Everyone loves to claim that artificial intelligence (AI) is changing everything, including pharma. But once you strip away the buzzwords, the real story is more grounded and far more compelling. 

The startups making real progress rethinking how drugs are developed, how patients are monitored, and how healthcare decisions are made. They’re navigating red tape, challenging outdated processes, and fixing what traditional pharma has long accepted as “just the way things are.”

And it’s already underway, but only a small group of companies are actually delivering results. 

So let’s skip the hype and dig into where AI and digital health are truly shifting the landscape and what makes those few startups worth paying attention to.

1. Drug Discovery

Yes, drug discovery is faster with AI. But the real shift isn’t speed, it’s the strategic reframing of what’s possible to target.

Take Recursion Pharmaceuticals, for example. This Utah-based company doesn’t just use AI to screen molecules; it has built a vertically integrated system that combines high-throughput biological experiments with machine learning models trained on over 23 petabytes of cellular imagery. What’s unique is that their models discover phenotypic effects, not just molecular matches, allowing them to repurpose drugs for entirely new indications. They're now running 5+ clinical programs internally, a rarity for AI-native pharma startups.

Compare this to older approaches where wet lab validation came late and often invalidated years of theoretical work. Recursion builds validation into the model.

Meanwhile, Genesis Therapeutics combines deep learning with quantum chemistry, solving problems that traditional software and humans can’t touch. In 2022, they signed a partnership with Eli Lilly worth up to $670 million, and this collaboration and funding underscore Genesis’s leadership in AI-driven drug discovery. 

2. Patient Engagement

Most “digital health engagement” tools are glorified reminder apps. But a few startups are quietly cracking what pharma companies never could and i.e., understanding patient behavior before it becomes non-compliant.

Biofourmis takes remote monitoring to the next level, integrating wearable data with AI-driven predictions. Instead of just tracking vitals, it anticipates adverse events. Their work with Novartis on heart failure patients showed hospital readmission could be cut by up to 30%. That’s cost-saving at the payer level, which is what pharma cares about.

Patient engagement is only effective when it generates clinical-grade data. Startups gaining traction in this space are building platforms that treat engagement as part of the data infrastructure, with a focus on measurable outcomes rather than just user experience.

3. Operational AI

Drug discovery is a lengthy process, but clinical trials remain the primary bottleneck in bringing new drugs to market. They eat up 60% of total development costs and often fail for reasons that have nothing to do with the drug itself, like slow recruitment or protocol design errors.

Unlearn.AI is solving this by creating “digital twins” of trial participants, statistical models of how a patient would have progressed on placebo. In 2023, their Alzheimer’s trial with Merck received regulatory approval to use synthetic controls in clinical trials. This advancement enables faster trials, smaller sample sizes, and reduced risk.

Another example is Phesi, an AI-driven data platform that analyzes data from over 132 million patient records to help design smarter clinical trials. By leveraging this extensive dataset, Phesi enables sponsors to predict failure risks at the trial design stage and significantly reduce costly protocol amendments-which can delay trials by over five months on average. While specific figures vary, Phesi’s approach has helped many organizations minimize amendments and streamline the clinical development process.

Operational AI isn’t glamorous, but it’s where the money is lost or saved. And startups that know how to sell efficiency to Big Pharma’s CFOs, not just impress its R&D heads, are the ones closing deals.

What Role Will AI and Digital Health Play in the Next Generation of Pharma Startups?

AI and digital health are becoming foundational to how pharma startups are conceived, built, and scaled. Rather than being used for isolated tasks, AI is increasingly integrated across the pharmaceutical value chain—from R&D and clinical development to regulatory planning and patient outcomes.

Digital health tools are also evolving beyond basic wellness apps or compliance trackers. They are forming the backbone of systems that collect real-world evidence, enable adaptive treatment planning, and improve access through decentralized care models.

Even with the rise of machine learning, the role of scientists and clinicians remains critical. AI can detect patterns and support predictive modeling, but human expertise is essential for framing research questions, interpreting complex biological data, and making clinical decisions. The most effective startups are those enhancing expert performance through AI-driven platforms, rather than aiming to replace professionals.

In this context, AI and digital health are helping to create systems that are more scientifically robust, clinically relevant, and operationally scalable. As pharma continues to grapple with inefficiencies and high failure rates, these shifts represent not just progress but necessary evolution.

Edited by Harshajit Sarmah