Developing new medical treatments is a long and costly journey, with many potential drugs failing to make it past the initial phases of clinical trials. What if we could predict a drug's success earlier? Researchers have developed CTP-LLM, an AI model that analyzes clinical trial protocols—detailed documents describing a trial’s plan—to forecast its likelihood of progressing to the next phase. Using a vast dataset of over 20,000 trials, the AI learned to identify patterns in protocol language that correlate with success or failure. Interestingly, the research found that participant selection criteria and the clarity of the study description are stronger predictors than the drug itself. This suggests that how a trial is designed is just as important as what is being tested. The CTP-LLM achieved 67% accuracy in predicting trial phase transitions overall and an impressive 75% accuracy in predicting whether a Phase III trial would lead to final approval. This technology has the potential to revolutionize drug development, helping researchers design better trials and potentially saving billions in wasted resources. By identifying potential pitfalls early on, CTP-LLM could accelerate the delivery of life-saving treatments to patients. While promising, the model is limited by the quality and availability of data. Future research will focus on refining its ability to explain the reasons behind its predictions and better handle rare scenarios, but the potential for AI to transform clinical trials is clear.