The medical field has always been a beacon of innovation, and the integration of artificial intelligence (AI) has supercharged advancements, particularly through the convergence of devices, data, and diagnosis. India, with its unique strengths, is poised to lead the development of AI-based diagnostic models by leveraging its vast and diverse population to provide high-quality, annotated medical data.
The role of AI in medical diagnosis
Medical diagnosis often hinges on pattern recognition, whether in chest X-rays, MRIs, or CT scans. Each disease or condition exhibits distinct patterns that clinicians identify to draw conclusions. This process forms the backbone of objective diagnosis. AI has the potential to revolutionise this step by analysing vast amounts of anonymised patient data to recognise these patterns with unparalleled accuracy.
However, the success of AI models depends on the quality and ethical sourcing of data. Ethical data excludes personally identifiable information (PII) such as names or addresses, while still retaining critical demographic details like age and gender, which are often essential for accurate analysis. Additionally, the data must be meticulously annotated. For example, in an X-ray, patterns indicating diseases like pneumonia, emphysema, cystic fibrosis, or cancer need to be clearly marked. With millions of such annotated data points, AI models can be trained to deliver precise and reliable diagnostics.
The key challenge is data bias. What this means is that in the example of chest X-Ray, if AI can identify around 25 types of patterns (diseases/illnesses), then we need data for these 25 types of patterns in almost equal numbers. Too much data of one pattern and too little data for another can create a learning bias. Getting sufficient data quantity of rare illness patterns is always a challenge. This can impact on the specificity and sensitivity (false positive and false negative results) of the AI model. Thus, getting accuracy beyond 97% is always a challenge with AI models in medical area.
While AI can now generate synthetic data to supplement real-world datasets, even synthetic data creation requires a robust foundation of real, high-quality data. This underscores the importance of ethical data collection and annotation as the cornerstone of AI-driven medical innovation.
Innovations in medical devices
The field of medical devices is also undergoing a transformative evolution, driven by advancements in AI. A striking example of this innovation is the stethoscope, a tool first invented in 1816 by French physician René Laennec. Originally designed to allow doctors to listen to heart and lung sounds, the stethoscope has now evolved into a sophisticated digital device that is gaining widespread adoption worldwide.
Modern digital stethoscopes not only amplify sound but also record and store it, creating a rich repository of data that can be used to train AI systems. Traditionally, physicians have relied on their expertise to identify patterns in these sounds, diagnosing conditions such as heart murmurs or aortic stenosis. However, with the integration of AI, this diagnostic process is being revolutionised. By training AI models on annotated sound data, healthcare providers can achieve greater accuracy, efficiency, and consistency in diagnosing cardiovascular and respiratory conditions.
This transformation has breathed new life into the stethoscope, a device that had become more symbolic of a doctor’s identity than a cutting-edge diagnostic tool. Today, it serves as a critical first point of diagnosis, bridging the gap between traditional medical practices and the future of AI-driven healthcare. As medical devices continue to evolve, the fusion of AI and time-tested tools like the stethoscope promises to redefine patient care, making it more precise, accessible, and impactful.
India’s unique opportunity and government support
India, with its vast and diverse population, is uniquely positioned to lead the development of AI-based diagnostic models. Indian doctors encounter a wide range of diseases, infections, and illnesses, many of which are rare in other parts of the world. For instance, tuberculosis, which is prevalent in India, is rarely seen in Scandinavian countries. This exposure positions India as a key source of ethical data collection and annotation, enabling the development of robust AI-based diagnostic models.
While many Western countries face challenges in medical data collection due to stringent data privacy laws, India has the opportunity to strike a balance between data privacy and the ethical use of data for medical advancements. The Indian government should consider promoting the free use of ethical datasets on public platforms and encouraging its premier medical and engineering institutions to develop meaningful AI models. Institutions like AIIMS and IITs can play a pivotal role in creating an ecosystem like that in the United States, fostering innovation and collaboration.
The Indian government’s focus on fostering innovation in electronics and medical devices further strengthens this potential. By encouraging the development of cutting-edge medical technologies and harnessing the power of AI, India can challenge global leaders in the field, creating innovative solutions unencumbered by legacy technologies. This approach not only positions India as a global leader in medical AI but also addresses critical healthcare challenges within the country.
In order to promote R&D, the government has set up the Startup India Seed Fund, Technology Research Development Corporation (TRDC), Biotechnology Industry Research Assistance Council (BIRAC) and many other initiatives. This has helped kick-start the startup ecosystem. Now is the time to infuse an R&D culture within the startup ecosystem. If we look closely at the underlined policies of some of the government initiatives, some rules are not conducive to the type of R&D that can make substantial impact, the way Chinese companies have exhibited.
For instance, a condition like the expectation of a substantial full product development within two years from inception or mandatory incubation for eligibility of applying for TRDC is devoid of logic. There are startups outside of the incubation center, started by experienced people and doing very deep R&D. By one shot, they are eliminated by this clause. A deep R&D itself may take more than two years. The criteria should focus on the R&D potential of the company, the type of technology they work on, credential of the founders on R&D front and so on. These aspects seem to be missing.
The path forward
The convergence of AI, medical devices, and ethical data collection holds immense promise for transforming healthcare. India, with its unique advantages, has the potential to lead this transformation by developing robust AI-based diagnostic models and fostering innovation in medical technology. By striking the right balance between data privacy and ethical data use, India can pave the way for a new era of medical advancements that benefit both its population and the world at large. The Indian startup ecosystem is well positioned to take advantage of the evolving technology landscape provided government tweaks some of its policies to promote deep tech R&D.
(Bhupendra Bhate is a technocrat who is currently CEO and co-founder of Saintiant Technologies Pvt Ltd, a company specialising in advanced AI-based diagnostic medical devices. [email protected])
Published – April 02, 2025 02:44 pm IST