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Practical Insights and Strategies to Leverage AI-enabled diagnostics for Better Health Outcomes in Resource-Constrained Settings

Dr. Jaydeep Purohit, YO.DH Core Committee Member

Young changemakers across India and Africa are stepping up to shape digital health in ways that truly matter. Whether you're a community health worker in a busy Nairobi clinic or a coder in Bengaluru building the next big health app, this blog is for you. We’ll be talking about digital tools for diagnostics and how they can help detect diseases faster in places where doctors are few, budgets are tight, and everyday hurdles like power cuts or flooded roads are real.

In many low-resource settings, clinics often face shortages of trained radiologists, high patient loads, limited internet connectivity, and frequent stock-outs of basic supplies. Diagnostic equipment, when available, may be outdated or under-maintained, and rural communities may have to travel long distances for even a simple chest X-ray. These challenges make the timely detection of diseases like Tuberculosis (TB) and pneumonia especially difficult.

Yet, despite these barriers, there are real successes emerging from both regions—solutions designed with the realities of the ground in mind, and led by young innovators who understand the need for affordable, practical, and community-friendly approaches.

Source: https://i0.wp.com/www.ictworks.org/wp-content/uploads/2019/05/digital-health-clinic.png?ssl=1

Why AI in Diagnostics is a Game-Changer for Our Regions

Diagnostics are at the heart of good healthcare—they help detect problems early so treatment can begin before things worsen(1). In many parts of India and Africa, particularly in rural areas where specialists are hard to find and city hospitals are overwhelmed, AI is stepping in as a powerful support system(2). We’re already seeing AI tools that can screen X-rays for tuberculosis or analyze blood samples for malaria with good accuracy. For example, Software tools such as qXR (by Qure.ai), Lunit INSIGHT CXR, CAD4TB, InferRead DR Chest, and JF CXR-1 have shown excellent results in automated reading of chest X-rays for TB(3). The global market for AI diagnostics reached USD 1.71 billion in 2024 and is projected to grow to USD 4.72 billion by 2028(4). In India alone, the sector was valued at USD 12.87 million in 2024 and is expected to rise to USD 44.87 million by 2030(5). What’s really exciting is how AI is being tailored to local needs. In India, AI is being used to digitize traditional medical systems — Ayurveda, Unani, Siddha, Sowa Rigpa, and Homoeopathy — via the AI-powered Traditional Knowledge Digital Library, which helps preserve, validate, and make this old knowledge more accessible(6). Also in India, AI startups like Remidio and Artelus are developing diagnostics (e.g., diabetic retinopathy screening tools) suited for rural settings—using fundus-on-phone devices so non-specialists can participate(7). This kind of community-first approach builds trust and ensures the tech actually works in places it’s needed the most.

Closing the Care Loop: Turning AI Insights into Action

AI diagnostics only make a difference when they lead to real-world results, i.e., a diagnosis is then followed up by appropriate medical care and treatment, or referral to the next level of care facility. Unfortunately, this common issue of unassigned alerts is often overlooked. Some simple fixes would be to establish clear follow-up guidelines or assign each alert to a specific person, like a community health volunteer, to follow up on the diagnosis within 24 hours. Additionally, connect systems with everyday tools like WhatsApp for virtual consultations, or link them to local transport options, such as bike taxis, to help patients reach clinics.

AI diagnostic tools need to be designed for the toughest conditions where regular tools have failed to close the gap, such as a remote clinic in South Sudan or a flood-affected village in Assam. That’s where smart, practical AI can truly shine.

Thriving in Fragile Zones: AI That Stands Up to Instability

In places facing conflict or instability, healthcare systems are often disrupted. Clinics shut down, power cuts are frequent, and medical staff may be forced to relocate. Under these conditions, cloud-based AI tools that rely on constant internet access are simply ineffective.

That’s why we need an offline-first approach, such as apps that can process data directly on the device and sync later when the internet is back. Rugged, solar-powered tablets that can be repaired with basic tools can be a good option.

It is critical to always have a human backup plan. Laminated checklists or voice-recorded instructions can keep care going when tech fails. These aren’t just nice-to-haves—they’re essential. Crises often bring a surge in health needs, like cholera outbreaks in displacement camps, so the tools we build must be tougher than the environments they’re used in.

Some innovative projects in Africa are already pairing AI with drone-delivered test kits to reach areas cut off during unrest. It’s this kind of smart, resilient thinking that keeps healthcare moving and builds trust in the community.

Source: https://www.livemint.com/news/india/digital-health-technology-can-revolutionise-healthcare-in-india-report-1566457450376.html

Climate-Proofing AI: Making Tech Work in Tough Weather

Climate-induced harsh weather disrupts the delivery of care. Devices can overheat, roads get washed out, and health camps are canceled. That’s why we need devices that can handle high temperatures and humidity, and AI tools that run on basic phones without needing constant charging.

Smarter systems can also use local weather updates to adjust plans—like sending a “Monsoon alert” to postpone TB screenings. And when fridges stop working during power cuts, AI can help analyze samples that don’t need cold storage. Even better, it can predict disease outbreaks like malaria spikes after heavy rains so clinics can prepare in advance..

Tools That Deliver: What’s Working and Why

A few successful illustrations of AI tools being utilized for diagnostics

Tool / Modality	Example & Technical	Sources
Image Analyzers for Wounds, Eyes, or X‑Rays	qXR from Qure.ai in India: deep learning / computer‑aided detection (CAD) applied to digital chest X‑rays. In a meta‑analysis of over ~21 studies, qXR achieved ~90% sensitivity and ~64% specificity for TB detection using AI‑CXR imaging. Deployed at scale: in Mumbai, seven municipal hospitals screened >100,000 X‑rays, improving TB notifications by ~30‑40%. Results available in <1 minute per scan, helping nurses triage efficiently.	PubMed meta-analysis: https://pubmed.ncbi.nlm.nih.gov/40529749/ ; The Hindu report: https://www.thehindu.com/sci-tech/science/chest-x-ray-interpretation-using-ai-can-detect-more-tb-cases/article67259924.ece
Audio Apps for Coughs or Heartbeats	Helfie AI / Respiratory AI: uses cough‑sound input + symptom questions to assess risk of pneumonia and other respiratory conditions. Very high accuracy (~95%) when cough symptoms are present. The app uses AI models trained on audio + symptom data to produce risk scores, guiding triage during outbreaks and reducing pneumonia misdiagnosis rates.	Helfie AI: https://www.helfie.ai/respiratory-ai
Lab Result Scanners / AMR Testing Apps	Antibiogo (MSF): a smartphone app using image processing + AI + expert‑system logic to interpret antibiograms. Technicians measure inhibition zones on Petri dishes, and the app interprets antibiotic susceptibility. Concordance with expert microbiologists is 90‑98%. CE‑marked as an in‑vitro diagnostic device; deployed in MSF labs in DRC, Jordan, and Mali.	MSF Antibiogo Project: https://www.msf.fr/communiques-presse/l-application-antibiogo-est-en-place-en-rdc-et-en-jordanie-et-bientot-dans-d-autres-laboratoires-msf
Blood Sample / Malaria Diagnostics	AIDMAN (West Africa): YOLOv5-based detection + CNN classifier for malaria parasites on thin blood smear images. Clinical validation shows ~98.44% accuracy. Also, mid‑infrared spectroscopy + ML detects malaria from dried blood spots with >90% sensitivity even at low parasitemia, aiding diagnosis in resource‑poor settings.	Malaria Journal: https://link.springer.com/article/10.1186/s12936-024-05011-z ; AIDMAN PubMed: https://pubmed.ncbi.nlm.nih.gov/37720337/

Common Mistakes That Prevent AI from Helping

Even the smartest AI tools can fall flat if they’re not built for the real world. Fancy dashboards might impress funders, but can confuse nurses. If a pilot project is dropped in without local ownership, it often fades away once the funding ends—because no one feels responsible for keeping it going.

Too much data entry? Health workers skip it to focus on patients. No backup plan? One network glitch and everything stops. The key is listening to frontline staff—if they say, “This slows us down,” it’s time to rethink. Maybe switch from typing to voice inputs.

Responsible AI for Diagnostics: Building Trust and Impact

In Africa, concerns around data privacy and patchy infrastructure are real. But if done right, AI could add up to $2.9 trillion to the economy by 2030. The potential is huge—if we build with people, not just tech, in mind(8).

Doing AI right means keeping it ethical and grounded. AI for diagnostics should be built with care and respect—asking for consent in local languages so people understand how their data will be used, and collecting only what’s necessary to protect privacy. Clinicians should always have the final say, with the ability to override AI decisions if needed for patient safety. Every alert or flag should have someone assigned to follow up so nothing gets missed. Strong security—like encryption and limited access—is a must, and everything should follow local laws(9).

And to keep these solutions alive beyond the pilot phase, it’s important to plan for long-term funding and support. When AI is designed this way, it earns trust and actually works in the real world.

Conclusion: Building a Healthier Future with AI

AI in diagnostics isn’t a one-size-fits-all solution, but for young innovators from the Global South, it’s a powerful ally to reshape healthcare. By grounding AI in our realities, inclusive of local languages and traditions, and focused on driving action, we can make health systems fairer and more accessible. AI can help in task-sharing the work of overstretched health workers, detect diseases earlier, and empower communities to take charge of their health. The potential is massive: in Africa alone, AI could contribute $2.9 trillion to the economy by 2030, with healthcare leading the charge. In India, AI-driven diagnostics are already transforming TB and cancer care, with market growth signaling even bigger opportunities ahead.

But this isn’t just about tech—it’s about people. It’s about the nurse in Malawi who can triage faster with an AI-powered X-ray tool, or the family in rural Odisha who gets clear health advice via a chatbot in their language. It’s about young leaders taking small, smart steps like prototyping a fever app or testing a wound photo guide to solve real problems. The tools and ideas here, from offline-first apps to community-driven pilots, are starting points you can adapt and share. Let’s not just dream about equitable healthcare—let’s build it, one pilot at a time.