Brown University researchers have introduced a physics-informed artificial intelligence framework capable of predicting drug-release rates in controlled-delivery systems, a breakthrough that could substantially reduce development timelines and costs for medical patches, bandages, implants, and oral medications. Published in the Journal of Drug Delivery Science and Technology in 2026, the study demonstrates how physics-informed neural networks generate highly accurate projections using minimal experimental data. Traditionally, designing and optimizing controlled-release materials relies on iterative laboratory testing, a process that consumes significant time and financial resources. The new approach, spearheaded by engineering associate professor Vikas Srivastava alongside researchers Daanish Qureshi and Khemraj Shukla, integrates fundamental physical principles directly into machine learning architectures. Originally conceptualized by Brown mathematician George Karniadakis, these networks bypass the need for massive datasets by embedding governing equations, such as Ficks Law of Diffusion, into the model training process. This allows the system to extrapolate long-term drug release behavior from short-term observations. In testing, the team evaluated existing experimental data across various material designs. For simple planar materials, the model achieved accurate predictions using only the first six percent of available data, reducing required experimentation by ninety-four percent. More complex, structured materials like folded or wrinkled patches required thirty-three percent of the data, cutting experimental time by sixty-seven percent. To address inherent laboratory variability, the researchers enhanced the diffusion model with Bayesian statistical methods, enabling the AI to quantify data uncertainty and align outputs more precisely with real-world measurements. While the initial validation focused on external delivery formats, the underlying methodology is adaptable to internal systems, including pills and injectable therapies. Srivastava emphasized that the framework represents a tangible application of artificial intelligence in biomedical engineering, offering a pathway to faster, more affordable therapeutic development. By minimizing reliance on extensive trial-and-error testing, the technology promises to accelerate the transition of novel drug-delivery products from laboratory concepts to clinical availability.