Adam
Artificial intelligence is often described as a technology that will reshape the future of medicine. Many people expect it to speed up drug development, improve patient safety, and help scientists make more accurate decisions with far less time and effort. In early phase clinical studies, which focus on how a new drug behaves in the body, AI appears to offer tremendous promise. Expectations are high because these studies involve large amounts of complex information that must be interpreted quickly to ensure safe and efficient progress. Yet the results seen in real world settings do not always match the optimism that surrounds the technology, and this has created a clear gap between what many believe AI can deliver and what it is actually achieving today. As Dinkar Sindhu, CEO of AXIS Clinicals, explains, “There’s no question AI has potential, but I’ve seen it oversold in clinical research. The safety of participants with novel drugs is absolutely paramount as the margin for error is razor thin.”
The strength of AI’s promise comes from its ability to process and analyze data at a scale that goes far beyond human capabilities. Early phase clinical studies generate detailed information about how a drug is absorbed, how it is metabolized, and how it affects organs and systems in the body. These studies also track side effects, dosing levels, and early signals of safety concerns. AI systems, at least in theory, should be well suited for identifying meaningful patterns within all this information. Supporters often emphasize that AI could shorten development timelines by helping researchers detect problems earlier, refine study designs, and choose more appropriate dosing strategies. In a field where time and accuracy directly influence patient safety and research costs, the potential advantages feel significant.
Despite these compelling benefits, the actual performance of AI tools in early phase research does not always meet expectations. One of the central obstacles is the limited size of early phase datasets. Since these studies typically involve small groups of healthy volunteers or patients, the amount of available data is relatively modest. AI models thrive on large, diverse datasets, so small study populations can restrict their ability to learn meaningful relationships. If the data used to train the model are incomplete or skewed, the predictions it produces can be unreliable. Researchers may then find themselves questioning whether an AI generated insight is genuinely valuable or simply the result of noise in the data.
Another issue involves the challenge of understanding how AI systems generate their conclusions. When an AI model identifies a trend or signals a potential risk, clinicians and regulators often require a clear explanation before they will trust the finding. Many AI systems function as black boxes that reveal little about how they arrive at a given outcome. This lack of transparency can slow adoption because decision makers must be confident that any conclusions will stand up to scrutiny and protect patient safety.
There are also practical concerns related to how AI fits into existing research processes. Integrating new tools into clinical operations requires specialized staff, updated infrastructure, and consistent data workflows. Many organizations do not yet have the technical foundation needed to use AI tools effectively, and even when they do, the transition can be slow. Early phase clinical research is tightly regulated, so any new method must demonstrate reliability and compliance before it can be widely accepted.
Closing the gap between promise and performance will require stronger data practices, more interpretable AI models, and closer collaboration between data scientists, clinicians, and regulatory authorities. Even with these challenges, AI remains a valuable addition to the early phase research landscape. It can support decision making, flag unexpected signals, and help guide study design, but it is not a miracle solution. The gap that exists today reflects the natural pace of scientific progress. As the technology matures, AI is likely to find a more stable and trusted role in early stage drug development.
