Researchers at the University of Manchester have developed a structured approach to evaluate whether artificial intelligence can reason logically in the context of biomedical research. This new methodology aims to improve the reliability and safety of AI applications in healthcare innovation by rigorously testing the machine’s ability to draw sound, logical conclusions from complex biological and medical data. Traditional AI systems often excel at pattern recognition but struggle with causal reasoning or understanding the underlying mechanisms in scientific domains. To address this, the Manchester team created a framework that uses controlled, real-world biomedical scenarios to assess AI’s capacity for logical inference—such as identifying cause-and-effect relationships, making valid predictions based on evidence, and avoiding flawed assumptions. The approach involves designing test cases that mirror actual research challenges, such as interpreting genetic data to predict disease progression or evaluating the effectiveness of drug treatments. These scenarios are structured to probe not just accuracy, but the reasoning process behind AI-generated insights. By analyzing how the AI arrives at its conclusions, researchers can identify when it relies on spurious correlations or fails to account for key variables. The team’s work is particularly significant as AI becomes increasingly embedded in drug discovery, diagnostics, and personalized medicine. Ensuring that AI systems don’t just produce correct answers but do so through valid logic is essential for building trust among clinicians, regulators, and patients. The methodology has already been tested on several AI models used in genomics and clinical data analysis, revealing that many systems produce plausible results through flawed reasoning. This highlights the need for more transparent and accountable AI in high-stakes medical environments. By introducing standardized benchmarks for logical reasoning, the Manchester researchers aim to set a new standard for evaluating AI in biomedical science—helping to ensure that future innovations are not only accurate but also scientifically sound and ethically robust.