Ensuring access to healthcare remains a pressing global challenge, particularly in low- and middle-income countries (LMICs), rural areas, and underserved urban communities.
According to the World Health Organization (WHO), an estimated 11 million additional healthcare professionals will be needed by 2030, with more than 70% of the deficit concentrated in LMICs.
Sub-Saharan Africa, for example, has just 3% of the world’s healthcare workers, yet it bears 24% of the global disease burden. With rising outbreaks of Ebola, cholera, and polio –exacerbated by war, climate crises, and funding shortages – the need for a sustainable solution has never been more urgent.
Additionally, global healthcare costs are increasing at an unsustainable rate, with nearly 20% of expenditure, or a staggering US$1.8 trillion, deemed to be unnecessary or inefficient. Experts believe that AI-driven solutions could significantly optimise resource allocation, reduce costs, and improve patient outcomes.
How AI is reshaping global healthcare
Currently, half of the world’s population lacks access to essential health services. However, AI-powered telemedicine in the form of remote medical consultations and automated diagnostics using AI to analyze medical data are able to close this gap by providing faster, more accurate and cost-effective health solutions.
See also: Rise of telemedicine: Transforming healthcare delivery
A report unveiled during the 2025 World Economic Forum predicts that the AI healthcare market will grow at an average annual rate of 43% from 2024 to 2032, potentially reaching US$491 billion by 2032.
With about 4.5 billion people without access to vital healthcare services and a predicted health staff deficit of 11 million by 2030, AI technologies are already helping clinicians to:
- Recognise fractures and early disease symptoms
- Optimise patient triage
- Improve healthcare delivery in resource-limited areas
Expanding access to remote and underserved communities
AI-powered digital health tools, such as m-mama in sub-Saharan Africa and the WISH Foundation in India, demonstrate how technology can bridge critical healthcare gaps worldwide.
m-mama, an AI-driven emergency referral system, connects pregnant women and newborns with volunteer drivers for transportation to the nearest healthcare facility. This innovation is believed to have led to a 38% reduction in maternal deaths due to faster access to emergency care. India’s WISH Foundation has leveraged AI-powered telemedicine services to provide healthcare access to 140 million people, significantly easing the doctor-patient ratio pressure. With over 37.9 million consultations handled by telemedicine conducted across 1,000+ primary healthcare institutions in six states, this initiative has reduced the burden on doctors and ensured timely access to healthcare services for people in rural and underserved regions.
AI-driven automation: Closing workforce gaps and easing workloads
With 32% of healthcare workers worldwide at risk of quitting their jobs because of burnout and sub-Saharan Africa facing a shortage of 5 million healthcare professionals, AI-powered automation can provide a scalable solution:
- AI reduces the administrative burden by automating documentation, transcriptions and scheduling thus allowing doctors to focus on patient care. At Apollo Hospitals in India, doctors save two to three hours per day due to AI solutions.
- AI-enhanced Electronic Health Records (EHRs) have improved data interoperability healthcare workflows at COPE Community Services in the United States.
AI-enhanced diagnostics and early disease detection
According to WEF research, AI models have demonstrated significant improvements in the accuracy of TB diagnosis, with some exceeding a 90% sensitivity in detecting abnormalities. AI can also analyse medical imaging for cancer and cardiovascular disorders faster than human radiologists.
- In India, AI-powered chest X-ray screening has considerably improved TB diagnosis
- In Africa, AI-powered algorithms are being used to track and forecast malaria and Ebola outbreaks by analysing real-time epidemiological data.
- In the UK, Derm, an artificial intelligence technology, has achieved a 99.9% accuracy rate in detecting melanoma, speeding up skin cancer diagnoses. Clinicians at Chelsea and Westminster Hospital in London use an iPhone with a magnifying lens to take photos of worrisome moles, which the AI analyses in seconds. In Australia, ECgMPL, created by researchers at Charles Darwin University, detects endometrial cancer with 99.26% accuracy and has good accuracy rates for colorectal, breast, and oral cancers.
By expanding these advances globally, AI can reduce the death rates for diseases such as cancer and TB, ensure timely medical treatments and improve public health surveillance, allowing for speedier responses to infectious disease epidemics.
Challenges of AI implementation
However, despite its positive aspects, there are numerous challenges with the expansion and implementation of AI.
Digital Infrastructure Gaps – There is limited internet access in many low- and middle-income countries. For example, just 28% of sub-Saharan Africans have access to broadband internet, significantly restricting the potential for AI-powered telemedicine and other digital health solutions.
See also: Can Telehealth Revolutionize Global Healthcare Access? | DevelopmentAid Dialogues
Frequent power outages, cybersecurity issues, and poor data storage capacities impede AI implementation in these areas.
AI bias and data limitations – Biased, non-representative healthcare data could worsen existing disparities. For example, an AI system utilized in various U.S. health institutions showed bias by prioritising healthier white patients over less well black patients since it was trained on cost statistics rather than treatment requirements.
Data scarcity in LMICs means there is a lack of comprehensive local healthcare data which makes it difficult for AI models to generalize effectively across populations.
AI success stories in LMICs
- AI for snakebite identification in South Sudan: Médecins Sans Frontières (MSF) is testing an AI-powered snakebite identification tool that analyses 380,000 snake images to help doctors to prescribe accurate antivenom treatments for snake bites.
- Rwanda’s AI-Powered Drones: The government, in partnership with Zipline, uses AI-powered drones to deliver medical supplies to remote areas, reducing delivery time from hours to minutes and ensuring timely access to essential medical products such as blood transfusions and vaccines.
- AI in Maternal and Neonatal Health in Malawi: A health center has recorded an 82% decrease in stillbirths and neonatal deaths due to AI-enabled fetal monitoring software.
- AI-Powered Healthcare Guidance: Ada Health’s AI-powered app provides free and rapid health recommendations based on users’ symptoms and risk factors. The platform is available in 148 countries and 11 languages, with over 13 million users worldwide.
AI – a powerful tool supporting, not replacing, medical professionals
AI is not a replacement for medical professionals but a transformative tool that can improve healthcare delivery, particularly in underprivileged areas. According to Michael Pfeffer, MD, Chief Information Officer of Stanford Health Care, human oversight is critical in AI applications to provide flawless and trustworthy results.
Similarly, Mihaela van der Schaar, Professor of Machine Learning, AI, and Medicine at the University of Cambridge, emphasizes AI’s collaborative potential, stating that it should improve human talent while also empowering both healthcare personnel and patients, resulting in a genuine human-machine collaboration.
