AI is revolutionizing healthcare, from diagnostic imaging and clinical triage to personalized treatment plans. But as we accelerate innovation, one critical question remains: who truly benefits? Unless deliberately designed for inclusivity, AI risks reinforcing healthcare disparities instead of resolving them. To ensure AI empowers every patient, from metropolitan cities to villages in rural areas, we must embed equity into every layer of its development and deployment.
The Equity Gap in AI-Driven Healthcare
Despite AI’s promise, its real-world implementation often reveals a troubling pattern: systemic inequities are baked into the models that are meant to transform care. These inequities are rooted in:
- Data Disparities: Most AI models are trained on datasets heavily skewed toward certain populations - often urban, upper-income, and digitally connected. This underrepresents the realities of rural, marginalized, and tribal communities. Roughly 65% of India’s population lives in rural areas, but only 23% of rural Primary Health Centres (PHCs) have reliable internet or digitized records.
- Deployment Biases: Even widely adopted tools can exhibit racial or demographic biases. For example, studies have shown that pulse oximeters may be less accurate for individuals with darker skin tones, illustrating how device calibration and training data diversity matter.
- Infrastructure Gaps: In India, AI is disproportionately deployed in tier-1 cities and private hospitals, if at all. 75% of AI healthcare pilots in India are concentrated in urban, private settings, under 15% are deployed in public or rural systems. Public health infrastructure, especially in rural areas, remains far from digitization. Language diversity and unstructured formats pose further technical hurdles. Less than 5% of available Indian health AI datasets include language or demographic diversity beyond Hindi and English.
- Socioeconomic Blind Spots: Caste, gender, and income are rarely included in AI fairness assessments, despite being key social determinants of health in India. A 2022 AI audit in India showed that nearly 40% of AI diagnostic tools underperform on women and darker-skinned populations due to underrepresentation in training data.
Principles of Responsible AI in Healthcare
To address these disparities, AI innovation must go beyond technical excellence and embrace principled development:
- Fairness: Ensure model accuracy across demographic subgroups
- Transparency: Enable explainable decision-making for providers and patients
- Privacy: Safeguard patient data, especially in low-literacy or low-infrastructure settings
- Accountability: Audit AI systems to flag and fix biased outcomes
These principles are especially critical in a country like India, where healthcare delivery intersects with complex social, cultural, and linguistic realities.
Strategies to Empower Equity with AI
Building an equitable AI ecosystem demands both technical innovation and policy foresight:
- Diversify the Data
– Create and curate high-quality, de-identified datasets from public hospitals, community health centers, and primary healthcare centers
– Develop open-access Indian health datasets with diverse representation
– Use NLP models tailored to India’s multilingual population to structure clinical narratives and health records
- Design for Inclusion
– Increase participation of underserved communities in design processes
– Develop AI interfaces in local languages, accessible on low-end devices
– Prioritize edge AI solutions that work offline in rural environments
- Federate the Model, Not the Data
– Use federated learning frameworks to train models on distributed hospital data without compromising privacy
– Promote state-level AI collaboratives to reduce data centralization risks and promote regional inclusivity
- Align with Public Health Policy
– Integrate AI equity goals into India’s Ayushman Bharat Digital Mission (ABDM) and National Digital Health Mission (NDHM) frameworks
– Incentivize startups and research institutions working on low-cost, high-impact AI tools for primary care settings
A Snapshot of India’s Healthcare AI Market
India’s ‘AI in healthcare’ sector is poised for exponential growth – currently valued at around USD 750 million (2024) and expected to grow at over 40% CAGR through 2027. Globally, the AI healthcare market is projected to hit USD 188 billion by 2030
Key Trends Include:
- 83% of Indian healthcare leaders plan to increase investments in AI tools over the next 2 years (PwC India, 2023)
- Over 200+ Indian health startups are leveraging AI in diagnostics, triage, and remote care
- In the ‘Diagnostics AI’ domain, Qure.ai uses deep learning to automate interpretation of radiology exams like X-rays, CTs and Ultrasounds scans for time and resource-strapped medical professionals, enabling faster diagnosis and speed to treatment; Niramai has developed a low-cost portable AI device that automates detection of breast cancer
- For remote patient monitoring, Dozee uses AI to enable real-time monitoring of health vitals and insights, Tricog uses AI to track cardiac analytics and provide timely diagnoses of Cardiovascular Diseases
- Public health AI integration through initiatives like ABDM are gaining traction
- Challenges: Under-digitized rural areas, poor data interoperability, and limited AI literacy among frontline workers
- Opportunities: Multilingual tools, federated learning, equity-focused datasets, AI-driven telemedicine, Internet of Medical Things (IoMT) Integration
Case Studies: AI for Equity in Action
Despite a general lack of focus on integrating fairness within AI models to ensure equitable healthcare, it is worth highlighting initiatives that have taken steps to integrate fairness metrics into their models. Â
- Stanford’s COVID-19 ICU Triage Tool
During the height of the COVID-19 pandemic, when healthcare systems were overwhelmed with requirements for ICUs, Stanford University researchers developed a data-driven triage algorithm to help hospitals allocate limited ICU beds more equitably and efficiently by explicitly including social vulnerability indices, ensuring equity in resource-constrained care. Stanford’s model went beyond traditional clinical risk scores (like SOFA or APACHE II) by integrating social vulnerability indicators into its triage algorithm. These included socioeconomic status, racial/ethnic background, ZIP code-based health disparities and preexisting access-to-care gaps. The tool became a reference model for ethically informed AI deployment during public health crises.
- Mobile AI Diagnostics in Rural India
In India, roughly 65% of the population lives in rural areas, where access to specialists, especially radiologists and ophthalmologists, is severely limited. Several health tech startups have turned to AI to bridge this gap using smartphone-based diagnostic tools.
Startups like Remidio and Qure.ai have deployed AI-assisted tools in rural Maharashtra and Bihar, enabling point-of-care diagnosis of diabetic retinopathy and TB – conditions that often go undiagnosed due to a lack of specialists.
Remidio uses a smartphone-based fundus camera with embedded AI to detect diabetic eye disease. The device can be operated by minimally trained health workers at PHCs and it generates on-the-spot diagnostic reports, without the need for an internet connection. Remidio has screened over 1 million patients, many in remote regions, and identified early-stage disease in patients who otherwise would not have seen an ophthalmologist.
These examples demonstrate how inclusive design and targeted deployment can dramatically improve healthcare outcomes, especially in underserved populations.
Conclusion: Engineering Equity into Innovation
AI has the power to revolutionize healthcare, but only if we are intentional. The path to equitable healthcare doesn’t begin with algorithms. It begins with asking: “Who is missing from the data?” “Whose voice is excluded from design?” “Who can’t access what we’ve built?”
To deliver equitable healthcare through AI, we must engineer fairness into the code, represent all people in the data, and embed fairness into the culture of innovation.
The future of healthcare will not be equitable by default, it will be equitable by design.
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