Picture a clinic where test results are summarized in seconds, rare diseases are flagged before symptoms spiral, and clinical trials enroll the right patients in days—not months.
That future isn’t science fiction; it’s what happens when the speed of innovation collides with medicine, and we can thank artificial intelligence.
The future of healthcare is being written at unprecedented speed. AI is rapidly emerging as one of the most transformative forces in medicine, attracting massive investment and intense innovation. There are now over 70,000 AI startups worldwide, with $100 billion invested in 2024 alone. Digital health funding has surged by 47 percent in recent months. AI is claiming most of today’s “mega-rounds,” with AI start-ups capturing more than half of all digital health funding. Even the CDC is in on the AI act, deploying an AI Accelerator Program to develop and scale solutions for public health.
AI coding assistants have revolutionized software development, while advances in open-source foundation models and fine-tuning techniques have democratized access to cutting-edge AI capabilities. What once required months of work from large engineering teams now happens in weeks with small, agile squads, compressing the journey from concept to market-ready product. This acceleration is transforming how quickly we can deliver better diagnoses, safer therapies, and more equitable access to care.
As a longtime healthcare executive and investor, I'm particularly energized by two use cases that could fundamentally transform how we develop and deliver medicine. The first is making drug development more efficient using Big Data. Even as multiomics have accelerated discovery by surfacing more viable drug candidates, those gains have not yet translated into a surge of approved therapies because the true bottleneck sits in clinical development. The time and cost to advance a drug through clinical trials and regulatory approval exceeds 10 years and $1 billion on average.
But AI can bend the curve. By combining real-world data, synthetic data, and digital twins, innovators can simulate trial design—anticipating failures and dropouts—to make studies smaller, faster, and more patient-centered. We can even replace certain placebo groups with AI-powered control arms, protecting patients while preserving scientific rigor. If we do it well, we will shorten the path from bench to bedside, expanding the pool of people who benefit from the therapies brought forward. Recent research predicts that AI will slash the time and cost of drug development by years (potentially even half) and billions of dollars.
A second AI use case is enabling doctors to practice “deep medicine,” a term coined by physician and author Eric Topol. As Dr. Topol argues, the real promise of AI is more humanity, not less. When algorithms summarize notes, reveal patterns, and manage routine tasks, clinicians reclaim their two scarcest resources in care: time and attention. In my previous role working at one of America’s largest urgent care operators, I saw firsthand how the overwhelming majority of patient visits were routine cases that could have been handled by a well-trained AI triage engine. If AI manages those straightforward visits safely, doctors can focus their expertise on the complex minority of cases, which demand complex reasoning, careful judgment, and hands-on care.
Ultimately, AI can reduce physician burnout and restore joy to clinical work, improving clinical outcomes. AI won’t replace doctors; it will simply give back what patients deserve and need most—their full attention.
Of course, serious headwinds remain for AI. Access to high-quality data remains fragmented and heavily siloed, preventing startups from developing robust, generalizable models. This challenge is particularly acute in healthcare, where medical records are incomplete and isolated within hospital systems while claims data are closely guarded by commercial payers. Regulatory initiatives like the 21st Century Cures Act in the United States and the European Health Data Space initiative create a cross-border health data infrastructure, aiming to break down silos by legally requiring interoperability and access.
There is also legal uncertainty surrounding intellectual property, forcing startups to navigate unclear “fair use” boundaries for training data while facing potential litigation over model outputs. And the risks of AI misuse are well-documented—especially in healthcare, where a flawed algorithm can amplify bias, leak sensitive data, or recommend unsafe treatments.
However, with effective internal and external safeguards, the benefits of AI in healthcare far outweigh the risks. AI won’t fix healthcare by itself, but it can help deliver care that is faster, fairer, and more human.
If we rise to the challenge together, with urgency, humility, and collaboration, we can make healthcare more efficient and more compassionate. That is a future worth building.
Quentin Chu is a longtime healthcare executive and investor. He currently serves as managing member of Quercus Capital.
