AI – Enabled medtech: The next leap

Where the adoption is real 

India’s MedTech sector is having its most consequential year yet on AI. A knowledge paper released by FICCI and Praxis Global Alliance at the 9th Edition of India Medical Device 2026 lays out the scale of what is at stake: a population crossing 1.47 billion, more than 230 million people expected to be aged 60 and above by 2036, and NCDs already accounting for over 65 per cent of deaths in the country.

Against this, India has just 9.6 physicians and 15.9 hospital beds per 10,000 people, both well below the global average. The gap between what the health system needs to deliver and what it currently can is precisely where the industry believes AI has the most to offer.

Dr Bipin Chevale, CEO, Gleneagles Hospital, Mumbai, points to the areas where adoption is already becoming visible. “The most meaningful AI adoption in Indian MedTech is currently visible in medical imaging, radiology, pathology, patient monitoring and cardiology, where AI is helping with faster interpretation, early detection and clinical decision support. AI-enabled ultrasound, ECG analysis and remote monitoring are also gaining traction. Surgical navigation and robotic technologies are emerging areas, particularly in larger hospitals.” 

That observation sets the tone for much of the conversation around AI in Indian MedTech: its strongest case may lie not in technological novelty, but in its ability to address persistent gaps in expertise and access.

Dr Sabine Kapasi, Global Health Strategist, Founder, ROPAN Healthcare, and UN advisor, sees the same pattern. “The strongest adoption is happening in areas where India faces a clear shortage of specialist expertise, particularly imaging, ECG interpretation, pathology, ultrasound guidance, patient monitoring and screening. The value of AI here is not about replacing doctors. It is about making specialist level support available where a specialist may not be physically present, may be available only after a delay, or may already be handling too many cases. That ability to extend clinical expertise is where AI can make its most meaningful contribution to Indian healthcare.” 

Pavan Choudary, Chairman, MTaI, identifies diagnostic imaging as the clearest example. “Adoption is strongest where there’s high diagnostic volume, specialist shortages and digitised clinical data – making diagnostic imaging (chest X-rays, CT, TB screening) the clearest use case. In practice, radiology includes chest X-ray and CT triage products. Cardiology includes AI-assisted ECG interpretation and remote cardiac diagnostics – an Indian huband-spoke ECG network reported ~5-minute average diagnostic turnaround, showing how specialist-level support can extend to lower-resource settings. Ophthalmology and pathology/digital microscopy include retinal images and pathology slides that suit algorithmic analysis; AI is increasingly embedded in monitoring and clinical decision-support tools rather than sold as standalone products.” 

“For India, the most consequential applications aren’t the most futuristic – they’re the ones that screen large populations, prioritise abnormal cases, shorten reporting time and connect scarce specialist expertise to patients outside metro hospitals.” 

The emphasis on access also comes through in the experience of Anuj Chahal, Founder and CEO, Maverick Simulation Solutions. “The most meaningful AI adoption in India is occurring where the technology addresses a clearly defined clinical or operational problem, works with structured data, and delivers measurable improvements in speed, consistency, or access. Solutions that produce quantifiable gains such as faster turnaround times, more consistent interpretation, or expanded specialist reach.” 

“AI-enabled diagnostics remain the most promising area for scale, particularly in radiology, ophthalmology, pathology, ECG interpretation, and pointof-care screening. Imaging is a natural starting point because digital scans can be analysed consistently; AI can support triage, flag urgent cases, assist reporting, and extend specialist expertise to hospitals that lack adequate access to radiologists or other specialists.” 

For Vikas Sharma, COO, Cryoviva Life Sciences, the significance extends beyond individual devices. “AI is opening a new frontier for MedTech; however, the impact goes well beyond the application of AIpowered devices in hospitals. In diagnostic services, imaging, monitoring, and laboratory medicine, the technology could aid in translating the biological and clinical data into information that will enable better decision-making. The future for life sciences lies in linking those abilities to developments in precision medicine, regenerative medicine, and data-based patient management.” 

The first question, therefore, is not where AI might eventually find a role, but where that role is already becoming visible in clinical practice. The pattern is clear: the applications gaining traction are those where AI can help extend scarce expertise without requiring the health system to wait for the specialist workforce to expand. 

The device becomes a decision partner 

If the first stage of AI adoption has been about helping devices interpret information, the next stage is about changing the role of the device itself. Medical devices are increasingly being designed to analyse data, identify patterns and support decisions rather than simply capture measurements.

Dr Sameer Vankar, Consultant Interventional Cardiologist, Medicover Hospitals, Kharghar, Navi Mumbai, describes this shift from the perspective of day-to-day clinical use. “AI is helping medical devices become more intelligent and useful in day-to-day clinical practice. It can save valuable time during the diagnostic work-up by analysing large amounts of patient data and helping clinicians identify abnormalities more quickly. It can also support therapeutic decision-making by providing additional insights that help doctors choose and plan appropriate treatment. Another important benefit is remote monitoring, where AI-enabled devices can continuously track a patient’s condition and alert the healthcare team to significant changes, allowing timely intervention even when the patient is not physically in the hospital.” 

The change is also reflected in device design itself. “As far as design is concerned, the development of AI-powered technologies is gradually transitioning from standalone medical devices to platforms capable of processing multiple data types,” says Shrish Kumar, CIO, Sri Balaji Action Medical Institute, Delhi. 

Dr Ranjan Shetty, Medical Director & Lead Consultant – Cardiologist, SPARSH Hospital, Infantry Road, Bangalore, takes the argument further into the hospital environment. “From the viewpoint of hospital technology, integration holds greater promise. The intelligent capabilities of these devices become more valuable when their insights can be integrated into electronic medical records, PACS systems, laboratories, and clinical work processes. Designing intelligent devices becomes a matter of software, integration, and cybersecurity along with hardware capabilities.” 

That integration is becoming important because a device increasingly sits within a wider information ecosystem. Its value is not determined only by what it can measure, but also by what happens to that information afterwards.

Ashissh Raichura, Founder & CEO, Scanbo Technologies, stresses: “Devices used to be measurement instruments. The clinician did the interpretation. That is no longer true for the modern generation of AI-enabled devices. The device now interprets alongside the clinician, and often flags what to do next.” 

“This changes design in two ways. The device, the AI and the clinical record need to be built as one integrated system. Stitching AI on top of a legacy device after the fact does not work well; the signal quality, the workflow and the record all have to be designed together. The second change is that the device is no longer just an instrument. It is a decision partner.” 

For Gaurav Goel, CEO and MD, Healthium Medtech, this evolution is already visible across different stages of care. “AI has the potential to transform Indian MedTech by helping clinicians interpret information faster, plan procedures more precisely, and monitor patients more effectively. Its applications are already extending across the continuum of care, from diagnosis and active interventions to post-surgical recovery. Image-based assessments are enabling more accurate tracking of wound healing, while computer-assisted technologies are enhancing pre-operative planning and intraoperative guidance across orthopaedics and arthroscopy. The integration of navigation technologies with intelligent algorithms is also supporting more data-driven planning and decision-making in robotic-assisted surgery.” 

The direction of travel is therefore towards devices that are connected to the clinical context around them. But making a device smarter does not automatically make it easier to adopt. 

The barriers are operational, not algorithmic 

The technology may work in a controlled setting, but healthcare is an operational environment. Devices have to connect with existing systems, fit into established workflows and function within the constraints of hospitals and clinical teams. That is why the obstacles to adoption increasingly sit outside the algorithm itself. 

“The biggest barriers include limited clinical validation, interoperability issues, data privacy and cybersecurity concerns, along with the high cost of implementation. Hospitals also need trained staff, clear regulatory pathways and clinician confidence to integrate AI-enabled devices smoothly into everyday workflows,” says Dr Chevale. 

Dr Vankar sees another dimension to the problem: the gap between clinical and technical expertise. “One of the biggest barriers is the gap between the clinical need and the available technical expertise, as doctors understand the medical problem while technology teams understand the AI and device capabilities. Bringing both experts together on one platform is essential to develop solutions that are clinically relevant, practical and easy to integrate into existing workflows. Stronger collaboration between clinicians, MedTech experts, data scientists and hospitals can help bridge this gap and accelerate meaningful AI adoption.” 

The issue of workflow fit is equally important. “The most significant hurdles to routine adoption still include integration with existing systems, data quality, infrastructure preparation, cybersecurity, and physician acceptance. AI needs to make decision making easier; it does not need to add an additional interface or burden on anyone’s shoulders. AI implementation will require process changes, user training, governance, and accountability,” says Dr Shetty.

Chahal similarly sees data fragmentation and workflow integration as key barriers. “AI adoption in MedTech in India faces challenges that go beyond algorithms, with data fragmentation, validation gaps, workflow integration, skills shortages and economic uncertainty emerging as key barriers. Fragmented hospital systems and weak interoperability make it difficult to move AI from pilots to routine clinical use, while models developed elsewhere may not perform reliably across India’s diverse patient populations, clinical settings and devices. This makes broader local validation across different hospitals, regions and patient groups essential.” 

Choudary describes the barriers as largely operational rather than algorithmic. “The barriers are largely operational, not algorithmic. Interoperability means devices need to plug into HIS, EMR, PACS and lab systems, not add separate screens or manual data transfer. ABDM’s FHIR-based exchange is moving India in this direction, but hospital digitisation remains uneven. Workflow and human factors mean that even accurate algorithms fail if they add alert fatigue or extra steps – AI needs to fit the clinician’s workflow, not the reverse. Trust and accountability require clinicians to have clarity on an algorithm’s intent and limits.” 

“The right model for most higher-risk uses is human-in-the-loop decision support with clear override – echoed in Indian cardiology literature on local validation, calibrated alerts and bias surveillance. Economics also matters: hospitals weigh integration costs, licences, IT infrastructure, training and maintenance, not just purchase price – adoption stays pilot-stage without demonstrated ROI. Data governance is equally important: privacy, consent and cybersecurity are patient-safety issues, not just IT issues; ICMR’s ethical guidelines address this across the AI lifecycle.” 

An AI capability has little value if accessing it makes the clinician’s job harder. The next challenge, therefore, is not simply proving that the technology works, but demonstrating that it works in the environments where it is expected to operate. 

Building the evidence, earning the trust 

Clinical trust has become one of the central questions around AI-enabled MedTech. Accuracy on a dataset is only one part of the equation. Hospitals and clinicians need evidence that a technology performs reliably across different populations and real-world settings.

“The major issue lies in gaining trust of the insights generated by AI. There should be adequate evidence for safety, efficacy, and reproducibility of the insight across different patient populations. As far as emerging technology is concerned, there must be evidence even after the regulatory approval of the device,” says Sharma.

Dr Kapasi believes that the evidence needs to be generated closer to the environments in which the technology will ultimately be used. “Clinical trust will be built through evidence generated in Indian healthcare settings, not through accuracy numbers alone from overseas studies. AI-enabled devices need to be evaluated across medical colleges, district hospitals, private hospital networks and resource-constrained settings because the conditions in which these technologies operate can be very different.” 

“The evidence also needs to answer a more useful question than whether the algorithm is accurate. Does it help doctors diagnose faster? Does it reduce missed cases? Does it improve referrals? Does it reduce complications? Most importantly, does it change clinical decisions in a way that improves patient outcomes? Those are the measures that will determine whether AI moves from an impressive demonstration to a trusted clinical tool.” 

Chahal argues that building trust requires evidence throughout the technology lifecycle. “Building clinical trust in AI-enabled medical technologies goes far beyond demonstrating accuracy or showcasing product features. True trust is earned through transparent validation, clinically relevant evidence and continuous monitoring in real-world settings. This means defining the intended purpose clearly, testing across diverse patient populations and clinical environments, and moving beyond retrospective datasets to prospective evaluations, workflow integration and health economic assessments. In India, shared testing and benchmarking infrastructure, supported by initiatives like the IndiaAI Mission, can help address data representativeness, bias, cybersecurity and interoperability. Ultimately, clinician involvement across the AI lifecycle from product conception to deployment is indispensable to ensure these technologies address real clinical needs, produce actionable outputs and fit seamlessly into healthcare workflows.” 

That need for real-world validation is also reflected in how devices are developed and tested. “AI is fundamentally transforming medical devices by evolving them from static, fixed-function products into dynamic, adaptive, and connected systems driven by software. Today, a medical device is no longer just hardware; it integrates sensors, algorithms, clinical data, and feedback mechanisms to deliver smarter, more responsive care,” says Chahal. 

For Dr Shetty, validation must ultimately translate into measurable clinical outcomes. “Physician trust has to come through proper validation and evidence of effectiveness, especially for varied Indian patients. The evaluation process will have to measure important outcomes such as diagnostic accuracy, patient safety, efficiency, and early intervention.” 

The bar is therefore moving from technical performance to demonstrable clinical impact. For MedTech companies, that means evidence cannot be treated as something added after product development. It increasingly has to influence how the technology is designed, validated and introduced into care. 

Regulation is moving, reimbursement has some catching up to do 

Evidence may establish whether a technology is clinically useful, but regulation determines the pathway through which that technology can enter the healthcare system. At the same time, procurement and reimbursement determine whether it can move beyond isolated deployments. 

The regulatory environment is evolving, but the industry is still navigating how AI-enabled products should be assessed, purchased and paid for. 

“India’s regulatory and procurement frameworks are evolving, but they still need clearer, AI-specific pathways for validation, approval, accountability and post-market monitoring. Reimbursement is also an important gap, as healthcare systems need clearer models to recognise and pay for AI-enabled technologies based on demonstrated clinical value,” says Dr Chevale. 

Choudary points to several recent developments in regulation, while noting that reimbursement remains less mature. “Regulation: the most concrete recent development. CDSCO has formalised diagnostic AI software as requiring a formal medical-device licence, closing a long-standing gap.” 

“CDSCO’s Draft Guidance on Medical Device Software (October 2025) separates Software in a Medical Device (SiMD) from Software as a Medical Device (SaMD), with a four-tier risk classification (Class A-D). Class A/B is licensed by state authorities; Class C/D – including AI cancer-detection tools – falls under CDSCO’s Central Licensing Authority. An Algorithm Change Protocol (ACP) allows iterative AI/ML updates without constant re-licensing, addressing the “adaptive AI” regulatory gap. Final guidance was released 21 July 2026 – a live regulatory shift worth flagging, not settled history.” 

“Reimbursement: less mature. Programmes like AB PMJAY reimburse via treatment packages rather than paying separately for AI/software – the 2026 guidelines continue this package-based approach. There’s room to move toward evidence-based recognition of AI that demonstrably improves outcomes or cost of care. 

Procurement: beginning to respond – tenders have appeared for AI-ready portable digital X-ray systems compatible with AI chest-X-ray interpretation. Next, specifications should evaluate evidence quality, interoperability, cybersecurity and lifecycle support, not just price or an “AI” label.” 

The reimbursement question is also closely linked to the way healthcare institutions assess value. “India also needs regulatory frameworks that will take into consideration the evolving nature of AI while at the same time ensuring patient safety. The procurement and reimbursement systems must move towards an outcome- and value-based approach rather than focusing only on the cost of a device,” says Kumar. 

Raichura sees reimbursement as the larger unresolved issue. “Regulation is catching up faster than reimbursement. CDSCO has evolved meaningfully, and India’s Medical Device Rules give a working framework. AI-specific regulatory guidance is still developing, but that is true globally. Reimbursement is the bigger lag. Most Indian health insurance and payment models were built for procedures and consultations, not for continuous AI-enabled care between visits or for the kind of point-of-care intelligence that can prevent a hospital admission. Until payment models evolve, adoption of AIenabled MedTech in India will be concentrated in segments that can absorb the cost without insurance support: government programmes, corporate hospitals, and out-of-pocket premium care.” 

For Dr Vankar, the pace of evolution remains an important concern. “India’s regulatory, reimbursement and procurement frameworks are evolving, but the pace remains slower compared with Western countries, particularly as AI-enabled MedTech is developing rapidly. There is a need for clearer pathways around AI validation, clinical evidence, regulatory approval, reimbursement and procurement, so that useful technologies can move from innovation to routine clinical practice more efficiently. A more responsive framework, developed in consultation with clinicians, MedTech companies and regulators, can help India adopt AI-enabled technologies faster while maintaining patient safety and clinical standards.” 

The issue is therefore no longer simply whether India has a regulatory framework. It is whether regulation, payment and procurement can evolve at a pace that allows clinically useful technologies to move from pilots into routine care. 

The road to the patient who needs it most 

The next phase of AI-enabled MedTech is likely to be defined less by standalone intelligent devices and more by connected systems that bring together diagnostics, monitoring, patient history and clinical intelligence. 

For Sharma, that could fundamentally alter the way healthcare is delivered. “The future of MedTech powered by AI would increasingly integrate diagnostics, continuous monitoring and predictive analysis of biological and patient level data. Such approaches would help detect diseases early and implement personalized care for the same. 

For India, the potential is even greater since the healthcare system creates large amounts of heterogeneous clinical data. With responsible management and translation of such data into intelligence, AI can facilitate the transition of the health care delivery system to become predictive, preventive and personalised. The focus should not be on innovation for innovation’s sake, but on delivering measurable clinical benefit and extending quality health care to all.” 

Dr Kapasi sees the opportunity in distributing clinical intelligence beyond major centres. “The next generation of devices will be more connected, predictive and embedded into clinical workflows. Instead of looking at one data point in isolation, devices will increasingly bring together imaging, vital signs, biomarkers, patient history and risk models to help clinicians identify problems earlier and make decisions with more context. 

For India, the bigger opportunity is distributed clinical intelligence. A primary health centre or district hospital may not have immediate access to every specialist, but it can have technology that helps a general physician identify a high-risk patient, interpret a diagnostic signal or decide when a referral is necessary. That could change healthcare delivery at scale by extending specialist support beyond major cities and tertiary hospitals, rather than waiting for specialist availability to expand at the same pace as demand.” 

Choudary outlines a future in which AI-enabled devices increasingly move into primary and rural care. 

“What’s coming: Point-ofcare diagnostics include handheld ultrasound with automated interpretation, smartphone retinal cameras for diabetic screening, AI-guided ECG patches, cartridge-based molecular tests. These bring specialist level assessment to primary and rural care. Continuous monitoring wearables and bedside sensors track vitals, glucose, and arrhythmias in real time, catching deterioration before it becomes an emergency. Ambient AI in clinics (mic/camera-based) auto-documents visits, cutting clinician paperwork. Imaging and pathology automation AI acts as a “first reader” for Xrays (TB, pneumonia), CT, mammography, and pathology slides, clearing normal cases and flagging suspicious ones. Closed-loop and assistive systems include automated insulin delivery, AI-guided surgical tools, decision support built into the device itself.” 

“What this means for India: it closes the specialist gap. Radiologists and pathologists are scarce and city-concentrated. AI lets a nurse at a rural PHC do screening that would otherwise need a referral. It enables screening at scale. TB, diabetic retinopathy, cervical/oral cancer, and cardiovascular risk are massive burdens here; cheap AI tools make mass screening programs viable. India already runs some for-TB chest X-rays and retinopathy. It also plays to India’s frugal-engineering strength. Devices built for patchy power, low bandwidth, and offline inference suit the market better than hospital grade Western equipment. Finally, it plugs into digital health infrastructure. With Ayushman Bharat Digital Mission and health IDs, device outputs could feed into longitudinal records enabling real follow-up and referral tracking instead of one-off tests.” 

The same shift is visible in the expectations around affordability and usability. Dr Sameer Vankar says, “The next generation of AI-enabled medical devices should be accessible, affordable and easy to use, so that they can be adopted across different levels of healthcare, not just advanced centres.Their outputs should be clinically meaningful and easy for clinicians to interpret, supporting doctors rather than replacing their judgement. They should also help both patients and doctors with follow-up, remote monitoring and long-term disease management, making care more continuous and proactive.” 

Dr Shetty also sees connected and predictive systems as a route to extending specialist expertise. “Next generation devices will be more connected, predictive and contextual, with the ability to pick up early warning signs and facilitate intervention when required. For India, the potential benefits would be to extend specialist expertise to remote areas.” 

For Chahal, this evolution is ultimately about integration rather than intelligence alone. “The real shift for the MedTech industry is therefore from innovation-first to evidence-and-integration. The winners will not simply be those building smarter devices, but those that can demonstrate clinical value, integrate into real hospital workflows and augment clinicians rather than replace them.” 

Goel similarly argues that the opportunity lies in moving beyond isolated applications. “Having said that, the larger opportunity lies in scaling these applications from individual use cases to broader clinical adoption. This will depend on generating robust evidence of clinical value across diverse patient populations and realworld care settings. With the right ecosystem in place, AI has the potential to help Indian MedTech improve access, affordability, and healthcare capacity, while also driving innovations that can be applied across other resource-constrained healthcare markets.” 

And for Raichura, the ultimate measure of this transition remains the patient. “The next generation will do more, not less. Devices will interpret, predict and increasingly take on defined parts of the clinical workflow directly where evidence supports it. For India, this could be significant. A community health worker equipped with the right diagnostic technology and clinical intelligence can do work that once required a specialist, a lab and weeks of waiting. She does it in minutes, for a patient she knows by name. 

The important design question, in my view, is not human or machine. It is whether the patient at the centre of that care retains agency, understanding and control over what happens to them and to their health data. Get that right, and AI in MedTech is one of the most important levers India has to close the access gap.” 

Ultimately, the success of AI-enabled MedTech will be measured not by the number of devices deployed, but by whether those devices help close some of the access gaps that make specialist care difficult to reach in the first place. The technology is becoming more capable. The more difficult task is making that capability clinically useful, economically viable, trusted by healthcare professionals and accessible beyond the country’s most advanced hospitals. That is ultimately the test for the next phase of AI-enabled MedTech: whether greater intelligence in the device translates into better, more accessible and more accountable care for the patient. 

Way forward 

The next leap will be about making AI-enabled devices not just smarter, but more useful, trusted and accessible. For India, that could mean taking specialist-level clinical intelligence closer to the patient. 

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