For high-stakes decisions like halting a clinical trial, current AI models lack the reproducibility and explainability demanded by regulators. The 'Brakes' platform deliberately avoids AI in its core decision engine, applying it instead to adjacent problems like patient subgroup analysis where the stakes for error are different.

To gain trust from medical and regulatory teams, AI companies must move beyond being 'tech demos.' The key is to build solutions as medical products with transparent validation, reproducible results, and deep integration into existing clinical workflows. Trust is earned through reliability over time, not just peak performance on a single dataset.

Life sciences AI vendors can satisfy regulatory demands for explainability without exposing proprietary model code. By providing empirical rationale—such as matching past cohort sizes from similar indications—platforms ground recommendations while protecting their intellectual property.

When buying AI solutions, demand transparency from vendors about the specific models and prompts they use. Mollick argues that 'we use a prompt' is not a defensible 'secret sauce' and that this transparency is crucial for auditing results and ensuring you aren't paying for outdated or flawed technology.

To manage compliance risk in regulated industries, treat AI agents like new employees. Before deployment, the agent must pass the same knowledge assessment a human would take. This quantifies the risk, turning a 'black box' AI into an observable and testable system with a verifiable accuracy score.

In a skeptical, regulated industry, simply predicting an outcome is insufficient. Causal AI is non-negotiable because it provides a 'glass box' explanation for its recommendations. It connects outputs to data points and biological reasoning, satisfying the critical 'why' questions from both sponsors and regulators.

Many AI vendors, focused on novel algorithms, fail to control for basic statistical principles like Type 1 error (false positives)—a strict requirement from the FDA. 'Cool AI' is useless and dangerous in drug development if it isn't validated by the 'boring' but essential foundations of statistics.

Internal scenario testing allows for some statistical uncertainty to narrow broad portfolio risks, but regulatory submissions demand much higher rigor. Generating synthetic control arms or digital twins requires close collaboration with biostatisticians and epidemiologists to statistically mirror real patient distributions.

For AI systems to be adopted in scientific labs, they must be interpretable. Researchers need to understand the 'why' behind an AI's experimental plan to validate and trust the process, making interpretability a more critical feature than raw predictive power.