India is witnessing an extraordinary surge in artificial intelligence. New foundation models are announced every few months. Healthcare chatbots are becoming increasingly advanced. Hospitals are trying out AI-augmented processes, while startups are competing to come up with groundbreaking applications.
This is indeed heartening since there is no doubt that healthcare will greatly benefit from the use of AI. But having been involved in creating AI-driven diagnostics for many years now, I feel like we are asking the wrong question. The conversation today is largely about how intelligent our models are becoming.
The more important question is whether our healthcare system is ready to make that intelligence useful. AI in healthcare won’t work simply because the algorithms get bigger and faster. Instead, the system will become more successful because of better diagnosis, access to the information, and process integration.
AI is only as good as the data it receives
The current debate revolves around issues such as architecture, computational capacity, and benchmarks. While these are all valid points, they constitute just one component of the AI healthcare technology stack. Unlike other industries, the healthcare industry starts with collecting biological signals.
All recommendations offered by the system will depend on the quality of the diagnostics fed into the system.
When the original signal is incomplete, inaccurate, or poorly represented, the sophistication of the algorithm matters very little. The healthcare sector has always abided by the fundamental philosophy that quality decisions require good data.
In AI, that principle has not changed. The challenge therefore is not simply building better models. It is building systems capable of generating trustworthy clinical data in real-world environments.
The missing conversation around diagnostics
India has seen some significant strides towards digitisation of the healthcare sector. Electronic health records, telemedicine, and the use of connected health technology have all become more common practice. However, the process of diagnosis continues to be a fragmented one.
Patients will still go from clinic to laboratory to imaging facility to hospital before being able to piece together their clinical picture. Data will be created in different silos which do not always talk to each other. Useful data will become fragmented rather than integrated. Artificial Intelligence alone cannot overcome the problem.
If AI in healthcare is supposed to enable early detection, effective triaging, and quick decisions, there needs to be development in the infrastructure for diagnostics as well. The best algorithms will not make up for poor data or disassociated workflow. This is where healthcare innovation should move forward from here.
The real challenge begins after the algorithm is built
Creating an AI model is an essential step, but it certainly does not mark the beginning of the challenging journey. This journey begins when this model is brought into the clinic. Clinics are unpredictable places. Devices perform in different conditions. Patients differ from each other. The connectivity may be unreliable. The staff works under time pressure.
Technologies which perform exceptionally in a laboratory setting do not guarantee success in such environments. Through my experience over the years, I have come to realise that successful implementation of health care AI is dependent on the quality of deployment rather than laboratory results.
Can the system capture reliable diagnostic signals? Can it fit seamlessly within the clinical workflow? Do clinicians feel comfortable with the results and the process that generates them? These are the factors that can influence acceptance of the model even more than accuracy itself.
Supporting clinicians, not replacing them
An assumption about AI is that its ultimate purpose is automation. With regard to the healthcare industry, the ultimate purpose is augmentation. Healthcare professionals have to make difficult choices continuously even as they work with patients, documentation, regulations, and professionalism.
Technology should aid in making their job simpler, rather than complicating it further. Good artificial intelligence technology is always invisible. They make sense of information by identifying patterns and communicating meaningful insights at the right time during existing processes.
Where AI requires distinct dashboards, extra login systems, or new procedures altogether, acceptance is tough no matter how capable the technology is. Healthcare workers don’t need technology that’s competing with their own expertise.
Trust must Be Built Into the System
Healthcare operates on trust. Patients trust clinicians. Clinicians trust evidence. The clinicians place trust in the evidence. For any AI to come into this setting, it should be trusted.
Clinical confidence does not come from marketing or benchmark numbers. It is created where systems work in a consistent manner among different patient populations, understand their output, and work effectively under normal circumstances. Validation therefore extends far beyond technical testing.
This includes implementation in practice, ongoing monitoring, input from clinicians, and responsible governance of the entire lifecycle of the technology. As more data in healthcare moves to the digital realm, issues of privacy, consent, ownership, and responsibility become increasingly important. These are not regulatory niceties; they are basic preconditions for gaining and maintaining public trust.
India’s Opportunity Lies Beyond Software
India has several distinct strengths that most other nations cannot compete with. The country’s developing digital health ecosystem, skilled engineers, developing healthcare infrastructure, and one of the biggest primary healthcare delivery systems in the world offer a great platform for AI-assisted treatment. The next opportunity is not simply to build more healthcare applications.
This is because of the need to enhance the diagnostic capacity infrastructure that will enable AI to function efficiently in real time. The reliability of data collection, interoperability of systems, good workflows, and sound governance will determine whether healthcare AI is able to improve the health of patients or not. It may not necessarily be the countries with the biggest models that will succeed in healthcare AI. They will be those that build the strongest foundations beneath those models.
Looking Ahead
AI is bound to have an impact on the future of medicine. But the most valuable thing that AI will do for us will definitely be beyond substituting doctors or automating clinical decisions. It will add value by assisting physicians in making better decisions through proper diagnostics. In moving ahead with the Indian healthcare AI ecosystem, we need to avoid evaluating progress purely based on our algorithms.
The crucial test will come when each advancement made in artificial intelligence is accompanied by an equivalent advancement in diagnostics, workflow, and patient trust. Because in healthcare, intelligence does not begin with the model. It begins with the quality of the signal, the confidence of the clinician, and the trust of the patient. Those foundations will determine whether India’s AI revolution transforms healthcare or simply transforms headlines.
Ashissh Raichura — Founder & CEO, Scanbo Technologies
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)
