Closing the Gap Between Regulators and Innovators with Real-Time Risk–Benefit Assessment

In all honesty, the risk-benefit process for drug approvals has barely evolved in the last twenty years. The emergence of AI has created a huge opportunity for players to disrupt this market and look at ways to transform how new drugs are evaluated and approved. This new type of technology is finally equipping regulators and companies with tools to assess, in real time and from large, diverse data, how likely a drug is to succeed - and what risks may emerge. Here is why this capability isn’t just nice-to-have, it’s becoming essential.

The problem: Legacy gaps in how risk and benefit are assessed

A staggering 93% of drug candidates fail. They don't only fail, but more than half of them fail after an entire decade of investment and billions spent. Many of these failures could have been better predicted if more complete, dynamic evidence had been available earlier.
Decision-making in the benefit-risk process currently relies on siloed data (clinical trials, narrow patient populations), late-stage reviews, or static snapshots. Even after approval, monitoring is often weak—only a fraction of adverse effects are reliably captured, leading to critical uncertainties during clinical development.

Regulators are increasingly open to incorporating real-world evidence (RWE) and new frameworks, but they need trusted methodologies, reliable data pipelines, and transparent, auditable tools.
This is where the new class of AI-powered benefit-risk assessment tools that we are seeing emerge in the market, becomes critical.

The opportunity: AI-driven, real-time tools to bridge the divide

First of all, these AI-powered tools ensure data integration across silos. By unifying clinical trials, real-world data, historical safety reports, and patient registries, AI can surface signals earlier and more robustly than traditional methods allow.

Second, these new models offer predictive modeling and simulation that were nonexistent 5 years ago. Instead of waiting for late-stage trial data or post-market results, one can simulate risk–benefit trade-offs under different scenarios. These tools enable companies to prioritize drug candidates with a higher chance of regulatory success. 

Lastly AI tools allow for continuous assessment and transparency, which is typically the most costly aspect of drug development.

Regulators want frameworks that adapt—not fixed snapshots. They need tools that allow ongoing monitoring of safety, efficacy, and real-world performance to support regulatory decisions and better protect patients. They also help innovators communicate risk and benefit more clearly.

Regulatory momentum and what’s changing

The regulatory landscape is shifting in favor of innovation, because it’s now widely understood and accepted that tech, and more specifically AI, can close some of the widest gaps in medical research and patient care.

EMA’s DARWIN EU is one example: it aggregates patient-level clinical and real-world evidence across multiple European countries, increasing available data for regulatory decisions.
Additionally, new regulatory science platforms in Europe are being launched to develop better methodologies that align medical product development with patient needs and safety. Regulators are signaling willingness to accept new evidence frameworks, provided they are rigorous, transparent, and validated. Innovators must meet this standard.

What must innovators and regulators do to move forward

To close the gap, we need three things to be put in place. 

First, we need methodological rigor and auditability — AI tools must be transparent about data sources, model assumptions, and performance metrics, so regulators and clinicians can trust outputs.

Second, the industry requires early alignment and dialogue. Innovators should engage regulators early, co-defining the evidence needed to satisfy regulatory requirements. This reduces unforeseen outcomes and improves efficiency.

Lastly, adoption of common dynamic frameworks needs to be encouraged or even imposed. Regulators should continue reforms that allow for augmented trial designs, adaptive trial protocols, and continuous monitoring, which will facilitate faster yet safe approvals.

We are at a turning point. The cost, risk, and delays in bringing new drugs to market are no longer bearable—not for patients, not for healthcare systems, and not for innovators. AI-powered, real-time benefit–risk assessment isn’t an optional tool ^— it is rapidly becoming fundamental to safe and relevant innovation.

Closing the gap between innovators and regulators means building tools and processes that deliver evidence when it matters most. Doing so doesn’t just accelerate approvals: it builds trust, improves public health, and ensures that breakthroughs reach patients sooner and more safely.