Harnessing AI to Overcome Healthcare Barriers in Rural India
By Arunima Rajan
HE examines how AI can specifically address healthcare challenges in rural India.
In rural India, healthcare is a constant battle. Millions of people live with limited access to medical care, facing not just the absence of healthcare professionals but also the burden of outdated infrastructure. These aren't just challenges—they are the reasons behind delayed treatments and preventable diseases that continue to take a toll on lives.
Enter Artificial Intelligence (AI), a technology many believe could change the game. AI's potential to enhance diagnostics, manage resources more efficiently, and extend healthcare services to the remotest areas is promising. But the real question is: how do we integrate this powerful tool into the existing healthcare system in a way that genuinely benefits those who need it the most?
Healthcare Challenges in Rural India
"The COVID-19 pandemic was a wake-up call to spruce up our primary healthcare services, yet there have been no additional investments in the sector. In the absence of accessible, affordable and quality healthcare services, people in rural India, especially those living in hills, forests and deserts, continue to fend for themselves when faced with an illness – often delaying care till the disease becomes severe, not seeking care at all or seek care from informal, poor quality providers. Even when they are able to seek healthcare, they are mistreated, receive poor quality healthcare and often become indebted in the process," says Pavitra Mohan, Executive Director, Basic HealthCare Services, Vidya Bhavan Rural Institute (VBRI), Udaipur.
The challenges in rural healthcare aren't just limited to patients; they also weigh heavily on the shoulders of healthcare professionals. Shankul Dwivedi, a Medical Officer at CHC Rampur in Baghelan, Satna, Madhya Pradesh, points to the harsh realities doctors face in these areas—violence, inadequate housing, and crumbling infrastructure. These factors deter many from choosing to work in government hospitals here. Despite efforts to bring in specialists, lacking such professionals often means patients must be referred to district hospitals for care. Yet, Dwivedi sees a silver lining in the form of AI, which he believes could transform rural healthcare by empowering even technicians to diagnose conditions like tuberculosis and breast cancer, often without the need for a physician.
AI's Potential in Rural Healthcare
Building on this, Rajiv Pathni, founder of Praar Consulting, emphasises that AI's role could extend far beyond diagnostics.
"Time and distance are the biggest hurdles in rural healthcare," says Pathni, reflecting on insights he shared with the FICCI-Deloitte telemedicine panel some 15 years ago. He breaks down these challenges into various aspects: the critical minutes during emergencies, the long hours spent travelling to healthcare facilities; the days are taken away from livelihood activities, and the many forms of distance—geographic, social, gender, and financial—that all create barriers to accessing care.
In rural India, the healthcare system leans heavily on public infrastructure—ranging from Health and Wellness Centers (HWCs) to Primary Health Centers (PHCs), Community Health Centers (CHCs), and District Hospitals (DHs). Yet, there's a glaring shortage of qualified healthcare professionals, particularly specialists, in these regions. The challenges go beyond just a lack of human resources. Rural healthcare systems need comprehensive improvements. While the staff are often highly motivated, they require ongoing training and upskilling. Healthcare facilities struggle with inconsistent access to essential medicines, the need for frequent referrals for diagnostics, and the limited availability of specialists. The scarcity of trained medical personnel remains a pressing issue that underscores the broader systemic weaknesses in rural healthcare delivery.
Rajiv Pathni has noticed tangible progress during his field visits in recent years. Rural areas now enjoy better connectivity, improved healthcare infrastructure, and greater access to medical equipment and diagnostic services. Government initiatives like PMJAY, ABHIM, ABDM, Kayakalp, NQAS, and IPHS have strengthened public healthcare. The work of ASHAs and ANMs remains essential to delivering care in these regions, and the introduction of Community Health Officers (CHOs) has significantly expanded services at Health and Wellness Centers (HWCs), helping to reduce out-of-pocket expenses for patients. Empowering community representatives through JAS and RKS has also led to more effective localised solutions.
Pathni is convinced that Artificial Intelligence (AI) could be a game-changer in optimising resource distribution within rural healthcare. AI's ability to rapidly process and adapt to large datasets makes it well-suited to expanding healthcare coverage, enhancing access to quality care, and building more robust, people-centred health systems. He suggests mapping populations, patients, diseases, and healthcare infrastructure, along with supply chains and referral pathways, would allow AI to focus efforts on underserved and vulnerable communities, ensuring equity in service delivery.
With the healthcare sector generating 30% of the world's data, AI can leverage information from healthcare facilities, PMJAY, and surveillance systems to identify needs and match them with appropriate resources. AI could also revolutionise the management of Essential Drug List (EDL) and Essential Medicines List (EML) supply chains by predicting demand, pinpointing bottlenecks, and reallocating surplus resources to prevent waste. Predictive analytics could be used to deploy strategically and pre-position both human and material resources in anticipation of healthcare crises.
Pathni underscores that AI tools are powerful force multipliers, capable of democratizing healthcare by enabling non-physicians to perform essential diagnostic work without requiring specialist input. Technologies like Niramai and Qure.ai have already shown promise in early cancer and tuberculosis diagnosis, while indigenous AI tools are proving effective in screening for diabetic retinopathy. Additionally, AI-driven solutions like Brainsight and Wysa have made strides in diagnosing and managing mental health conditions, illustrating AI's potential across a broad spectrum of healthcare needs.
Enabling Targeted Healthcare Interventions
AI can revolutionize patient care in rural India by making the healthcare process more efficient and targeted. For instance, AI-driven tools can help select patients for referrals, reduce the reliance on specialists through Clinical Decision Support Systems (CDSS), and improve the effectiveness of telemedicine consultations. By predicting which patients are at high risk or likely to miss follow-ups, AI can enable more focused healthcare interventions, ensuring that those who need care the most are not left behind.
Telemedicine, a vital tool for expanding healthcare access in rural and underserved areas, still faces significant challenges. Aligning the availability of specialists, ensuring patients have access to necessary infrastructure like internet and electricity, and managing the overwhelming clinical responsibilities that specialists often face are all hurdles that need addressing. AI can streamline this process by optimising telemedicine consultations, routing cases to the right centres based on availability, and supporting a district-wide, AI-enabled, hub-and-spoke model that could improve appointment satisfaction rates.
CDSS, supported by AI, are particularly valuable in rural settings where clinicians may not have immediate access to specialists or comprehensive resources. These systems provide evidence-based guidance, helping reduce subjectivity and cognitive biases in medical decision-making. While the government has introduced Standard Treatment Guidelines (STGs) and Clinical Workflow Frameworks (CWFs) to promote rational medical practices, these tools can be cumbersome and difficult to access at the Primary Health Center (PHC) and Health and Wellness Center (HWC) level AI-based CDSS offer a more practical alternative, accessible even to healthcare staff in peripheral areas. Tools like ThinkMD, for example, allow non-physician users to perform clinical assessments that yield a 40-60% improvement in outcomes.
"Follow-up care remains a significant challenge, particularly for managing non-communicable diseases (NCDs), infectious diseases (IDs), and maternal and child health (MCH). AI can play a critical role here, identifying patients who have missed follow-ups and predicting who might miss future appointments. This enables healthcare providers to intervene early and focus on at-risk people. AI-powered population-based interventions can also map beneficiaries, identify at-risk individuals, and analyse disease trends. In Rajasthan, for example, Khushibaby works with the government to track primary health programs and deliver data-driven care, while Kiya.ai supports MCH services through AI-based tools.
Moreover, AI's potential extends to auditing and improving fraud control mechanisms, which is crucial for preventing resource leakages in programs like Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY). By ensuring that resources are used effectively and ethically, AI can help strengthen the overall integrity of rural healthcare delivery systems," concludes Pathni.
Case Studies: Real-World Applications of AI in Rural Healthcare
CureBay
CureBay is a digital healthcare platform that provides primary healthcare services to remote areas through physical e-clinics and a technology platform.
"AI can significantly enhance early disease diagnosis in remote areas by analysing vast amounts of patient data swiftly and accurately. AI/ML algorithms, such as Learner Regression Classifier (LR) and Naïve Bayes (NB), can be employed to sift through patient histories, symptoms, and diagnostic reports to identify patterns that may indicate the onset of diseases. These algorithms are particularly valuable in detecting conditions that might be missed during initial assessments due to the lack of specialised medical professionals in these areas. The platform's integration with Optical Character Recognition (OCR) solutions further aids in accurately interpreting medical records and tests, ensuring that no critical information is overlooked," says Priyadarshi Mohapatra, founder and CEO of CureBay.
CureBay's platform, which is 100% open-source and cloud-enabled, also incorporates IoT-enabled devices approved by CE and FDA. These devices play a crucial role in monitoring patient's health in real time, sending data directly to the platform, where AI can assess risk levels and trigger early warnings. This allows for proactive interventions, potentially preventing the progression of diseases before they become severe.
The impact of this technology is already being felt across CureBay's network of clinics in Odisha and Chhattisgarh, where it has facilitated healthcare for thousands of rural patients. By the end of 2024, CureBay plans to expand its reach to over 250 e-clinics.
CureBay uses AI-powered diagnostic tools that analyse symptoms and medical images, offering real-time insights. Additionally, AI-driven virtual assistants suggest potential diagnoses and treatment options during telemedicine consultations, enabling more accurate and timely care. This approach empowers healthcare workers in rural areas to deliver better patient outcomes despite limited resources. "CureBay incorporates CE and FDA-approved devices for conducting over 40 tests, including ECG, lipid profile, and diabetes tests, ensuring high-quality healthcare. One of the critical objectives of this innovation is to enhance the affordability and accessibility of healthcare services for rural patients. Various apps have been developed for doctors, pharmacists, diagnostic partners, and hospitals to achieve this, streamlining healthcare delivery. The extensive usage of AI/ML algorithms such as Learner Regression Classifier (LR), Naïve Bayes (NB), and others, along with OCR solutions, contributes to precise healthcare provision. The platform collates comprehensive patient data, utilising AI/ML models to enhance operational efficiency, enable early warnings, and ensure better patient care," adds Mohapatra.
Wysa
Jo Aggarwal, founder of Wysa, explains that in rural India, accessing mental health support presents distinct challenges, including stigma, a shortage of trained professionals, and inadequate infrastructure, often leaving people without the help they need. To address these barriers and make mental health care accessible to everyone, Wysa, an AI-powered mental health support app, has been developed. Currently, over 6 million people use Wysa, with about a million users in India. The goal is to increase this number to 60 million by 2035, with more than half of them in India.
Wysa offers cognitive behavioral therapy (CBT) support for anxiety and depression, with its 'man ka coach, badle soch' feature providing many Indian users their first experience of a non-judgmental space to explore their emotions and develop positive coping strategies. However, Wysa doesn't rely solely on AI; it also incorporates human interaction at critical points to ensure that users receive the necessary support when it's most needed. Over the past eight years, Wysa has gained valuable insights from global work and is now applying these to co-create solutions specifically for rural India. The goal is to foster mental resilience and encourage positive behavioural changes, addressing the gaps left by traditional mental health services and creating a more supportive and inclusive environment for all.
"In rural areas, stigma, lack of awareness, and infrastructure challenges limit access to mental health services. At Wysa, we're adapting by developing low-bandwidth versions of our app and exploring SMS and WhatsApp-based services to ensure support even with limited internet access. By offering resources across multiple platforms and in local languages, we're committed to inclusive care. Our 'human in the loop' approach, combined with AI, creates a comprehensive support system for rural populations," says Rhea Yadav, Head of Impact at Wysa.
AiSteth
AiSteth, an AI-powered stethoscope created by Ai Health Highway Inc., is designed to facilitate early detection of cardiac and respiratory disorders in primary healthcare settings. According to Dr (Maj) Satish S Jeevannavar, the founder of Ai Health Highway, the company was established in 2019 and seeks to reduce premature deaths from non-communicable diseases by 30% by 2030. In a country where many people must travel significant distances for specialised medical care, AiSteth aims to address this pressing healthcare challenge.
The AiSteth Murmur Analysis Platform (AsMAP) is designed to detect murmurs and screen for valvular heart disorders. It has been deployed across 19 primary healthcare clinics in rural Maharashtra as part of the Smart PHCs initiative. Presently, AiSteth is used by MBBS doctors, nurses, and ASHA workers in primary healthcare centres, screening camps, and door-to-door campaigns. This technology aids in screening and triaging patients and identifying those who require further evaluation and treatment by cardiologists.
Over 38,000 patients have been screened in the 18 months since the launch of AsMAP. Ai Health Highway is also expanding its focus to other clinical areas within cardiology and respiratory disorders. When scaled up, such innovations have the potential to significantly enhance the early detection and management of cardio-respiratory disorders, particularly in areas lacking diagnostic facilities and specialists.
Challenges and Limitations of Implementing AI in Rural Healthcare
Dr Jeevannavar highlights the challenges AI-driven healthcare solutions face, noting that MedTech devices often have a long gestation period before market acceptance. AI/ML applications in healthcare require vast amounts of structured data, which is often lacking, especially in primary care settings with limited infrastructure and trained staff. As a result, Ai Health Highway had to build infrastructure from scratch, train staff, and work closely with policymakers to demonstrate the potential of their innovations. He also points out that MedTech products must undergo clinical, technological, and regulatory validation, a process complicated by evolving global guidelines. During the COVID-19 pandemic, Ai Health Highway faced manufacturing and supply chain disruptions, forcing them to temporarily pivot to COVID-related solutions before resuming their primary focus on AiSteth and non-communicable diseases.
In India, the high patient load in clinics and hospitals makes it challenging to find Principal Investigators for clinical studies, and there is a shortage of researchers with expertise in both technology and clinical fields. The constantly changing regulatory environment further
complicates approvals, with some startups being misled by unreliable agents, leading to wasted resources and delays. To address these challenges, Ai Health Highway partnered with incubators and academic institutes like FSID IISC Bangalore, Social Alpha, Forge, and MeitY, which offered crucial support. These issues are even more pronounced in rural areas due to insufficient skilled workers, funding, and operational challenges.
Dr Jeevannavar points out the severe shortage of cardiologists in India, with just over 5,500 serving a population of 1.4 billion, highlighting the urban-rural divide where most specialists are concentrated in cities. Despite an increase in medical colleges and graduates, the lack of infrastructure in rural areas limits access to specialist care. To address these challenges, India needs innovative, portable solutions like AiSteth, an AI-powered stethoscope that aids in the early detection of cardio-respiratory disorders. In primary healthcare settings without access to advanced diagnostics, AiSteth allows trained healthcare workers to perform quick and effective screenings, reducing the need for patients to travel long distances. Upskilling local healthcare providers with such tools can significantly improve the healthcare system.
"Access to clinical datasets is vital for AI in healthcare, as machines can analyse large data sets to reveal insights. The main challenge is defining use cases, creating structured datasets, and ensuring transparency in AI models. This requires upskilling healthcare professionals. While AI has progressed in fields like radiology, rural areas lack the infrastructure to support meaningful AI applications, especially for non-communicable diseases. AI is more likely to augment rather than replace human expertise in healthcare, with the human touch remaining essential," adds Dr Jeevannavar.
Collaboration and Stakeholder Engagement
Poonam Muttreja, Executive Director of the Population Foundation of India, stresses the need for better healthcare access in rural areas, where 65% of the population has only 20% of the country's doctors. A severe shortage of healthcare workers, including a 76% shortfall of specialists in rural Community Health Centers, hampers services, especially in maternal and child health.
Muttreja advocates investing in healthcare infrastructure, expanding telemedicine, and training local healthcare workers. She also highlights the importance of community involvement in healthcare planning and addressing social norms that hinder health-seeking behaviour.
Artificial Intelligence (AI) offers promising solutions like diagnostic tools, patient data management, and virtual health assistance. However, it's not a cure-all; challenges such as poor digital infrastructure, investment needs, and data privacy concerns must be addressed. AI should be viewed as a complementary tool, not a standalone solution. By integrating AI with communityinvolvement, we can work towards equitable healthcare access, which is crucial for sustainable national development, says Muttreja.
Policy Recommendations and Future Directions
As AI continues to make strides in healthcare, its potential to enhance the capabilities of clinicians is undeniable. However, as Pathni, the lead accreditation assessor for NABH, points out, the journey is far from straightforward. AI systems' accuracy, fairness, and trustworthiness must be beyond reproach, demanding rigorous internal and external validations. The principle of "Primum non nocere"—First, do no harm—remains an ethical cornerstone that must guide the deployment of AI in healthcare.
The transparency of AI, particularly in complex technologies like Machine Learning, is another critical concern. When the logic behind AI decisions becomes opaque, it undermines the trust of those who rely on these tools for life-saving decisions. Current regulatory frameworks, while a starting point, are insufficient for the nuanced challenges posed by adaptive AI technologies. There is an urgent need for comprehensive policies that authorise AI use in healthcare and ensure ongoing audits in real-world settings. The legal landscape surrounding AI's liability and accountability remains murky and demands thorough exploration.
The path forward calls for a collaborative approach, bringing together policymakers, academics, researchers, ethicists, industry leaders, and civil society to create balanced solutions. Engagement platforms must be established to navigate AI's intricate benefits and risks.
In the broader context of rural healthcare, the integration of AI must be complemented by robust investments in infrastructure and resources. As Pavitra Mohan suggests, a strengthened partnership between the private sector, civil society, and government through trust-based policies is essential. This includes embedding primary care into state-funded health insurance schemes and improving the work culture within government health systems to motivate healthcare teams in rural areas. With over 80% of primary healthcare facilities falling short of Indian Public Health Standards, targeted financial investments are critical. While the focus has shifted towards programs like PMJAY, it is vital that these investments also strengthen the foundational pillars of rural healthcare, ensuring that the promise of AI is not just a technological advancement but a tangible improvement in the lives of those who need it most.