AI is revolutionising healthcare - from diagnostics and robotic surgeries to hospital administration and preventive care. With India’s AI health market growing rapidly, this shift promises faster, personalised, and accessible care.
Dr Ganesh Natarajan is Chairman of GTT Data Solutions Ltd.
Artificial Intelligence isn’t some far-off idea in healthcare anymore—it’s here, and it’s already changing the game. Whether it’s helping doctors detect diseases earlier, guiding precision in robotic surgeries, enabling virtual consultations, or streamlining hospital workflows, AI is steadily transforming how healthcare is delivered and experienced.
Globally, the AI healthcare market was valued at USD 14.92 billion in 2024 and is expected to climb to USD 21.66 billion in 2025. India is catching up fast. As per the IMARC Group, the Indian AI healthcare market is projected to grow from USD 333.16 million in 2024 to a massive USD 4,165.26 million by 2033, growing at an impressive CAGR of 30.78%.
These numbers are not only impressive, but they also indicate a fundamental shift. In a healthcare landscape dealing with growing patient volumes, a rise in chronic illnesses, and overburdened systems, AI is no longer a nice-to-have. It’s becoming essential to build a healthcare system that’s faster, smarter, and more personalised.
Transforming Health Administration
AI’s first wave in healthcare is already evident in how administrative tasks are handled. From faster claims processing to reducing manual errors in health record analysis, AI is streamlining back-end operations. Hospitals are using AI tools to analyse patient records, interpret test results, and even allocate medical staff efficiently. With machine learning algorithms combing through vast datasets, institutions are experiencing fewer errors and faster workflows, freeing up clinicians to focus more on patient care.
Natural Language Processing (NLP) is also finding its place across hospitals, parsing complex patient histories, and assisting physicians in diagnosis. AI’s predictive analytics are helping forecast patient admissions, flag potential readmissions, and even identify at-risk patients, all of which are crucial to both cost management and care quality.
Reimagining Diagnosis and Preventive Care
The most visible impact of AI is in clinical diagnosis and early intervention. Algorithms trained on vast datasets can now detect anomalies in radiology images, identify signs of diseases such as cancer, or assist in pathological analysis with high accuracy. Robotic surgeries, where AI assists surgeons by enhancing precision, are reducing recovery times and improving outcomes.
AI’s impact is also significant in preventive care. Telemedicine platforms now use AI to triage patients, while wearable devices collect and interpret real-time health metrics to warn users and doctors of impending health issues. With such technologies, India is inching closer to bridging its healthcare accessibility gap, especially in rural or underserved regions. AI-led virtual consultations, supported by mixed reality tools and possibly soon the metaverse, are redefining the meaning of access and affordability in healthcare.
Data: The Backbone and the Bottleneck
While AI thrives on data, healthcare has traditionally struggled with fragmented and often unstructured information. The quality and completeness of data remain a challenge, particularly in developing nations. In developed countries, privacy regulations are essential but can sometimes hinder the free exchange of data. For AI to truly deliver on its promise, healthcare systems must ensure that Electronic Health Records (EHRs) are standardised, interoperable, and secure.
AI tools are helping address some of these gaps by identifying inconsistencies and pulling insights from informal sources like doctor-patient conversations. But balancing this capability with the need for data privacy remains a tightrope walk. Ethical considerations around data usage, patient consent, and transparency in AI decision-making will remain a top priority as the sector evolves.
Risks, Reliability, and the Role of Human Oversight
While AI has great potential in healthcare, it also comes with certain risks. The phenomenon of “AI hallucination,” where systems misinterpret patterns or generate incorrect suggestions, can be fatal in a clinical context. Hence, a dual-intelligence approach is critical—AI can perform routine, repetitive tasks, but diagnosis and prescription must always be vetted by trained medical professionals.
Moreover, AI integration into existing hospital IT infrastructure requires careful planning. Interoperability, clinician training, and trust-building are just as important as the algorithm itself. Physicians must understand not only the "what" of AI recommendations, but the "why" behind them. Explainability, in this context, becomes essential.
The Road Ahead: Systemic Transformation
The trajectory of AI in healthcare is moving from isolated point solutions to system-wide transformation. From the first interaction with a patient, through diagnostics, surgery, recovery, and post-care monitoring, AI has the potential to be a companion across the care continuum. It is already influencing career decisions, encouraging young professionals to join the field with the confidence that they will be supported by cutting-edge tools.
In research, AI-enabled large language models are expediting drug discovery and enabling collaborations between academic institutions, pharmaceutical companies, and med-tech startups. Accelerated clinical trials, patient cohort identification, and personalised medicine are no longer theoretical possibilities; they are becoming the norm.
Ultimately, the promise of AI in healthcare is not just technological but deeply human. It lies in reducing pain, preventing suffering, extending healthy lifespans, and improving quality of life at scale. What was once aspirational is now achievable, and the transformation has already begun.
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