The second edition of the International Conference on AI in Healthcare was held at Yashoda Hospital in Hyderabad on 13-14 June 2026. Robotic cancer surgeon Dr. Sunkavalli Chinnababu led the event, which attracted 2,000 delegates, 375+ speakers, policymakers, innovators, healthcare leaders and media partners across six parallel halls. Covering precision medicine, AI in radiology and pathology, innovation, discovery, policy, and startup challenges, the conference showcased the culture of a technologically enthusiastic society. 

The 45-minute panel discussion, Peering Into the Future: The Autonomous Hospital AI Era, as the final session, proved to be the conference’s crowning event. The panel brought together a distinguished group of experts: Dr. Mona Duggal, Director, Indian Council of Medical Research (ICMR), New Delhi; Dr. Amrut Kadam, Professor and Head of Radiation Oncology, Victoria Hospital, Bengaluru; Dr. H. Narendra, Professor and Head of Surgical Oncology, Sri Venkateswara Institute of Medical Sciences (SVIMS), Tirupati; and Dr. Bhaskar Rajakumar, CEO of Charaka MedTech. I was invited to moderate and lead the discussion.

In my opening remarks, I invoked what I called the Empire Analogy. Throughout history, empires have swept over kingdoms and societies. The defeated often adopted the language, institutions and knowledge systems of the new order. India under British rule is one example. Over time, Indians mastered the English language, absorbed Western education, and participated in the institutions of the Empire so fully that a person of Indian origin, Rishi Sunak, eventually became Prime Minister of the United Kingdom, serving for nearly twenty months before his party’s electoral defeat in 2024.

The analogy was intended neither as praise nor as criticism, but as a reminder that transformative systems cannot be ignored. Artificial Intelligence is emerging as a new global force—an empire of knowledge, computation and data. Nations that merely resist it may be left behind; nations that understand it, internalise it, and adapt it to their own strengths can use it as a powerful instrument of growth and leadership. India must, therefore, learn to engage with AI not as a passive consumer, but as an active participant and creator.

The metaphor lightened the atmosphere and encouraged the panellists, all highly accomplished professionals, to speak with candour. The discussion soon converged on the Ayushman Bharat Health Account (ABHA), which many regarded as the first serious step towards creating a digital health infrastructure for India. ABHA provides individuals with a unique digital health identifier, enabling them to link and manage their medical records across participating hospitals, laboratories, pharmacies and other healthcare providers. Just as UPI created a common digital infrastructure for financial transactions, ABHA has the potential to become a foundational layer for the secure exchange of healthcare data, treatment histories, insurance-related information, and continuity of care.

The second major concern was the quality of healthcare data itself. The panel noted that India suffers not from a scarcity of data but from an abundance of fragmented, inconsistent and poorly curated data. Information collected in screening camps without recording the type of equipment used, calibration standards, operator competency, or measurement protocols often has limited scientific value. Worse, such data can produce misleading conclusions, such as proclaiming a city to be a ‘diabetes capital’ based on non-standardised surveys, estimating hypertension prevalence using inconsistent measurement techniques, or assessing oral and cervical cancers through purely visual examination without rigorous diagnostic confirmation. Artificial intelligence can only be as reliable as the data on which it is trained. Poor data creates poor intelligence.

The discussion then turned to the idea of the autonomous hospital itself—a hospital where AI manages and optimises many clinical, operational and administrative processes in real time, augmenting rather than replacing human professionals. Here, the panel was both optimistic and cautious. Three lessons and a cautionary point emerged.

The first lesson is about architecture. In mission-critical aerospace and defence systems, autonomy is never treated as magic. It is painstakingly built on redundancy, graceful degradation, fail-safe modes, continuous telemetry, rigorous verification protocols, and clear chains of accountability. Autonomous hospitals must be designed in the same spirit. Every AI recommendation must be explainable and traceable: what data entered the system, what inference was drawn, what action was suggested, what degree of uncertainty existed, and at what point human intervention became mandatory. Trust cannot be demanded; it must be engineered.

The second lesson is real-time situational awareness. In aerospace systems, sensor fusion is fundamental because no single sensor is trusted blindly. Multiple streams of information are continuously integrated to produce an accurate understanding of reality. Healthcare requires a similar approach. AI must synthesise vital signs, laboratory results, imaging studies, medication histories, nursing observations, bedside device data and clinical context. A single abnormal reading should not trigger blind action; the system must recognise patterns, trends, trajectories and risk profiles.

Nursing observation is an equally important sensor in this ecosystem. Experienced nurses often detect subtle deterioration in a patient’s condition before it becomes visible in laboratory reports or monitoring systems. Their judgment is built upon continuous observation, intuition developed through experience, and familiarity with the patient. Yet the indispensable contribution of nursing vigilance to patient safety remains underappreciated in many discussions on healthcare transformation. Any vision of autonomous hospitals that overlooks nurses is fundamentally incomplete.

The third lesson is independent auditability. In defence systems, telemetry is not ornamental; it is the memory of the mission. Every decision, event, anomaly, and response is recorded for later analysis. Hospitals require a comparable telemetry architecture—not only for clinical safety but also for ethical governance. AI can help create transparent and auditable trails that show why tests were ordered, which medications were prescribed, which consumables were used, whether treatment protocols were followed, and whether billing accurately reflected the care delivered. Such transparency protects patients, doctors, nurses, administrators, insurers and healthcare institutions alike.

However, the panel also issued a point of caution. AI must not become another instrument for accelerating the commercialisation of healthcare. Modern hospitals operate under increasing pressures from investors, expensive technologies, branded consumables, diagnostics-driven revenue models and expectations of financial returns. In such an environment, there is a risk that nurses and paramedics may be viewed merely as cost variables and patients as revenue events. Healthcare is fundamentally different from manufacturing or retail. Its purpose is not production but healing. Patients are not products, prototypes, or transactions; they are human beings whose dignity, trust, vulnerability and well-being must remain at the centre of every clinical decision. AI should strengthen this human-centred mission, not undermine it.

The real question, therefore, is not whether hospitals can become autonomous. The real question is: autonomous for what purpose? If autonomy simply means faster billing, higher throughput, more diagnostic testing, and greater revenue generation, it will erode trust. If autonomy delivers safer care, fewer errors, greater transparency in decision-making, reduced administrative burdens, fairer audits, earlier interventions and better patient outcomes, then AI can become a transformative force in healthcare—one in which treatments are transparent and transactions fair, accountable and traceable. 

The panel concluded with a clear consensus that the truly autonomous hospital remains at least five years away, and perhaps considerably longer in many settings. Automation should not be confused with artificial intelligence. Digitising a workflow is not the same as creating an intelligent system. Genuine AI depends on high-quality, real-time data acquired without manual delays, validated through redundancy, analysed systematically, and refined through continuous feedback. Above all, AI must never be allowed to compromise compassion, accountability, professional judgement, or human dignity.

How then do we progress?

The first requirement is education. AI literacy must be integrated into the curricula of nurses, paramedics, administrators and medical students. AI should be taught not merely as a technology but as a clinical and managerial tool.

The second requirement is transparency. Billing systems, procurement practices, diagnostic recommendations and treatment pathways should become auditable through AI-enabled oversight.

The third requirement is the creation of a national health data architecture built around the fully digital ABHA (Ayushman Bharat Health Account) framework and the broader Ayushman Bharat Digital Mission (ABDM). Just as UPI transformed digital payments by creating a common interoperable stack, India requires a healthcare ‘stack’ that enables secure, patient-consented exchange of health information across institutions. Such a platform could eventually support preventive healthcare, personalised medicine, clinical research, public health surveillance, and AI-assisted decision support at a national scale.

Only when these foundations are firmly in place can the autonomous hospital evolve from a technological aspiration into a trusted institution that unites the precision of machines with the compassion and wisdom of human caregivers. Until then, talk of fully autonomous hospitals remains an alluring vision—but visions unsupported by strong foundations are no more than castles built on sand.