Pathways for AI-Enabled Healthcare Reform

This editorial is based on “Build AI infrastructure to turn daily clinical data into a learning system” which was published in The Hindu on 14/10/2025. The article brings into picture that the transformative potential of AI in healthcare rests not on algorithmic complexity but on institutional control over data and feedback systems, where continuous clinician-led learning ensures adaptive and reliable outcomes.

For Prelims: Artificial intelligence in healthcare, Precision medicine, Natural Language Processing, IndiaAI Mission,  AIKosh (Dataset Platform),  Tele MANAS, Digital Personal Data Protection Act, 2023, Ayushman Bharat Digital Mission.  

For Mains: Key Applications of AI in Healthcare, Key Issues Associated with AI in Healthcare.  

The transformative potential of artificial intelligence in healthcare hinges not on algorithmic sophistication alone, but on institutional control over data infrastructure and continuous learning systems. Current healthcare AI implementations often rely on externally trained models that lack the feedback mechanisms necessary for real-time correction and local adaptation, creating a dangerous gap between clinical reality and system performance. The path to effective healthcare AI lies in building proprietary feedback loops where clinical expertise directly refines algorithmic performance, creating compounding improvements rather than static solutions. 

What are the Key Applications of AI in Healthcare?  

• AI in Advanced Medical Imaging and Diagnostics: AI algorithms, especially deep learning models, revolutionize diagnostics by analyzing medical images faster and more accurately than the human eye, crucial in a country with a severe shortage of specialists like radiologists.  
  • This accelerates the detection of conditions such as cancer, diabetic retinopathy, and cardiac issues, improving patient outcomes through early intervention. 
  • For instance, Indian startup Qure.ai's qXR system rapidly analyzes chest X-rays to detect abnormalities like tuberculosis.  
    • The India AI in Medical Diagnostics Market is forecasted to grow at a CAGR of 12.72% from 2025 to 2034, underscoring this trend's accelerating adoption and massive market potential. 
• AI-Powered Drug Discovery and Research: AI is a game-changer in accelerating and de-risking the lengthy, costly process of drug discovery and pharmaceutical research by simulating molecular interactions, predicting drug-target binding, and optimizing clinical trial design.  
  • This shift is critical for India's pharmaceutical industry, moving it from a generics-focused model to one of novel drug innovation and biosimilar development. 
  • For instance, Excelra, a Hyderabad-based company, leverages AI/ML and proprietary data to accelerate drug discovery for global clients.  
    • The number of patents filed by the Indian pharma sector surged from 1,590 in 2013 to 8,793 in 2023, reflecting enhanced R&D capabilities, partly driven by new digital technologies and strategic collaborations. 
• Personalized Medicine and Genomics Integration: Precision medicine, enabled by AI, moves healthcare beyond the "one-size-fits-all" approach by integrating a patient's genetic, behavioral, environmental, and clinical data to tailor treatment protocols, especially for complex diseases like cancer.  
  • This context-aware personalization is vital for India's diverse and socioeconomically varied population, where environmental factors often outweigh genetics. 
  • For instance, Bangalore-based Oncostem Diagnostics uses AI on genomics-based data for personalized breast cancer therapy and recurrence prediction.  
    • An increasing focus in India is on behavioral and socioeconomic determinants, with the AI-driven personalized medicine market segment projected to reach $500 billion globally by 2027, signaling massive localized potential. 
• Automation of Administrative and Operational Workflows: AI-driven solutions streamline the non-clinical, administrative burdens on hospitals, including patient scheduling, billing, claims processing, and electronic health record (EHR) management via Natural Language Processing (NLP).  
  • This operational optimization is essential for improving hospital cash flow, reducing errors, and allowing clinical staff to dedicate more time to direct patient care. 
  • For instance, Eka.care's AI medical scribe, Eka Scribe, converts doctor-patient conversations into structured prescriptions in real-time.  
• Expanding Healthcare Access through Telemedicine: AI serves as the backbone for advanced telemedicine and remote patient monitoring (RPM) platforms, using predictive analytics and conversational AI (chatbots) to extend quality healthcare to the population living in rural areas.  
  • It provides triage, virtual assistance, and specialist consultation without the need for physical travel. 
  • For instance, Tricog Health's AI platform, InstaECG, provides real-time ECG interpretation, enabling instant diagnosis of heart attacks even in remote clinics without cardiologists.  
    • The Union Cabinet allocated ₹10,372 crore to the IndiaAI Mission, which specifically targets application development in sectors like healthcare, underscoring the government's push for widespread digital health access. 
• Predictive Analytics for Public Health and Epidemics: AI's predictive analytics capabilities are crucial for proactive public health management by analyzing epidemiological data, social media trends, and environmental factors to forecast disease outbreaks, hospital admissions, and resource needs (e.g., ventilator and bed allocation). 
  • This enables early intervention and targeted public health campaigns. 
  • AI-based risk modeling is increasingly used to predict non-communicable diseases. For instance, NITI Aayog is working with Microsoft and Forus Health to roll out a technology for early detection of diabetic retinopathy as a pilot project. 
  • The growing AI-Kosh (Dataset Platform) has over 3,000 datasets for training AI models, providing a foundational data ecosystem for public health predictive analytics. 
• AI in Mental Health and Psychological Support: AI-powered conversational agents (chatbots) and AI-enabled telepsychiatry platforms are democratizing mental health support by offering 24/7, anonymous, and affordable initial screening, emotional support, and Cognitive Behavioral Therapy (CBT) techniques.  
  • This is essential given the high stigma and extremely low psychiatrist-to-patient ratio in India. 
  • The upgraded Tele MANAS App (India's national tele-mental health programme) is an example of expanding virtual support.  

What are the Key Issues Associated with AI in Healthcare? 

• Algorithmic Bias and Health Equity Concerns: AI models, trained on unrepresentative data largely from urban, well-documented, or Western populations, often inherit and exacerbate existing health inequities, leading to systematically inaccurate diagnoses for marginalized groups.  
  • This lack of fairness directly impacts vulnerable populations, defeating the goal of inclusive healthcare. 
• Data Privacy, Security, and Consent: The massive requirement for sensitive personal health data to train and operate AI systems creates profound risks around privacy breaches, unauthorized profiling, and loss of patient trust, especially in a digital ecosystem undergoing rapid expansion.  
  • Robust, dynamic security measures are essential but often costly. 
  • The Digital Personal Data Protection Act, 2023, is a step toward a legal framework, but AI-specific legislation is still lacking.  
• Lack of Data Standardization and Interoperability: Healthcare data in India is highly fragmented, existing in disparate, non-communicating "silos" across numerous hospitals, clinics, and labs, often in non-standardized formats or even paper records.  
  • This lack of interoperability starves AI systems of the rich, unified data needed for robust training and effective real-time clinical deployment. 
  • While the Ayushman Bharat Digital Mission (ABDM) promotes data interoperability, adoption of international standards like HL7/FHIR is still low, particularly in legacy systems.  
  • Physicians often manually lug paper files and disparate digital reports, a key friction point AI cannot overcome until data flow is seamless. 
• Regulatory and Accountability Ambiguity: The dynamic, self-learning nature of AI models challenges traditional, static regulatory frameworks for medical devices, creating a "black box" problem where the decision-making logic is non-transparent.  
  • This lack of clear legal and clinical accountability is a major deterrent to widespread clinical adoption. 
    • Concerns around data quality, misinformation, clinical safety, and ethical or regulatory risks are growing.  
    • A recent example is of a man who was hospitalized after following diet advice generated by ChatGPT, highlighting the dangers of unverified AI-driven medical guidance. 
  • The Medical Device Rules, 2017, do not fully address the unique aspects of Software as a Medical Device (SaMD) with AI/ML. 
• High Implementation Cost and Skill Gap: The initial investment in high-performance computing (GPUs), specialized engineering talent, and integration with legacy hospital IT systems is prohibitively high for many healthcare providers, particularly in resource-constrained Tier 2/3 cities and rural centers. 
  • The IndiaAI Mission is addressing this by providing high-end GPUs at subsidized rates (₹65 per hour), yet most Indian companies still allocate only 2% of revenue to technology.  
    • The cost of implementing AI in diagnostics can be between $40,000 to $1,000,000. 
• Data Scarcity and Quality in Rural Areas: While there's a volume of data in major urban centers, there's an acute scarcity of high-quality, annotated clinical data from rural and smaller towns.  
  • This absence makes it nearly impossible to train effective AI models tailored to the unique epidemiological and logistical challenges of the majority of India. 
  • For instance, despite the massive data being generated, it is estimated that only 1% of collected data is currently analyzed due to a lack of computational infrastructure and trained personnel.  
    • NITI Aayog also highlighted the absence of enabling data ecosystems as a major hurdle. 

ICMR Guidelines for Ethical AI Use in Healthcare:  
In March 2023, the Indian Council of Medical Research (ICMR) issued the “Ethical Guidelines for Application of Artificial Intelligence in Biomedical Research and Healthcare”, establishing ten core ethical principles centered around patient welfare and responsible innovation. 

Ten Guiding Principles: 

• Accountability and Liability: Conduct regular audits to evaluate AI performance, with results made publicly available to ensure transparency and responsibility.
  
• Autonomy: Maintain human oversight in all AI-assisted decisions and secure informed consent from patients, clearly communicating potential risks and limitations.
  
• Data Privacy: Safeguard patient privacy and personal data integrity throughout every phase of AI development and deployment.
  
• Collaboration: Encourage cross-disciplinary and international collaboration to promote the ethical and effective advancement of AI technologies in healthcare.
  
• Safety and Risk Minimization: Implement measures to prevent misuse, strengthen data security, and mandate ethical reviews by relevant committees before deployment.
  
• Accessibility, Equity, and Inclusiveness: Ensure equitable access to AI-driven healthcare infrastructure, addressing disparities caused by the digital divide.
  
• Data Optimization: Improve data quality and representation to minimize algorithmic biases and technical inaccuracies in AI systems.
  
• Non-Discrimination and Fairness: Promote fairness and inclusivity by ensuring AI tools are designed and deployed without bias, ensuring equal access for all users.
  
• Trustworthiness: Build confidence in AI systems through validation, reliability, ethical compliance, and adherence to legal standards.
  
• Transparency: Enable clinicians and researchers to assess AI validity and reliability by ensuring openness in methodologies, data sources, and performance metrics. 

What Measures can India Adopt to Enhance Integration of AI in Healthcare?  

• Mandate Semantic Data Interoperability: Establish a national technical standard for healthcare data that goes beyond basic data exchange (foundational and structural) to achieve semantic interoperability.  
  • This requires mandating the use of SNOMED CT for clinical terminology and LOINC for lab results across all public and private Electronic Health Records (EHRs) linked to the Ayushman Bharat Digital Mission (ABDM).  
  • This uniform data language is non-negotiable for training generalizable and robust AI models across diverse hospital systems. 
• Implement Federated Learning Architectures: To address data privacy concerns while ensuring AI models have access to diverse datasets, India should pivot towards federated learning.  
  • This architecture allows AI models to be trained locally on segregated hospital data without the data ever leaving the premises, only exchanging the model parameters.  
  • This Privacy-Preserving ML approach maintains data security, adheres to the Digital Personal Data Protection Act (DPDP Act), and ensures models are exposed to the full spectrum of Indian patient heterogeneity. 
    • Also, the Digital Information Security in Healthcare Act (DISHA) is a proposed Indian law aimed at regulating the collection, storage, and use of digital health data. 
• Establish Regulatory Sandboxes: Create regulatory sandboxes under the ICMR or CDSCO where high-risk AI medical devices can be tested in real-world clinical environments for a fixed period with human oversight, before being granted full market approval.  
  • This adaptive governance approach allows regulators to understand the AI's real-time performance and drift over time, moving beyond traditional, static regulatory checks for software and building dynamic trust among clinicians. 
• Curate Inclusive and Synthetic Datasets: Actively curate and annotate diverse national datasets for AI training, with a specific focus on underrepresented rural, socio-economic, and regional disease patterns to mitigate algorithmic bias. 
  • Simultaneously, government and academic bodies should invest in Generative Adversarial Networks (GANs) to create high-fidelity synthetic data that augments data scarcity for rare conditions or underrepresented patient demographics, ensuring fairness and equity in AI-driven diagnostics. 
• Clinical AI Literacy Mandate: Integrate mandatory AI literacy and critical evaluation modules into the undergraduate medical (MBBS), nursing, and paramedical curricula, as well as into mandatory Continuing Medical Education (CME) for practicing physicians. 
  • The focus must be on algorithmic transparency, understanding model limitations, and calibrating trust to prevent dangerous automation bias, ensuring the human clinician remains the final point of accountability. 
• Incentivize AI Co-Creation at the PHC Level: Launch targeted public funding programs and hackathons, possibly through the IndiaAI Application Development Initiative, that mandate partnerships between tech startups and Primary Health Centers (PHCs) in underserved areas.  
  • These programs should co-create AI solutions with frontline workers like ASHA and ANM workers, ensuring the technology is contextually relevant, local-language enabled, and addresses genuine last-mile challenges like triage and remote screening. 
• Establish an Explainable AI (XAI) Standard: Develop a national standard that requires all clinically deployed AI solutions to provide clear, understandable justifications for their output, moving away from opaque ’black box’ models.  
  • This XAI standard should be auditable by clinicians, detailing the features and data points that contributed most to a diagnosis or treatment recommendation, thereby boosting physician trust and making the AI's logic defensible in a clinical and legal context. 

Conclusion:

The promise of AI in healthcare rests not merely on technological sophistication but on ethical, inclusive, and interoperable integration within India’s health ecosystem. By prioritizing data interoperability, clinical feedback loops, and transparency, India can transform AI from a diagnostic tool into a public health equalizer. Effective regulation and human oversight must remain central to ensure accountability and patient safety. With the IndiaAI Mission as a catalyst, the country stands poised to democratize precision healthcare for all.

Drishti Mains Question:

Artificial Intelligence (AI) has the potential to make healthcare in India more equitable and efficient, yet it also raises critical concerns regarding ethics, accountability, and inclusion. Discuss.

UPSC Civil Services Examination, Previous Year Question (PYQ)  

Prelims:

Q. With the present state of development, Artificial Intelligence can effectively do which of the following? (2020)

1. Bring down electricity consumption in industrial units  
2. Create meaningful short stories and songs  
3. Disease diagnosis  
4. Text-to-Speech Conversion  
5. Wireless transmission of electrical energy  

Select the correct answer using the code given below:  

(a) 1, 2, 3 and 5 only  

(b) 1, 3 and 4 only  

(c) 2, 4 and 5 only  

(d) 1, 2, 3, 4 and 5  

Ans: (b)

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Mains:

Q.1 What are the areas of prohibitive labour that can be sustainably managed by robots? Discuss the initiatives that can propel the research in premier research institutes for substantive and gainful innovation. (2015)