AI meets the scalpel: The promise and prematurity of AI in healthcare

Time of India

08-05-2025

Saleh Dadkhahipour is an Iranian-born MBA student at Stanford Graduate School of Business, focusing on AI, business, and economic development. With a consulting background and experience across three continents, he is passionate about leveraging technology to drive economic transformation and foster cross-cultural collaboration. LESS ... MORE
'Healthcare in the US will likely get worse before it gets better,' said Amit Garg, Managing Partner at Tau Ventures, as we wrapped up our fireside chat on Stanford GSB's campus. The comment landed with a quiet finality, not alarmist, but precise. Garg, a seasoned venture capitalist focused on AI and digital health, wasn't hedging his bets. He was diagnosing a system with chronic ailments, from administrative bloat to perverse financial incentives, and forecasting a painful course of treatment.
His view is one I increasingly share. As an MBA student immersed in innovation and entrepreneurship, I'm surrounded by peers building the future of healthcare. Yet even the most promising technologies seem to run headlong into a legacy system engineered more for reimbursement cycles than patient outcomes.
The dysfunction we live with
The US spends over $4.5 trillion annually on healthcare, more than any other country by far. Yet our outcomes trail peers across every major health metric. Why? Because the system isn't designed to deliver care; it's designed to navigate itself.
Patients, providers, payers, pharmacy benefit managers, and policymakers operate in a web of misaligned incentives. Physicians drown in paperwork. Hospitals battle reimbursement codes. Innovation struggles to find oxygen in a space starved of interoperability and obsessed with liability. As Garg put it, 'Too many players benefit from the status quo.'
The hope: What AI can actually do
For all the dysfunction embedded in today's healthcare systems, artificial intelligence offers the most credible path to transformation. Its potential isn't theoretical anymore, it's unfolding in labs, clinics, and codebases around the world. Here's what AI is already
doing:
Diagnostics: Tools like DeepMind's AlphaFold or PathAI are now detecting diseases with a level of accuracy that sometimes exceeds trained physicians. In radiology, AI-assisted models have improved early cancer detection rates by up to 30% in clinical trials.
Drug discovery: Companies like Insilico Medicine and Recursion are compressing drug development timelines by simulating molecular interactions and optimizing clinical trial design. The average cost of bringing a new drug to market is over $2 billion, AI may soon slash that.
Surgical support: Robotic and AI-assisted systems are now guiding surgeons in real time, enhancing precision and reducing complications. In orthopaedics, for instance, AI tools can predict post-surgical outcomes based on thousands of prior cases.
Admin relief: Clinicians spend nearly half their workday on paperwork. AI is increasingly automating billing, transcription, and prior authorizations, freeing up time for patient care. A recent study found that 90% of doctors cite administrative burden as a major cause of burnout.
Personalized medicine: By analyzing genomic data, lab results, and patient histories, AI enables tailored treatment plans that outperform standardized approaches. This is especially promising in oncology, where individual responses to therapy vary drastically.
These advances are not just tools, they're becoming the infrastructure for a new kind of healthcare. But like all infrastructure, they must be embedded into systems that function. That's the real challenge.
The Reluctant Testbed
A few weeks ago, I was speaking with a venture capitalist on Stanford's campus who put it bluntly: in most sectors, you can launch a 'good enough' product, iterate fast, and let the market be your testing ground. But in healthcare, 'good enough' is never good enough. The stakes are too high; lives are quite literally on the line.
This has profound implications for founders and funders alike. Health-tech start-ups often face longer development timelines, complex regulatory approvals, and resistance from hospitals or providers who demand not just innovation but certainty. It affects how teams are built, how capital is raised, and how motivation is sustained across what can feel like a marathon of clinical trials, FDA filings, and institutional gatekeeping.
At GSB, I've seen students build beautifully engineered health products, only to find out their biggest challenge isn't the tech but the trust. In this industry, the minimum viable product isn't just code; it's clinical proof.
Before It Gets Better
Still, Garg is clear-eyed. 'We are near the peak of inflated expectations,' he recently wrote, referencing Gartner's famous hype cycle. 'But we also fundamentally believe that the plateau of productivity will lead to tectonic shifts.'
Those shifts won't come easily. In the US, we may see more burnout, deeper inequality, and slower adoption before the gains of AI and digital health reach the average patient. But ignoring these tools would be malpractice.
Because when an algorithm can catch what the eye might miss, when a doctor gets hours back from the clutches of bureaucracy, and when a village gains access to care through a screen, that's not just technology. That's a system beginning to heal.
And maybe, just maybe, it's worth the pain of getting there.
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Views expressed above are the author's own.