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The Four Shifts That Will Define the Next Era of Drug Development

Francisco Beca, MD, PhD, and Chief Medical Officer of QuantHealth — Tue, 01 Sep 2026 15:00:00 GMT

For decades, drug development has become increasingly sophisticated. We generate more data, recruit more patients, measure more biomarkers, and apply more advanced science than ever before. Yet nearly 90% of therapies entering clinical development still fail.

Artificial intelligence (AI) has the potential to change that, but not because it automates work. The greatest opportunity for AI is helping scientists, clinicians, and business leaders make better decisions throughout the development lifecycle.

Across conversations with pharmaceutical executives, AI leaders, regulators, and researchers, four shifts are beginning to reshape how medicines will be developed over the next decade.

1. From Automation to Reinvention
Most organizations are using AI to automate yesterday's workflows. That's an important first step—but it isn't transformation. The greatest opportunity is fundamentally rethinking how drug development works.

Today's clinical development model was built for an era of limited data, linear experimentation, and sequential decision-making. AI allows us to challenge those assumptions. Instead of waiting years to validate a hypothesis, researchers can now model, simulate, and refine decisions before a clinical trial even begins, reducing uncertainty before the first patient is enrolled.

This shift is about far more than efficiency. Every better decision made upstream has the potential to prevent costly late-stage failures, accelerate promising therapies, and ultimately improve outcomes for patients waiting for new treatment options.

Organizations that use AI to optimize existing workflows will become more efficient. Organizations that use it to reinvent the way medicines are developed will define the future of drug development.

2. From Predictions to Continuous Decision Intelligence
For decades, drug development has been built around a series of isolated decisions -- will this trial succeed, endpoint be met, patient population respond? Each decision was made with limited information and then locked in place for months, or even years, until new data became available. Then, maybe a decade later, we found out if that decision was right or wrong.

But diseases don't progress in snapshots, and neither should the decisions that shape how we develop medicines.

AI is making it possible to move beyond one-time predictions toward continuous decision intelligence. By integrating clinical, biological, operational, and real-world data, organizations can make decisions at the start, backed by evidence, and then continually refine their understanding of risk, probability, and opportunity as evidence evolves.

Instead of making a handful of high-stakes decisions throughout development, teams can make smarter decisions at the start – before a trial begins – and then continuously during -- adapting protocols, refining patient populations, and improving trial strategies before uncertainty becomes costly.

The future isn't simply better predictions. It's a fundamentally better way of making decisions.

3. From Humans in the Loop to Experts in the Loop
Much of the conversation around AI has centered on keeping "humans in the loop." That's an important safeguard -- but in drug development, it's not enough.

First, until recently, I went along with the industry and used the term "humans in the loop." However, over recent months, I've come to realize that this term undersells the expertise of the clinical development teams within biopharma. At QuantHealth, we believe the more accurate description is "experts in the loop" – a cycle in which experts train, test, and validate the technology; in which in-house experts make decisions and act as authorities in their field; and in which, collectively, everyone touching biopharma – partners and in-house teams alike – becomes smarter and more experienced.

This is an important nuance because drug development has never been the work of a single expert. Every major decision draws on the collective expertise of clinicians, statisticians, biologists, regulatory leaders, data scientists, commercial teams and now, AI partners. The challenge isn't replacing human judgment—it's bringing these perspectives together quickly enough to make the best possible decisions.

This is where AI creates its greatest value. Not by replacing experts, but by connecting them. AI can synthesize vast amounts of scientific, clinical, operational, and real-world data, giving multidisciplinary teams a shared understanding of risk, opportunity, and evidence before critical decisions are made.

The organizations that lead the next era of drug development won't remove humans from decision-making. They'll create environments where AI accelerates collaboration, while experts provide the judgment, context, and accountability that no model can replace.

The future isn't humans versus machines. It's experts and AI working together to make decisions that neither could make alone.

4. From Linear Development to Continuous Optimization
For decades, clinical development has followed a familiar sequence: Design, Execute, Analyze, and Learn. That model made sense when evidence could only be generated through long, sequential clinical studies. But waiting years to discover whether a critical decision was the right one is becoming increasingly difficult to justify.

Advances in simulation, predictive modeling, real-world data, and adaptive AI are creating the possibility of a fundamentally different approach: one where development becomes a continuous cycle of prediction, validation, and refinement.

Instead of discovering problems after hundreds of millions of dollars have been invested, organizations can identify risk earlier, refine trial strategies before execution, and redirect resources toward the programs most likely to succeed. This is important because it creates a future when:

Every prediction becomes another source of evidence.
Every study strengthens the next decision.
And every decision improves the development process itself.

The future of drug development won't be defined by faster execution: it will be defined by continuous optimization.

The Next Generation of Drug Development
These four shifts point toward something larger than AI: they represent a new operating model for pharmaceutical R&D.

The organizations that lead the next decade won't simply generate more data or deploy more AI, they will build cultures capable of making better decisions faster. That means moving beyond automation, isolated AI tools, and incremental improvement to being those that combine predictive intelligence, scientific expertise, and continuous learning to fundamentally rethink how medicines are developed.

That's not simply the next chapter of AI -- it's the next chapter of drug development.

A New Chapter for QuantHealth & BioPharma: Our Series B Is Ushering-in Simulation-First Clinical Development

Orr Inbar, CEO of QuantHealth — Tue, 04 Aug 2026 13:04:59 GMT

-- Orr Inbar, CEO of QuantHealth

Today I get to share something I'm excited about: QuantHealth has raised $45M in Series B funding, led by Qumra Capital and joined by a group of investors who believe, as we do, that drug development doesn't have to be this slow, expensive, or uncertain.

Here's the problem Arnon and I have spent years obsessing over. It takes roughly 10 years to bring a drug to market. Each costs an estimated $3 billion to develop, and about 92% of them fail in clinical trials. Behind each of those failures is a patient who's still waiting for life-changing treatments.

We started QuantHealth because we knew there was a better way – one where biopharma could simulate a trial before it ran, and catch the problems and opportunities on a computer instead of in a patient. This round is about one thing: helping us reach our mission of getting drugs to patients faster, with a lot more confidence along the way.

Why This Moment
A few years ago, "simulation-first clinical development" wasn’t even a topic for discussion. Now it's becoming the standard for the industry thinks. Leaders want to see the evidence before they bet on something new, and I don't blame them- huge amounts of capital and even careers are on the line. But the evidence is starting to speak for itself, and that shift is exactly what makes this such a good time to be doubling down.

I think about where this goes next: a future where in just 5 years, AI will help the industry get 50% more drugs approved per year. That's the scale of the bet we're making, and this funding is what lets us make it.

Why Now
Honestly, the numbers are why. Over the past year, we:

Doubled our clinical trial simulations to more than 600 trials — more than anyone else in the category
Hit up to 90% accuracy across nearly 30 therapeutic areas
Doubled the team with significant expansion into the US

We're also now working with 12 of the top 20 pharma companies globally

None of that is an accident. It's what happens when better predictions -- of who will respond to a therapy, of how patients actually behave in real-world trial settings -- start to compound. Better predictions mean more confidence, less rework, and less risk for everyone involved, especially the patients on the other end of these decisions.

What's Next
Since our Series A, the shift for us has been practical: going from simulating one protocol at a time to exploring thousands of protocol options in the time it used to take to test one. With this new capital, we're investing in:

Core modeling and infrastructure, including our foundation model, our modeling capabilities, and the data that underpins both
Broader product deployment so more pharma teams can put simulation to work in their everyday decisions, not just their biggest bets
An end-to-end clinical development platform that puts simulation directly in the hands of researchers, scientists, operations leaders, and commercial strategists
Our team, we're hiring across commercial, scientific, and medical roles, on both the go-to-market and research-and-delivery sides of the business

If the last few years were about proving this works, the next few are about making it the default way biopharma builds.

Why It's Possible
None of this happens without the people who believed in this before the results made it easy to.

Thank you to Qumra Capital for leading this round, and to our new and existing investors for backing us further. Thank you to our employees, who've put in the work through some genuinely hard years -- and to our mentors and board, whose guidance has shaped nearly every decision along the way.

I'm proud of what this team has built, and even more excited about what's next.

QuantHealth Raises $45 Million Series B, led by Qumra Capital, to Accelerate Simulation-First Clinical Trial Development

heather@quanthealth.ai (QuantHealth) — Tue, 04 Aug 2026 13:00:00 GMT

Funding will advance QuantHealth’s R&D, product innovation, and commercial expansion following 8x sales growth in 2025

Why Competitive Positioning Must Start Before Clinical Development Ends

Agya Garg, Head of Product — Mon, 27 Jul 2026 13:00:00 GMT

For decades, pharmaceutical companies have treated clinical development and commercial strategy as two separate disciplines. One team focuses on proving efficacy and safety. Another, often years later, determines how to position the product in the market.

However, in today's environment, the most successful therapies are not simply the ones that generate positive clinical data. They are the ones that demonstrate meaningful differentiation, target the right patient populations, and enter the market with a clear understanding of future competitive dynamics. The challenge is that these decisions are often made too late.

By the time many organizations begin evaluating market positioning, key development decisions have already been locked in: trial designs are finalized, endpoints have been selected, patient populations have been defined, and millions of dollars have already been invested.

At QuantHealth, we believe competitive positioning should begin before the first patient is enrolled.

The Cost of Waiting
The pharmaceutical industry has become exceptionally good at generating data. Yet commercialization challenges remain widespread. A few stats to consider:

One in three drugs misses its launch forecast.
Fifty-seven percent of drug launch failures are attributed to poor understanding of market and customer needs.
More than 40% of failures stem from insufficient differentiation.

These statistics point to a common problem: organizations are often making critical development decisions without a clear understanding of how those choices will influence future market success.

Clinical trial success does not automatically translate into commercial success.

The question every development team should be asking is not simply, "Will this trial succeed?" but rather, "Will this asset win in the market if it succeeds?"

Bridging Clinical and Commercial Strategy
The industry has long lacked a way to answer that question quantitatively – and before a product launches.

Competitive intelligence teams can build landscapes. Market access teams can develop forecasts. Commercial teams can evaluate positioning options. But historically, these efforts have relied heavily on retrospective analysis and expert opinion.

What has been missing is a predictive approach.

That's why we developed our predictive, competitive positioning capabilities, which quantify a drug's future market potential and de-risk commercial decisions while assets are still in development.

By integrating clinical and commercial intelligence into the R&D process, organizations can make more informed decisions across portfolio management, trial design, evidence generation, market strategy, and investment prioritization long before launch planning begins.

From Predicting Clinical Outcomes to Predicting Market Success
The foundation of predictive, competitive positioning is our industry-leading clinical trial foundation model. It was trained on more than 100 million patient records and validated across more than 600 completed clinical trials, the platform forecasts trial-level and patient-subpopulation outcomes for both investigational and marketed therapies.

Predicting outcomes, however, is only the first step. The real value emerges when those clinical predictions are combined with competitive intelligence, market dynamics, and deep-data trained with industry experience. It enables teams to answer strategic questions that were previously impossible to answer, much less quantify with a level of confidence, in advance of launch:

Which patient populations offer the greatest opportunity for differentiation?
How are competitors likely to perform in future clinical trials?
Which development paths maximize future market share?

Instead of reacting to the future, QuantHealth has introduced an era where organizations don’t have to hope for success, they can predict it.

Trust Is the Product: Why Quantifying Confidence Is AI's Biggest Competitive Advantage

Nadav Weinberg, Head of Sales — Wed, 22 Jul 2026 17:06:24 GMT

After hundreds of conversations with pharmaceutical leaders over the past few years, one thing has become clear: the industry no longer needs to be convinced that AI has potential. The harder challenge is knowing when AI can be trusted enough to change impactful decisions, and that distinction matters.

In drug development, trust is not built through novelty or ambition. It is built through evidence, validation, and confidence in decisions that carry enormous consequences for patients, researchers, timelines, and investment.

We Aren't Selling AI. We're Selling Trust.
Drug development is one of the highest-stakes decision-making environments in the world: a protocol change can affect thousands of patients, a portfolio decision can redirect hundreds of millions of dollars, and a single clinical trial may determine the future of an entire therapeutic program. AI adoption for life sciences has moved from its first phase: Innovation, to its second: Impact.

Life sciences organizations adopt AI because they need greater confidence in the decisions and workflows they already make. Our customers are not looking to replace scientific judgment – they are looking to reduce uncertainty, evaluate options earlier, and make better-informed decisions before capital is committed and patients are enrolled.

Trust Isn't Built by Making Bigger Claims
One of the biggest misconceptions in AI is that trust comes from claiming higher accuracy. Trust comes from helping people understand the evidence behind a recommendation and the confidence they should place in it.

In clinical development, a prediction is only valuable if teams understand its context, limitations, assumptions, and potential impact. Leaders need visibility into the variables influencing an outcome, the scenarios that were evaluated, and the uncertainty associated with each path.

Applying that need to our approach at QuantHealth, we focus on transparent alignment with our Knowledge Graph and the RWE data we utilize, its endpoints, potential proxies, and outcomes. Once there’s trust in the inputs of the model, there’s higher conviction in the model’s outputs, and therefore confidence in applying it to impactful decisions.

Confidence Can Finally Be Quantified
Predictive simulation is now changing the conversation.

Historically, confidence in drug development decisions has been built through experience, historical benchmarks, expert opinion, and intuition. Those inputs remain essential, but are also difficult to measure.

Predictive simulation introduces something new. It allows organizations to evaluate multiple development strategies before enrolling the first patient, estimate the probability of different outcomes, compare alternative scenarios, and understand uncertainty across each path.

Confidence is no longer just something a team feels.

It becomes something they can quantify and ultimately utilize to drive decision-making.

Quantified Confidence Creates Trust
People often say trust must be earned: I agree. However, the way trust is earned is evolving.

When confidence can be measured, validated, and continuously improved, trust becomes much easier to build. Teams spend less time debating assumptions and more time evaluating evidence. But when the world is moving quickly and the average time for peer-reviewed evidence generation is years, how do you establish trust – especially when, for many, simulation results are the ‘secret sauce’ to their success.

This is where accelerated engagements come into play. At QuantHealth, we know that the ‘proof is in the pudding’ and have seen through multiple engagements that, once a company sees the power of our predictions, it leads to broader adoption.

Because with those insights in hand, leadership gains greater visibility into risk. Cross-functional groups align around shared data rather than competing interpretations. Then the conversation shifts from whether an organization trusts AI to how much confidence they need for their next big decision.

The Future Belongs to Organizations That Can Trust Their Decisions
Eventually, most pharmaceutical companies will have access to increasingly capable AI so technology alone will not be the differentiator. The organizations that lead will be the ones that consistently make higher-confidence decisions, with their certainty quantified.

This is why, in commercial conversations, people often ask what I sell.

The answer is simple: I sell trust.

Trust in people, technology, and decisions made for the benefit of patients, resources, and science.

The Change Worth Getting Out of Bed For: Why Decision Intelligence Is the Next Transformation in Drug Development

Sukant Mittal, SVP of Corporate Strategy at QuantHealth — Sat, 11 Jul 2026 14:52:21 GMT

In drug R&D, excitement for generative AI has increased the adoption of technology at an unprecedented rate. In fact, over the past 18 months, budgets for early adopters have expanded, and most life sciences leaders plan additional increases this year — nearly half intend to raise AI spend by more than 10 percent.¹ At the same time, infrastructure players, frontier model companies, and smaller application-focused companies are extending their footprints across the R&D value chain. The focus has also moved from chats to multi-orchestrated agentic solutions to harnesses around agentic frameworks, and the industry has shifted its expectations for AI from efficiency alone to how solutions deliver ROI in complex use cases with humans-in-the-loop.

In a high-velocity environment, accelerating adoption requires three things: proving the technology and delivering value; mobilizing the ecosystem; and getting comfortable with human-in-the-AI-loop in place of AI-in-the-human-loop.

Proof of Technology and Delivering Value: A senior executive from a top 10 pharma put it plainly at a recent QuantHealth industry summit: "It's not worth getting out of bed for us unless we're talking about an impact worth at least $100 million." That level of value lives in the hardest problems, specifically drug development, where investments run high (~ billion dollars), approval timelines are long (~ decade), and the probability of success is low (~10 percent).

The industry has long known that changing the trajectory of drug development — from its high costs, timelines, and patient impact — starts with changing which clinical trials are ultimately pursued. Being able to quantify the potential of a drug for smarter decision-making and confidence, though, was largely a theoretical exercise.

But this change is finally upon us. Deep, model-driven outcome predictions and decision intelligence available months or years before trials begin, along with the ability to pinpoint areas of improvement — such as selecting endpoints and subgroups — while iteratively collaborating with experts from various disciplines, can meaningfully help improve the odds of a drug succeeding and have a real impact for patients invest time and effort into trials.

An important note: the economic value is clear if the drug succeeds, but the "fast-fail" approach reduces the opportunity cost, cutting spend on an asset more likely to fail and making space in the same infrastructure for the ones that might have a better probability to succeed (economic value compounds fast within the lifespan of an asset, and even faster on a portfolio level). This computation, resulting from fit-for-purpose, deep computational models augmented by LLMs, can help guide a decision to retire a drug asset earlier and clear a path to multi-million-dollar savings without strain.

While the examples in drug development of deep frontier technology and models integration are rare, in research recent examples provide guidance: CHAI Discovery’s CHAI-2 genuinely hard problem to designing antibodies for 52 targets with no known binders and confirmed hits in < 2 weeks³ and Ismorphic’s collaboration with Lilly and Novartis⁴, structured as relatively modest upfront milestones (compared to the overall potential payout) that pay only as frontier tech proves itself are good benchmarks: Earn conviction on a hard problem first by proving the value of your technology, then let the commercial terms follow the evidence.

Mobilizing the Ecosystem: No company alone can prove a new way of working; the surrounding system has to move with it. This is exactly what’s happening in drug development now.

- - Government as the legitimizer: Regulators have moved from tolerating computational evidence to inviting it, and the binding constraint has shifted downstream — from building a model to establishing that its output can be trusted, through the validation, provenance, and context that can turn a prediction into evidence. Several initiatives now formalize that credibility layer: MIDD Paired Meeting Program,⁵ ICH M15 harmonized framework and terminology for how model-derived evidence is submitted and judged, ⁶ AI credibility guidance is the FDA’s risk-based framework for AI used to support decisions on drug safety, efficacy, and quality,⁷ and Operation Trialblazer is an HHS-wide, expedited-IND effort with QSP-based first-in-human dose guidance and updated master-protocol guidance.⁸
  - Infrastructure as the foundation: The compute to train and run models over genomic, clinical, and real-world data sits with a few providers, now partnering downward into a stack of raw compute, foundation models, and applications. NVIDIA’s BioNeMo, running on AWS, puts GPU-scale training within reach of companies that could never build it alone.⁹
  - Intelligence as the distribution: The frontier model companies are moving from horizontal tools towards specialization. Anthropic’s Claude Science, a workbench that connects models to the tools researchers already run — PubMed, Jupyter, and open biomolecular models such as Boltz-2 — launched alongside an in-house program to pursue drugs for neglected diseases.¹⁰ Specialist connectors extend that reach. This intelligence-layer-as-distribution is the ultimate mobilizer of the ecosystem.

Human-in-the-AI Loop vs. AI-in-the-Human-Loop: The third source of trust takes the longest to build because it is about us. Drug development is multidisciplinary, and therefore a useful technology system has to speak medicine, science, regulation, and engineering, and interface with multiple stakeholders who each hold a piece of the decision.

Only a handful of companies working alongside pharma have assembled solutions that reflect the needs of these diverse groups, and even fewer have the same range of talent as their core DNA. That presence is what moves an organization from AI-in-the-human-loop, where the expert does the work and the model assists at the margins, toward human-in-the-AI-loop, where a frontier model carries the first pass and the expert becomes its reviewer and approver. Professionals still prefer a human to AI for complex, high-stakes work by roughly nine to one¹¹ and where a wrong call costs hundreds of millions of dollars, that caution is well placed. Closing the gap is slow work: staying beside the teams as the technology advances, keeping AI in the human's line of sight, and letting comfort accrue one decision at a time.

These necessities — proof, ecosystem, and people — are also changing what is bought and sold. When a system can carry a workflow to a validated result, value moves from accessing a software towards outcome — and commercial terms move toward the milestone-based, risk-sharing structures now forming across AI-driven R&D.¹² This is why QuantHealth has built its product portfolio with a combination of deep foundational models trained by scientific, medical, and biology experts, with recommendations provided with further expertise – or, human-in-the-AI loop.

The companies that earn trust on all three fronts — proving value where the stakes are highest — will be the ones that win and inspire us to get out of bed in service of bringing life-changing medications to patients – ones participating in clinical trials and the ones awaiting a successful outcome.

AI is a Decision Intelligence Tool, not an Administrative Assistant

Francisco Beca, MD, PhD, and Chief Medical Officer of QuantHealth — Wed, 01 Jul 2026 04:01:00 GMT

As pharmaceutical companies race to adopt artificial intelligence (AI) to speed up drug development and discovery, the biggest opportunity is not speed, but better clinical and development decisions -- the kind that make late-phase failure rare rather than just faster.

Clinicians, researchers, business leaders – we’ve all become obsessed with AI automation.

Open any industry journal or attend any healthcare conference today, and you will find an overwhelming focus on productivity. The conversation surrounding AI in pharma is dominated by a single question: How can AI do what humans already do, but faster?

But this is short-sighted. There are two kinds of AI.

The first is AI that automates existing work; activities like writing emails, providing rote chatbot answers, auto-dialing clinical trial enrollees, and matching patient claims.
The second kind of AI is AI that expands human decision-making. It’s capable of predicting trial outcomes, identifying patient populations, optimizing protocols, recommending interventions, and even personalizing treatment.

The latter is what excites me as a clinician, researcher, and technologist.

The real opportunity is decision intelligence
The fundamental challenge of modern drug development isn’t a lack of information; it’s an abundance of it. Despite decades of advances in biology, computation, and trial design, fewer than one in ten compounds that enter Phase 1 reach approval. Much of that attrition occurs in later, larger trials due to inadequate efficacy, when the trials are most costly and enroll the most patients. Every day, clinical development teams are buried under an avalanche of disparate data streams:

Multicenter clinical trial data
Real-world evidence (RWE)
Continuous patient-reported outcomes (PROs)
Complex biomarkers and deep genomics
Market and epidemiological trends
Patient-generated information

No human mind, nor any isolated team of experts, can realistically synthesize these billions of data points in real time to see the hidden correlations. The challenge of the modern CEO and Chief Medical Officer is making sense of data.

This is where the paradigm shifts from automation to intelligence. The most valuable AI isn't the AI that replaces human effort; it’s the AI that expands human decision-making.

It is the tool that helps clinical leaders predict trial outcomes, identify highly specific patient sub-populations, optimize complex protocols before a single patient is randomized, and recommend adaptive interventions.

The most valuable AI helps us make high-stakes choices we simply couldn't make on our own. Recent late-phase oncology readouts have shown how a strong signal in defined sub-populations can be obscured at the aggregate level. Every failed late-phase trial exposes hundreds or thousands of participants to a therapy that did not work, and a meaningful fraction of those failures were foreseeable from what was already known when the trial was designed.

Why static development no longer works
Historically, clinical development in biopharma organizations has operated as a rigid, sequential assembly line: Design. Recruit. Execute. Analyze.

We locked a protocol into place, spent years and millions of dollars running the trial, and crossed our fingers until the end of the study to finally understand what happened. If the hypothesis was flawed, we only find out after the capital was spent. Clinical trials already apply the logic of futility through interim analyses that halt a study once the data show it is unlikely to succeed, sparing later participants a futile exposure. That same logic should be applied earlier -- before the first patient is enrolled. Just as launching a pivotal trial without a prospective power calculation is now indefensible, initiating one without a quantified, calibrated estimate of its probability of technical success should, in time, become difficult to justify.

This static approach is becoming obsolete. By leveraging advanced AI, there is the possibility of continuous learning throughout the development lifecycle.

Instead of waiting for a retrospective post-mortem, AI acts as a live navigational system, allowing innovators to see patterns as they emerge and dynamically adapt. It shifts the industry from a culture of "execute and pray" to one of continuous, data-driven optimization.

Real-world data closes the loop
The shift toward continuous learning fundamentally alters the traditional lifecycle of a drug.

Previously, a thick wall existed between clinical development and commercialization. Once a drug was approved and entered the market, the development team’s job was largely done.

But AI and real-world data (RWD) completely dissolve that wall. Once a therapy enters the market, real-world patient outcomes, side-effect profiles, and compliance data become an active, ongoing source of intelligence. This creates a powerful feedback loop.

The insights gained from actual patients sitting in doctors' offices flow directly back into the lab, drastically improving future study designs, refining patient selection criteria, and sharpening overall treatment strategies. Development no longer ends at approval; it evolves – but so do expectations.

Precision medicine becomes practical
Precision medicine has been a buzzword for years — a noble goal that so far proves incredibly difficult to scale. But true precision medicine doesn't mean manufacturing millions of bespoke, hyper-individualized drugs for single patients. It means achieving a much better, highly sophisticated match between the patients who exist and the therapies that already work.

Think of how advanced recommendation engines like Google or TikTok seamlessly personalize content for billions of users by understanding their subtle preferences and behaviors.

In a similar, but far more rigorous way, AI can synthesize a patient's genetic, clinical, and lifestyle profile to help physicians personalize treatment decisions.

This is an achievement that neither a human nor an AI algorithm can accomplish in isolation. It requires the raw computing power of AI to synthesize the complexity, paired with the empathy, ethics, and clinical judgment of an expert decision-maker.

Shifting the conversation
If we evaluate AI solely by how much time or even money it saves, we miss the bigger picture. Efficiency is a commendable goal, but better outcomes for patients are the ultimate prize. Making clinical failure rare is a benefit that compounds across the entire ecosystem: sponsors recover capital that today is written off in late-phase attrition; regulators spend their finite review capacity on assets with a credible chance of approval; investigators and sites are spared the demoralizing work of running trials that were unlikely to succeed; payers and health systems avoid absorbing the downstream cost of therapies that never reach approval; and most importantly, trial participants and the patients waiting behind them are no longer exposed to experimental therapies whose failure was foreseeable. AI's greatest contribution to lowering the total cost of development won't come from making discovery cheaper, it will come from making clinical failure rare.

As leaders in biotechnology and pharmaceuticals, our mission shouldn't just be to build a faster version of the system we already have. Our mission must be to build a smarter one. By moving away from simple automation and leaning heavily into decision intelligence, we won't just bring drugs to market faster—we will bring the right drugs to the right patients with unprecedented precision – that’s best for patients, for researchers, for clinicians, and for the sponsors committed to doing so.

How AI Will Impact The Development of New Medicines

heather@quanthealth.ai (QuantHealth) — Mon, 29 Jun 2026 23:37:47 GMT

Drug Development Has a Learning-Speed Problem

Flavio Dormont, PhD and VP of Scientific Strategy, QuantHealth — Mon, 22 Jun 2026 04:00:00 GMT

QuantHealth Expands Executive Team with World-Class Medical and Innovation Leaders

heather@quanthealth.ai (QuantHealth) — Wed, 17 Jun 2026 23:05:51 GMT

Industry veterans from leading pharma and AI organizations will help build the predictive infrastructure that will power the future of clinical development