Beyond the Hype: Practical Applications of AI in Diagnostics for Indian Healthcare Leaders

AI in diagnostics is taking center stage, reaching far beyond traditional imaging into pathology, lab testing, genomics, and specialized disease screening. By harnessing machine learning to interpret a wide spectrum of clinical data—from medical imagery and genetic sequences to lab results and patient-reported symptoms—these solutions enable faster, more accurate disease detection. Powerful algorithms now pinpoint cancer cells in pathology slides, while predictive models flag subtle indicators of chronic illness through routine test patterns.

In this article, we’ll explore global innovations shaping diagnostic AI, the underlying technologies driving them, and the increasing relevance of these advances for healthcare leaders in India.

Global Examples of AI-Powered Diagnostics

  • Digital Pathology & Cancer Detection: Advanced AI algorithms in pathology help pathologists analyze biopsy slides for cancers and other diseases. For instance, Aiforia (Finland) offers CE-marked AI models for pathology; one model helps detect and quantify PD-L1 biomarkers in lung cancer tissue, assisting in identifying patients eligible for immunotherapy. In the UK, the NHS has begun adopting such tools via the PathLAKE consortium, indicating trust in AI for clinical diagnostics. Similarly, PathAI (USA) and Paige AI (USA) have developed deep learning models that can scan whole-slide images to find microscopic tumor foci or grade cancers, improving accuracy and reducing workload for pathologists. Early studies show these tools can shorten analysis time and reduce missed lesions when used as decision support.

  • Genomic Diagnostics: AI is also accelerating genomic analysis. Companies like Haystack Analytics (India) leverage AI to interpret complex genetic data for personalized medicine. By sifting through thousands of genetic variants, AI can help pinpoint mutations responsible for hereditary diseases or cancer subtypes much faster than manual methods. This is critical as genomics grows in importance for diagnosing rare diseases and tailoring treatments. In Japan, researchers are using AI on genomic and clinical data to predict disease risk likelihood of developing conditions like diabetes or certain cancers by analyzing genetic markers combined with routine health data. These AI models often employ advanced pattern recognition and even federated learning to aggregate insights from multiple databases without compromising privacy (more on federated learning later).

  • AI for Routine Lab Tests: Beyond complex genomics, AI is improving everyday lab diagnostics. A notable example is Piiko.io (India), which built a compact, AI-enabled machine for routine blood tests. Piiko’s device automates sample processing and uses AI-driven analytics to perform common tests (like CBC and biochemistry) at low cost. By optimizing reagent use and interpreting results with machine learning, it makes quality diagnostics accessible to smaller labs that cannot afford large analyzers. This kind of innovation illustrates how AI can democratize diagnostics by reducing cost and expertise barriers.
  • Oncology Diagnostics (Beyond Radiology): Several AI tools help detect cancers via endoscopy and other non-imaging diagnostics. In Japan, startup AI Medical Service (AIM) developed an AI system for endoscopy that analyzes live video from gastroscopy/colonoscopy to highlight suspicious lesions. Trained on over 200,000 endoscopic videos, the AI can flag potential early-stage cancers in real-time, helping endoscopists not to overlook subtle lesions. This is a boon in Japan, which has one of the world’s highest endoscopy rates for gastric cancer screening. The AI operates at high speed (analyzing an image in 0.02 seconds) and with ~94% accuracy in detecting cancer presence, showcasing how computer vision can assist even in optical diagnostics. Notably, the final diagnosis remains with the physician, and such tools are viewed as “second readers” to improve accuracy. Similarly, Iris Healthcare (Japan) created an AI device called “nodoca” to diagnose influenza by analyzing throat images and symptoms. Nodoca uses deep learning on 500,000 throat images and patient data to identify signs of flu within seconds, offering a pain-free alternative to swab tests. It exemplifies AI combining visual analysis with patient-reported data for rapid point-of-care diagnostics.
  • Specialty Screening (Ophthalmology & Cardiology): AI is excelling in screening for diseases in specialties like eye care and cardiology. In ophthalmology, automated retina scanners with AI can detect diabetic retinopathy or glaucoma early. Singapore’s national diabetic eye screening program uses an AI called SELENA+ (Singapore Eye Lesion Analyzer) to evaluate retinal photographs for disease; this system is deployed in a semi-automated mode to balance cost-effectiveness with accuracy. The AI flags cases that likely have disease for human specialists to review, greatly increasing productivity in screening large populations. In India, startups Artelus and Remidio have similar AI tools: Artelus provides deep learning models for diabetic retinopathy screening, and Remidio’s fundus-on-phone device can capture retinal images and automatically generate diagnostic reports on the smartphone. These innovations allow screening in remote clinics by non-specialists – a critical need in countries with uneven specialist distribution. In cardiology, AI-driven devices like the Japanese “super stethoscope” by AMI are emerging; this device records heart sounds and ECG in 10 seconds and uses AI to detect cardiac abnormalities, enabling remote diagnosis of heart disease. India’s Tricog Health has taken a related approach by using AI to interpret ECGs – Tricog’s platform analyzes ECG and echocardiogram data to identify heart attacks or arrhythmias and has touched over 10 million patients globally, illustrating how AI can scale expert cardiology interpretation to primary care settings.

Technologies Powering Diagnostic AI

Modern diagnostic AI solutions are driven by a robust set of technologies tailored to specific data types and use cases.

  • Deep Learning, particularly convolutional neural networks (CNNs), is the backbone of image-based diagnostics. These models excel at analyzing pathology slides, retina scans, and endoscopic videos by identifying complex visual patterns that may elude human eyes.
  • Natural Language Processing (NLP) plays a crucial role in parsing patient symptoms, clinical notes, and correlating textual data with imaging—helping systems derive deeper clinical insights.
  • Predictive Analytics and Classical Machine Learning are widely used to assess structured data, such as lab test results and vital signs, enabling early warning systems for cardiac events and chronic illnesses.
  • A notable innovation is Federated Learning, which enables AI models to be trained across decentralized hospital systems without sharing raw patient data. This privacy-preserving method allows for broader and more diverse datasets, improving model performance while safeguarding patient confidentiality. Projects like Eye2Gene have shown promising results using this approach to predict genetic mutations with high accuracy.

India’s Growing AI Momentum in Diagnostics

India’s diverse healthcare landscape presents both opportunities and challenges—and diagnostic AI is rising to meet them.

  • Niramai Health Analytix (Bangalore) is pioneering AI-based thermal imaging for early breast cancer detection. Their solution, Thermalytix, is portable, radiation-free, and operable by non-doctors, making it ideal for remote screening. It’s a prime example of frugal innovation—reducing costs while increasing access and impact, with the potential to improve survival rates by up to 4x through early detection.
  • Onward Assist (Hyderabad) offers an oncology decision-support platform that leverages AI to analyze histopathology images and reports. By prioritizing high-risk cases and quantifying tumor features, Onward has helped pathologists reduce reporting time by 30–40%—a game-changer for overburdened public hospitals.
  • The Tamil Nadu e-Governance Agency has developed ePaarwai, an AI-based mobile app for large-scale cataract screening. Using just a smartphone camera, it enables frontline health workers to detect cataracts in remote areas and refer only the confirmed cases for surgical consultation—optimizing limited specialist resources.
  • National initiatives like the Genome India Project and IndiGen are building indigenous genomic databases. These will serve as foundational data sources for AI models customized to India’s population, enabling precision diagnostics in hereditary and rare diseases.
  • The Indian Council of Medical Research (ICMR) has laid the groundwork for ethical AI development in healthcare by releasing comprehensive guidelines in 2023, signaling regulatory readiness for AI integration at scale.

Tailored, Localized AI in Action

Indian startups are designing AI solutions that reflect the country’s unique healthcare needs:

  • Artelus and Remidio have developed AI-based diabetic retinopathy screening tools suitable for rural deployment. Remidio’s fundus-on-phone system enables retinal imaging and diagnosis via smartphone, empowering non-specialist health workers in resource-limited settings.
  • DeepTek (Pune) provides “Augmento,” an AI platform that supports radiologists in interpreting X-rays and CT scans. With deployment in over 200 centers, it’s helping scale radiology services where expertise is scarce.
  • Tricog has revolutionized cardiology diagnostics by using AI to interpret ECGs and echocardiograms across thousands of clinics. Their platform has already touched over 10 million patients, enabling faster, more accurate heart attack detection—especially in primary care settings.

Global Benchmarks: How Leading Nations Are Shaping Diagnostic AI

Learning from countries with advanced healthcare systems offers a strategic lens for integrating AI into diagnostics. Each has adopted AI thoughtfully, aligning technology with infrastructure, clinician workflows, and patient needs.

🇬🇧 United Kingdom

The UK’s National Health Service (NHS) has been a global front-runner in adopting diagnostic AI. Initiatives like NHSX AI Lab and the PathLAKE consortium have funded large-scale trials across diagnostic workflows.

A notable example is the MASAI trial, one of the world’s first real-world validations of AI in breast cancer screening. Conducted in Sweden in collaboration with the UK, the study found that AI could safely replace one of two radiologists in mammogram double-reading—detecting more cancers while cutting workload by nearly 50%. In pathology, NHS trusts are adopting tools like Aiforia’s PD-L1 analysis, reinforcing institutional trust in AI as a clinical decision support tool.

The UK’s approach emphasizes rigorous validation and real-world evidence before widespread deployment—an approach India is beginning to mirror through pilot programs and data banks like the Tata Medical Center’s cancer image repository.

🇯🇵 Japan

Japan is witnessing a wave of clinician-led AI innovation in diagnostics. Startups like AI Medical Service and Iris Healthcare have built AI tools for endoscopy-based cancer detection and non-invasive flu diagnosis, respectively.

Culturally, Japan has positioned AI as a clinical assistant, not a replacement. This framing has led to broader physician acceptance. On the regulatory side, Japan has taken a proactive approach by building a national roadmap for AI in medical devices—something India has begun to emulate through ICMR’s ethical guidelines for healthcare AI.

🇸🇬 Singapore

Singapore demonstrates the power of integrating AI into national health programs. In its diabetic retinopathy screening initiative, SELENA+ AI is embedded into public clinics to scan retinal images. The model flags high-risk cases for human review—allowing clinics to screen more patients while maintaining accuracy.

Singapore’s strategy balances automation with cost-effectiveness through semi-automated workflows, which could serve as a blueprint for India’s public healthcare programs. The city-state also uses predictive AI to identify inpatients at risk of readmission, enabling preemptive interventions.

🇸🇪 Scandinavia

Nordic countries—Sweden, Denmark, and Finland—are leaders in digital pathology and system-level AI deployment. Sweden has launched several regional AI programs for chest X-ray analysis and mammography screening.

Denmark’s largest healthcare region recently moved to a cloud-based imaging infrastructure, enabling centralized deployment of AI tools across hospitals. This reflects a platform approach, consolidating data to maximize AI’s diagnostic utility. While India’s fragmented hospital IT landscape is a challenge, initiatives like the ABDM’s National Health Stack aim to lay a similar foundation for nationwide interoperability.

Key Takeaways for India

These global examples underscore four critical success factors for diagnostic AI adoption:

  1. Robust Digital Infrastructure – From imaging systems to data integration
  2. Regulatory Clarity – Clear pathways for AI validation and certification
  3. Clinician Buy-In – AI as a support tool, not a replacement
  4. Workflow Alignment – Seamless integration into day-to-day operations

For India, a strong public-private partnership model will be vital to replicate these successes. Encouragingly, momentum is building.

Challenges in Scaling Diagnostic AI in India

Despite growing adoption, India faces unique hurdles in bringing diagnostic AI to scale:

1. Data and Infrastructure Gaps

Many Indian labs still operate with analog systems—paper records, non-digital microscopes, or standalone machines. Without digital infrastructure, AI cannot be deployed. Public investment is needed in digital pathology scanners, cloud or edge computing capabilities, and integrated lab systems.

2. Regulatory Uncertainty

The 2023 ICMR guidelines are a starting point, but more clarity is needed on how AI tools are certified and monitored in clinical settings. Hospitals remain cautious due to liability concerns. There’s also a need for localized validation—AI models trained on Western datasets may not perform optimally on Indian populations.

3. Economic and ROI Pressures

High upfront costs for advanced AI solutions remain a barrier, especially for budget-conscious healthcare providers. For AI adoption to grow, vendors must offer flexible pricing models—such as pay-per-use, cloud-based deployments, or bundled solutions. ROI must be proven through metrics like time saved, diagnostic accuracy, or reduced unnecessary tests.

4. Cultural Acceptance and Training

Doctors and patients alike may view AI with skepticism. The key is building trust—through explainable AI (e.g., visual overlays showing what the AI “saw”) and hands-on training for clinicians and technicians. Positioning AI as an assistant—not a threat—will accelerate acceptance.

5. Privacy and Ethical Safeguards

AI in diagnostics handles highly sensitive data like biopsy images or genomic profiles. With India’s data protection laws still evolving, strong safeguards are essential. Federated learning and on-premise deployments can help mitigate risks. Adhering to ethical principles around fairness, accountability, and transparency is also crucial.

The Road Ahead: Building an AI-Enabled Diagnostics Ecosystem

Despite these challenges, India is heading in the right direction. Government support for “Make AI in India, Make AI work for India” reflects strong national intent. With the rollout of digital health IDs under ABDM and increasing collaboration between tech firms, hospitals, and regulators, the foundation is being laid for a robust, AI-powered diagnostic ecosystem.

By embracing innovation while addressing ground realities, India has the opportunity not just to catch up—but to lead—in scalable, equitable diagnostic care.