The traditional transactional model where AI companies purchase pharma data is ending. To resolve the stalemate where AI needs trial data and trials need validated AI, the industry is shifting to strategic partnerships. In these collaborations, both parties jointly generate evidence during trials, accelerating development and providing mutual benefit.
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.
A common mistake in pharma is viewing imaging data solely for its diagnostic value—identifying where a disease is. The greater, untapped potential lies in its predictive value. When made computable, imaging data contains signals about how a disease behaves and will respond to specific treatments, making it a powerful predictive asset in oncology.
AI is no longer a 'nice-to-have' for radioligand therapies. It is essential for precise patient selection and response monitoring. Without AI-driven confidence in outcomes, the commercial viability, adoption, and reimbursement for these expensive new treatments are at risk, making it a prerequisite for commercial success.
Current patient selection for radioligand therapies often relies on a simple binary 'positive' or 'negative' assessment, which fails to predict response 55% of the time. AI moves beyond this by providing quantitative, whole-body tumor assessments to predict who will actually respond to treatment, a far more critical factor for clinical and commercial success.