ChatGPT Health - When Health Meets Intelligence

There's something uniquely humbling about realizing that the frontier of transformation isn't always where you're looking.

For the past few months at DOMS, I've been immersed in how artificial intelligence might reshape taxation - from pattern recognition in transfer pricing to automating compliance checks. I've spoken to students at DTU and LBSIM about AI in finance, always circling back to efficiency, accuracy, and scale. But when OpenAI announced ChatGPT Health last week, I found myself thinking less about algorithms and more about my father.

He passed away in 2011. In those final months, I watched him navigate a maze of medical appointments, test results scattered across multiple hospitals, medication schedules that changed with bewildering frequency. My mother would carry a worn folder stuffed with reports, trying to piece together narratives for each new specialist. "What did the cardiologist say about the kidney function tests?" Simple questions that demanded complex archaeology through fragmented records.

What struck me about the ChatGPT Health launch wasn't the technology itself - we've known AI could process medical data for years. It was the fundamental reorientation of the question it answers.

The Question We're Actually Asking

Most health technology asks: "How do we make healthcare systems more efficient?"

ChatGPT Health asks something more intimate: "How do we help people understand their own bodies?"

The distinction matters immensely. With 230 million health questions being asked on ChatGPT weekly, OpenAI identified something profound: people aren't just looking for medical expertise - they're looking for translation, synthesis, and partnership in making sense of their health journey.

This is where my work in tax policy and health technology unexpectedly converge. Both deal with systems that have grown so complex that the gap between expert and citizen has become a chasm. Both struggle with fragmentation - multiple touchpoints, different data formats, institutional silos. And both are being transformed not primarily by making the system work better, but by empowering individuals to navigate the system more effectively.

Consider the practical reality. A person managing diabetes doesn't just need their glucose levels measured - they need those levels contextualized against their medication timing, exercise patterns, sleep quality, and stress levels captured across different apps and devices. The old approach: spend 15-30 minutes daily manually logging information, then several hours before each appointment trying to compile three months of scattered notes. The new possibility: upload your data, receive synthesized insights, generate a comprehensive health summary in two minutes. Arrive at your appointment with specific, data-informed questions.

That's not just efficiency - that's a fundamental shift in agency.

What Makes This Different

Working this closely with policy implementation at CBDT, I've learned to distinguish between technological novelty and genuine transformation. ChatGPT Health demonstrates several design choices that signal the latter.

First, the separation of health data from the general ChatGPT environment. All health conversations exist in a protected space, encrypted by default, with 30-day deletion options. This data won't train their foundation models. In an era where data privacy concerns often derail promising innovations, OpenAI chose to build walls between their business model and your medical information.

That matters because trust is the currency of health technology.

Second, the explicit framing: "designed to support, not replace, medical care." The technology positions itself as infrastructure, not authority. This reminds me of how we've been thinking about AI in tax administration - the goal isn't to replace tax professionals with algorithms, but to free them for complex judgment calls while AI handles what's systematic.

The Questions That Surface

But every powerful tool creates new responsibilities. Several questions keep surfacing for me:

The equity dimension: Connecting medical records and wellness apps assumes you have both. In India, where healthcare records are increasingly digital but far from universal, where Apple Health penetration remains limited to a small urban demographic, who benefits from this technology? How do we prevent health AI from becoming another layer of advantage for the already advantaged?

The interpretation gap: AI can identify patterns in your glucose levels, but can it distinguish between correlation and causation in complex biological systems? The AI might flag the pattern, but who owns the interpretation?

The data dependency: What happens when people begin outsourcing their health literacy to AI? There's profound value in learning to read your own body's signals. Does AI synthesis enhance that literacy or erode it? (or may increase anxiety!!)

These aren't questions with clear answers. They're tensions to be navigated.

A Personal Thought

I keep thinking about those folders my mother carried, stuffed with medical reports. In some future I can now imagine, those reports would flow seamlessly into a secure space where patterns emerge, where questions form themselves, where the doctor's appointment becomes a genuine conversation.

My father won't benefit from that future. But millions of others might. And if we get this right - if we build these tools with intention, with equity, with clear boundaries - they'll benefit not just from more efficient healthcare, but from deeper understanding of their own wellbeing.

That's the possibility that keeps me engaged with AI. Not because technology solves everything, but because thoughtfully deployed, it can shift the balance of agency back toward individuals navigating complex systems.