Lack of Data Connectivity

According to Phesi’s analysis, fewer than one in three clinical trial protocols are documented and linked to patient data and outcomes. Traditional data management systems are fragmented, and real‑time recording at the trial site is insufficient, creating this gap. The data gap weakens the foundation for AI training and increases the risk of generating misleading insights.

AI Proliferation Exacerbates the Problem

AI models are only useful when the input data are accurate; training on incomplete protocol data amplifies errors. Consequently, the likelihood of incorrect decisions during candidate selection or trial design rises, potentially extending development timelines and increasing costs.

Investment and Market Implications

Companies that do not invest in data‑quality improvement will see limited AI utility and heightened loss risk from poor decision‑making. Demand is expected to grow for firms that provide data‑integration platforms or electronic health‑record linkage solutions, such as Phesi. Investors should focus on biopharma companies with strong data‑infrastructure capabilities.

Regulatory and Transparency Demands

Regulators are placing increasing emphasis on the source and reliability of clinical data. If AI‑derived analytical results are not transparently validated, they are likely to trigger additional review during the approval process. Therefore, companies must strengthen data governance and explainable AI practices.