026-06-01 · Source: MIT Sloan Management Review · Field: Health & Wellbeing — Healthcare Systems & Policy, Medical Devices & Health Technology, Health & Medical Research · Depth: Intermediate, extended

Summary

Philips's chief medical officer, Carla Goulart Peron, highlights how artificial intelligence is transforming healthcare by expanding access, enhancing diagnostics, and allowing clinicians more patient-focused time. Drawing on her experience in Brazil's public health system, Peron emphasizes AI's role in bridging care gaps, particularly through technologies like AI-assisted imaging. Philips, a healthcare technology company, has developed tools such as SmartHeart, an FDA-cleared automated cardiac MR planning tool that reduces exam setup from 15 minutes to 30 seconds, significantly boosting machine throughput and diagnostic accuracy. Peron asserts AI adds to, rather than replaces, clinical roles, addressing the substantial undersupply of care globally. She also discusses challenges including patient trust, data bias, and the critical need for interoperability, which she identifies as the most impactful AI capability for global health.

Key takeaway

For healthcare leaders developing AI strategies, prioritize solutions that augment clinician capabilities and expand patient access, rather than focusing on clinician replacement. Your efforts should target workflow efficiencies, like automated imaging, and address critical data biases to ensure equitable care. Invest in interoperability as a foundational AI capability to enable longitudinal patient data visibility, which is crucial for improving outcomes and building patient trust in AI-enabled medicine.

Key insights

AI in healthcare augments clinicians to expand access and improve diagnostics, with interoperability identified as the most impactful global capability.

Principles

• AI adds to clinical capacity, it does not replace clinicians.
• Technology can bridge significant gaps in healthcare access.
• Medical protocols must incorporate diverse physiological data.

Method

AI-driven automation, like SmartHeart, plans cardiac MR setups in 30 seconds, reducing technician burden and increasing expensive machine throughput. This ensures "first time right" imaging.

In practice

• Automate routine imaging setup for efficiency.
• Use AI to analyze large datasets for protocol iteration.
• Prioritize interoperability for longitudinal patient data.

Topics

• Healthcare AI
• Medical Imaging
• Interoperability
• Patient Access
• Clinical Workflow
• Data Bias
• Women's Health

Best for: Executive, AI Product Manager, Product Manager, Director of AI/ML, Research Scientist, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by MIT Sloan Management Review.