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.

Contrary to fears of job replacement, AI's primary purpose in clinical trials is to automate low-value work like coordination and documentation. This frees up experts like CRAs and data reviewers to focus on high-value activities such as interpreting signals, managing risks, and making critical decisions, thereby amplifying their expertise.

Rapid trial generation is counterproductive if accelerated drafts lack scientific rigor and regulatory precision. Teams adopting clinical AI must strike an optimal balance between reducing generation timelines and maintaining the strict quality required to pass regulatory scrutiny.

The FDA has adopted AI systems like ELSA and Halo to review submissions. To achieve a successful review, companies must now match the FDA's capabilities by using their own AI tools, assisted by human experts, to prepare documentation, effectively leveling the playing field.

To overcome resistance to AI in critical fields like healthcare, position it first as a supplement, not a replacement. By providing AI-generated summaries that still require clinical review, organizations can demonstrate value and build trust, making clinicians see AI as a tool that frees them for high-value work.

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.

Generative AI is not viewed as a standalone solution for drug discovery. Alloy's perspective is that its primary value is in enhancing and automating existing workflows. The model requires a 'lab in the loop' and 'human in the loop,' where AI assists scientists by making them more efficient and improving data analysis, rather than replacing the core wet lab process.

While the FDA is adopting AI, it does not accept AI-generated outputs without human validation. Nader Fahy cites an FDA rejection of a company's submission that relied solely on an AI's assessment. This precedent underscores that for regulatory compliance, a "human in the loop" remains a non-negotiable requirement.

For high-stakes decisions like utilization management, validate an AI model by having it run alongside the existing human process. The AI renders a decision in parallel with the medical director, allowing the organization to confirm alignment and build confidence before “shifting left” to autonomous workflows.