Ajay Mahipal, Managing Director, HealthQuad and Divya Gulati, Senior Associate, HealthQuad throws light on how AI is transforming the healthcare sector in India
In the heart of Eastern India, Bihar’s Purnia District suffers from a high burden of Tuberculosis and a skewed doctor-patient ratio. With incomes hovering at a mere USD 5 per month, this region is emblematic of chronic impoverishment. qXR was deployed to address this challenge. Within its early stages, over 2,500 X-rays were seamlessly processed, pinpointing 299 TB-presumptive patients—a feat of early detection demonstrating that cutting-edge solutions can reform healthcare at the grassroots.
Artificial Intelligence (AI) in healthcare holds true across the healthcare value chain from self-care, prevention, triage and diagnosis to clinical decision support, care delivery, population health, operational improvement, and R&D. The global healthcare AI market is projected to grow at a rapid CAGR of 40 per cent thereby increasing the overall market from $13 billion in 2023 to $188 billion in 2031. Due to its market attractiveness and disruption potential, healthcare AI has witnessed the most investments across all AI sectors in recent years having raised a massive $56 billion in equity funding till date with AI tools for clinical decision support and precision medicine being some of the most funded sub-segments.
In India, over the last few years, there has been an emergence of a large number of healthcare AI start-ups particularly due to the abundance of tech talent, access to affordable datasets and inflow of private capital. These start-ups have been able to solve some of the mega healthcare challenges related to accessibility, affordability, and quality of care prevalent not only in India but also across other low- and middle-income countries globally. As per NITI Aayog, AI expenditure in India is expected to reach $12 billion by 2025, adding $1 trillion to India’s economy by 2035.
Below is a brief summary of the myriad ways in which AI is transforming healthcare:
• Preventive care & wellness: Rapid adoption of wearables and connected devices over the past few years have empowered patients to take care of their own health and wellness. Leveraging the large amounts of data collected through these devices, AI models have emerged which provide personalized plans and guidance based on individual health goals. AI-led models are also being increasingly used for chronic disease management such as diabetes and hypertension as well as for solving issues related to mental health.
• Triage: AI-enabled e-triage models have emerged which help triage patients and provide guidance if the symptoms require additional healthcare resources. These solutions are extremely useful in healthcare systems with long waiting times and in rural areas with shortage of primary care resources.
• Diagnostics: Some medical specialties have witnessed disruption by AI-led diagnostic models, due to their large emphasis on pattern recognition, such as radiology, pathology, dermatology, and ophthalmology. In these areas, several AI applications have received FDA clearance and are now witnessing rapid adoption globally due to associated benefits such as reduced costs, improved doctor productivity, lower error rates and turn-around time. In India, AI solutions are now being increasingly used for early detection of breast cancer, diabetic retinopathy, as well as for mass screening of contagious diseases such as COVID-19 and Tuberculosis.
• Care delivery: With the rapid increase of medical knowledge, it is ever harder for physicians to keep up to date. AI solutions that retrieve relevant medical knowledge for each patient and present it in a structured way can help the physicians decide on the best treatment option, saving time and leading to a more comprehensive evidence-based decision-making process. AI models may also be able to detect patients at high risk of complications and provide guidance for further clinical decision support, with the opportunity for early intervention which may result in improved health outcomes and reduced length of stay in hospital and related healthcare costs.
• Hospital administration: Potential areas for improving healthcare operations include automating low value-add administrative tasks across scheduling, hospital admissions, discharge, and capacity management. Such applications can help improve patient experience as inefficiencies along the patient pathway are ironed out and relieve health workers of mundane tasks, saving both money and time, and thereby improving clinical outcomes.
• Pharma R&D: Life science companies have begun using generative AI foundation models in their R&D to generate best suited candidate molecules thereby accelerating the drug development process. Generative AI deployment could unlock potential value equal to 2.6 per cent to 4.5 per cent of annual revenues across the pharmaceutical and medical device industries globally.
• Population health: AI can be used on large datasets to predict health outcomes within a population, which could help health systems focus on prevention and early detection, improve population health outcomes and, over time, ensure the financial sustainability of the care system.
Though the potential of AI in healthcare is promising, there are challenges and limitations in widescale adoption. Payor re-imbursement/insurance coverage for use of AI solutions will be critical for rapid adoption by healthcare providers and patients. Further, healthcare as an industry has been traditionally slow to adopt technology and the lack of interoperability may pose a significant challenge in integrating AI systems into the existing healthcare infrastructure. Patient privacy, data security and algorithm bias are other major concerns that must be addressed. Regulatory frameworks need to be in place to ensure ethical practices, transparency, and accountability in the use of AI for healthcare.
AI has ushered in a new era in Indian healthcare, with the potential to revolutionize the entire healthcare value chain. From early disease detection to personalised treatment plans and administrative efficiency, AI is transforming the Indian healthcare landscape in unprecedented ways. As the technology continues to evolve, it is essential for healthcare organizations, regulators, payors, and the medical community to work together to harness the full potential of AI while addressing the ethical, regulatory and interoperability challenges. The future of Indian healthcare is undoubtedly intertwined with the continued advancement of AI, promising improved patient outcomes and a more efficient Indian healthcare system.
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