AI and machine learning have advanced to a stage where their role in medical diagnosis is no longer theoretical but a practical reality. Their ability to learn from millions of cases across diverse populations enables them to generate diagnoses that can exceed the accuracy achievable by any individual doctor. This computational scale positions AI/ML as a catalyst for one of medicine’s most sought-after goals: truly personalised treatment. While a doctor’s expertise grows through experience with a limited number of patients, an AI system can process immense datasets, detect subtle patterns, and integrate countless variables at speeds far beyond human capability. As our understanding of biomarkers and influencing factors deepens, the promise of personalised medicine comes closer to fulfilment.
However, the rise of AI/ML does not reduce the essential role of human physicians. These technologies remain powerful statistical engines—they can detect correlations but cannot yet fully grasp context, mechanisms or meaning. True diagnosis requires understanding: an appreciation of causality, biological pathways, patient history and the complexities of human physiology. AI may reveal patterns, but only humans can interpret them with the depth and judgment needed for sound clinical decisions. In this sense, AI enhances rather than replaces medical expertise.
In this evolving landscape, the educational responsibility before us is substantial. We must prepare students who are proficient in machine learning, data science and computational methods, while also possessing foundational literacy in biomedical concepts. The goal is not to turn every student into a doctor, but to ensure future AI practitioners understand the analytical foundations of diagnosis, treatment and patient data. Without this grounding, they risk building systems that are technically advanced yet clinically misaligned.
AI in biomedicine is still at an early stage. Achieving the ambitious goal of personalised medicine will require vast amounts of high-quality data, well-structured experiments and continuous feedback loops between domain experts and AI systems. This is work that may span an entire generation. Students trained at the intersection of AI/ML and biomedical understanding will play a crucial role—identifying relevant data, designing meaningful experiments, interpreting computational findings and collaborating closely with clinicians and researchers. Their contributions will shape how AI progresses in healthcare and how safely and responsibly personalised medicine becomes a reality.
Ultimately, the future of AI-driven healthcare depends not only on technological innovation but on education—on cultivating professionals who can bridge computation and biology, correlation and causation, machine capability and human judgment. The path to personalised medicine will require nothing less.
- By Professor Vijaysekhar Chellaboina, Vice Chancellor, JK Lakshmipat University