While crucial, the slow, administrative, and sometimes political process of defining "responsible AI" is becoming a deterrent for pharma companies. Aditya Gherola argues that regulators must move faster to provide clear guidelines, preventing the concept from becoming a roadblock to critical innovation in drug discovery.

Drug developers often operate under a hyper-conservative perception of FDA requirements, avoiding novel approaches even when regulators might encourage them. This anticipatory compliance, driven by risk aversion, becomes a greater constraint than the regulations themselves, slowing down innovation and increasing costs.

AI delivers the most value when applied to mature, well-understood processes, not chaotic ones. Pharma's MLR (Medical, Legal, Regulatory) review is a prime candidate for AI disruption precisely because its established, structured nature provides the necessary guardrails and historical data for AI to be effective.

The FDA is abandoning rigid, fixed-length clinical trials for a "continuous" model. Using AI and Bayesian statistics, regulators can monitor data in real-time and approve a drug the moment efficacy is proven, rather than waiting for an arbitrary end date, accelerating access for patients.

The pharmaceutical industry risks repeating Kodak's failure of inventing but ignoring a disruptive technology. For Kodak, it was digital photography; for pharma, it's AI. The industry possesses vast amounts of data (the new 'film'), but the real danger lies in failing to embrace the AI-driven intelligence layer that can interpret and act on it.

While AI is on the verge of cracking preclinical challenges, the biggest problem is the high drug failure rate in human trials. The next wave of innovation will use AI to design molecules for properties that predict human efficacy, addressing the fundamental reason drugs fail late-stage.

MedTech AI companies can speed up regulatory approval by building a trusted, real-time post-market surveillance system. This shifts the burden of proof from pre-market studies to continuous real-world evidence, giving regulators the confidence to approve innovations faster, turning them from blockers into partners.

An FDA-style regulatory model would force AI companies to make a quantitative safety case for their models before deployment. This shifts the burden of proof from regulators to creators, creating powerful financial incentives for labs to invest heavily in safety research, much like pharmaceutical companies invest in clinical trials.

AI tools can be rapidly deployed in areas like regulatory submissions and medical affairs because they augment human work on documents using public data, avoiding the need for massive IT infrastructure projects like data lakes.