India’s diagnostic future: Unlocking tomorrow’s healthcare with AI, machine learning and digital innovation
By Arunima Rajan
In an interview with Arunima Rajan, Dr. Chiranjib Sur from IIT Guwahati explains how AI and machine learning are reshaping India's diagnostic landscape. He explains that innovations ranging from AI-powered image analysis and multi-omics integration to IoT-driven point-of-care testing, these innovations are set to deliver faster, more precise healthcare across the country. Yet, challenges remain—data standards are inconsistent, regulatory hurdles persist, and the gap between breakthrough research and everyday clinical application still looms large. Dr. Sur insists that a closer collaboration between academia and industry is not just beneficial but essential to bring high-quality, accessible healthcare to every corner of India.
How can AI and machine learning reshape India's diagnostic landscape?
India's diagnostic landscape is undergoing a rapid transformation, driven by advancements in artificial intelligence (AI) and machine learning (ML). AI-powered image analysis, predictive analytics, and automated screening tools can significantly enhance the accuracy and speed of diagnosis. These technologies have the potential to enhance diagnostic accuracy, improve early disease detection, and reduce dependency on specialist doctors, particularly in resource-limited settings. AI-powered image analysis, predictive analytics, and automated screening tools can play a crucial role in addressing India's healthcare challenges. Emerging research areas such as federated learning for privacy-preserving AI, AI-driven multi-omics analysis, explainable AI for greater clinical trust, and edge AI for real-time diagnostics are poised to be game-changers. Digital health and big data convergence are also set to revolutionise patient care by enabling remote AI-assisted consultations, predictive analytics for disease outbreaks, and intelligent electronic health ecord (EHR) systems that support evidence-based decision-making.
However, bridging the gap between groundbreaking research and practical diagnostic tools presents significant hurdles, including data scarcity, lack of standardisation, regulatory barriers, clinical validation challenges, and funding constraints. Academia-industry collaborations can help overcome these obstacles through research consortia, startup incubation, and joint initiatives with government agencies like ICMR and DBT. The role of IoT and sensor technologies in point-of-care diagnostics is particularly promising, with wearable biosensors, smart diagnostic kits, and AI-powered mobile labs improving healthcare accessibility across India's diverse regions. Institutions like IIT Guwahati (IITG) can play a pivotal role in fostering innovation by establishing AI and HealthTech research hubs, launching joint industry-led research programmes, and organising hackathons to crowdsource solutions. Recently, BIRAC-GCI sponsored a project to detect mental health in individuals at IIT Guwahati.
Furthermore, IITG's contributions to personalized medicine through AI-driven drug response predictions, cancer diagnostics, and precision public health are helping to shift healthcare towards more individualized treatments. To effectively serve urban and rural communities, academic innovations in diagnostics must be tailored to suit diverse needs—AI-driven automation can enhance urban healthcare efficiency, while low-cost AI-assisted diagnostic tools and mobile health units can improve accessibility in rural areas. Notable research at IITG includes deep learning models for mental health detection, AI-assisted cervical cancer screening, and microfluidic biosensors for rapid disease diagnosis.
To fully unlock the potential of diagnostic innovation in India, regulatory support must evolve to include faster AI approval pathways, robust data privacy frameworks, and public-private research incentives. Clearer guidelines and streamlined approval processes will accelerate the translation of AI-driven research into practical healthcare solutions. With sustained innovation, strategic collaborations, and regulatory advancements, artificial intelligence and machine learning will play a transformative role in shaping India's diagnostic future, making high-quality, accessible healthcare a reality for all.
Which emerging research areas might become game-changers for diagnostics?
Several cutting-edge research areas have the potential to revolutionise diagnostics in India, like through Federated Learning and privacy-preserving AI, where given the sensitivity of healthcare data, federated learning enables AI models to be trained across multiple hospitals while keeping patient data decentralised, enhancing both privacy and effectiveness. AI-driven Multi-omics Analysis integrates genomics, proteomics, and metabolomics with AI, which can help in disease prediction and precision medicine. Research on explainable AI can build trust among healthcare professionals and improve regulatory acceptance. Edge AI for point-of-care diagnostics includes deploying lightweight AI models on mobile devices, and portable diagnostic kits that can enable real-time disease screening in remote settings.
How can digital health and big data transform patient care, especially in resource-limited settings?Education
The convergence of digital health and big data can revolutionise patient care through Remote AI-assisted consultations like Telemedicine platforms integrated with AI-driven diagnostic support, which can expand healthcare access to underserved areas. Predictive Analytics for disease outbreaks through monitoring healthcare centers and analysing health records, environmental factors, and epidemiological data can help predict and contain disease outbreaks like dengue, tuberculosis, or COVID-19. Intelligent Electronic Health Record (EHR) and AI-based Decision Support Systems can assist doctors in making evidence-based decisions, minimizing diagnostic errors.
What key hurdles exist in bridging research and practical diagnostic tools, and how can academia-industry collaborations help?
Some of the key hurdles include non-standardisation and no-regularisation of data quality, data scarcity and standardisation, validation and clinical Adoption, and gaps in the pipeline between data collection and commercialisation. The funding resources flow in limited directions. We need something like Food and Drug Administration (FDA) and European Medicines Agency (EMA) in USA and EU, which is very strict and very focused with active participation from industry.
Academia-industry collaboration helps. Institutions like IITG can act as hubs where interdisciplinary teams (AI, medicine, bioinformatics) collaborate with industry players to develop real-world solutions. Startup incubators like BioNest and TIC are also available. IIT's research incubators can support AI-driven health startups, helping them navigate regulatory and commercialisation challenges. Government-backed AI Initiatives with agencies like ICMR, DBT, and MeitY can facilitate pilot studies and large-scale deployment.
How can IoT and sensor technologies improve point-of-care diagnostics?
IoT-enabled diagnostics can significantly enhance healthcare delivery in India's diverse landscape. wearable biosensors for Continuous glucose monitors, ECG patches, and blood pressure sensors can track health conditions in real-time. Smart Diagnostic Kits like low-cost IoT-enabled testing devices for infectious diseases like malaria or tuberculosis can help with early detection and treatment. AI-powered mobile Labs where they dedicate research works for developing portable diagnostic systems integrated with AI can be deployed in rural areas, reducing the burden on overburdened hospitals.
How can institutions like IIT foster collaborations for next-gen diagnostic solutions?
IITG can play a crucial role in fostering innovation through AI and healthTech innovation hubs. Establishing dedicated research hubs focusing on AI-powered diagnostics. Joint Industry-Led Research Programs Collaborating with pharma, MedTech, and AI firms to co-develop AI-powered diagnostic solutions. Hackathons and Challenges is organising competitions to crowdsource innovative AI-driven healthcare solutions and train people interested in getting into this domain. Apart from that, many interdisciplinary initiatives and dedicated centers nurture the growth of AI usage in healthcare problems.
How is IIT contributing to personalised medicine, and what opportunities exist for mainstream integration?
IITG's AI and bioinformatics research is advancing personalised medicine through AI-driven drug response predictions. Using machine learning to predict patient-specific drug responses based on genetic profiles. Personalised cancer diagnostics-based AI-powered genomic analysis to tailor cancer treatments. Precision Public Health leverages AI to optimize treatment plans based on demographic and genetic diversity in India. However, clinical trials and other commercial benefits are yet to be ripped out from these research works.
How can academic innovations in diagnostics be tailored for both urban and rural India?
There are significant differences in the healthcare requirements for urban and rural areas. Like AI-driven automation in radiology and pathology can reduce diagnostic turnaround times in urban areas, whereas rural areas require AI-assisted low-cost diagnostic tools and telemedicine platforms can address the shortage of specialists. There are many government-backed initiatives like mobile health units and Asha workers like semi-nurses that can be made to access these deploying AI-enabled diagnostics technologies in mobile health vans for rural areas for quick screenings and gathered preparedness
Are there any current IITG projects that could significantly impact India’s diagnostics?
Partnerships with hospitals and regulatory agencies will be key to integrating personalized diagnostics into mainstream healthcare. Some ongoing research at IITG includes deep learning for different disease detection through analyzing X-ray images to detect diseases like TB in early stages, cervical cancer screening including developing low-cost AI-assisted screening tools for early detection, point-of-care Biosensors like microfluidic biosensors integrated with AI for rapid disease diagnosis and, mental health detection and prevention amidst climate change.
What regulatory support is needed to unlock the full potential of diagnostic innovation?
Most of the funding is coming from government of India. We need more public-private research Incentives to increase funding and policy support for AI-healthcare startups. Data privacy and security regulations to strengthen the policies on AI-driven healthcare data protection and safeguard data collection business and ownership. Lastly, faster AI approval pathways through the establishment of structured guidelines for AI validation and regulatory approvals.