The Algorithmic Assistant Is Already Here
From reading medical scans to automating chart notes, AI is already a powerful partner in Indian healthcare. These systems can analyse vast amounts of data at incredible speeds, identifying patterns the human eye might miss. In radiology, for example,
AI helps sift through thousands of images, flagging potential abnormalities for a specialist to review. This can lead to faster diagnoses, more personalised treatment plans, and reduced administrative burdens on overworked doctors. More than half of healthcare plans and a quarter of providers are already using these tools to improve everything from diagnostics to care navigation. The goal is to augment the skills of medical professionals, freeing them up to spend more time focusing on their patients rather than on paperwork.
When the Code Gets It Wrong
Despite its promise, AI is not infallible. The technology is only as good as the data it's trained on. If historical data contains biases, the AI will learn and amplify them. For example, a US risk algorithm was found to have underestimated the health needs of Black patients because the data it was trained on reflected historical inequalities in care. In other cases, AI has recommended unsafe cancer treatments or failed to recognise serious injuries in test cases. These systems can struggle with new or complex scenarios that fall outside their training data. A recent study showed that while an AI could often reach the correct diagnosis, it frequently made mistakes in explaining its reasoning or describing the medical imagery, highlighting a gap in true understanding. Without a human expert to question and correct these outputs, the risk of misdiagnosis, delayed treatment, or unnecessary procedures becomes a serious patient safety issue.
The Doctor in the Loop
This is why human oversight is not just a safety net, but a core component of ethical and effective healthcare AI. Human involvement means a qualified professional—a doctor, a nurse, a technician—reviews, interprets, and ultimately takes responsibility for the AI-assisted decision. This 'human-in-the-loop' model ensures that AI-generated insights are placed within the unique context of an individual patient's life, values, and medical history. A machine can analyse a scan, but it cannot understand a patient's fear of a particular treatment, their quality-of-life priorities, or the social factors affecting their health. The human clinician provides the crucial layers of empathy, complex judgment, and holistic understanding that algorithms cannot replicate. Regulatory bodies and professional standards increasingly call for this partnership, embedding human accountability directly into the workflow.
Building Trust Beyond the Algorithm
Ultimately, trust in healthcare AI is not built between the patient and the algorithm, but between the patient and their doctor. Surveys consistently show that patients are cautiously positive about the use of AI, but only if they are confident that a human clinician has reviewed and approved its output. Trust comes from transparency—knowing that AI is being used as a tool to support, not replace, clinical judgement. This requires clear communication from healthcare providers about how and why these technologies are being used. When patients understand that AI is one of many tools being wielded by an expert they trust, it becomes a welcome enhancement rather than a source of anxiety. Building this trust is essential for the responsible and successful integration of AI into our healthcare system.
