ASKING FROM PAKISTAN:How can we take help from Ai in improving the healthcare facilities in the underdeveloped countries where there is no know how about the use of this intellectual technology...
Sergio Luis ConteHelping to create solutions for everyone| Worldwide based OrganizationsBuenos Aires, Argentina
If you have a device connected to the internet and you have access to generative AI tools then you have all you need. Saving Changes...
Imran AfzalAuthor| The Strategic PMOCary, NC, United States
I think AI has real potential to improve healthcare access in developing countries, but the biggest opportunity is probably not replacing doctors — it’s extending limited expertise and improving operational efficiency.
A few areas where I think AI can realistically help:
• Translation and communication AI tools can help bridge language gaps between patients, clinicians, and medical information — especially in rural areas.
• Medical education and training Doctors, nurses, and healthcare workers can use AI as an always-available learning assistant for procedures, research summaries, and clinical concepts.
• Administrative burden reduction In many healthcare systems, staff spend enormous time on paperwork, documentation, scheduling, and reporting. AI can help automate some of that work so providers can spend more time with patients.
• Basic triage and decision support AI-powered chatbots or mobile tools may help identify high-risk symptoms earlier and guide patients toward appropriate care — especially where specialists are limited.
• Remote healthcare support Mobile-first AI tools could help rural clinics access medical guidance, summarize patient information, or support telemedicine initiatives.
That said, technology alone is not enough. Successful adoption also depends on: • internet and device access • training and digital literacy • trustworthy data • privacy/governance • local language support • clinician adoption and workflow integration
In my opinion, the most effective approach is to start small with practical use cases that solve immediate problems for healthcare workers, rather than trying to implement “AI transformation” all at once.
As someone originally from Pakistan, I’ve seen firsthand how resource constraints and access gaps can affect healthcare systems. Even small improvements in efficiency, education, and access to information can have a meaningful impact at scale. Saving Changes...
AI can improve healthcare in underdeveloped countries by supporting early disease detection, telemedicine, digital patient records, and remote consultations in areas with a shortage of doctors. The key is to create awareness, train healthcare staff, and use simple, low-cost AI solutions via mobile technology and local-language support to improve accessibility and patient care. Saving Changes...
Program Manager| HARPER SRLSanto Domingo / Distrito Nacional, Dominican Republic
AI could help a lot in areas where healthcare access and specialists are limited, especially through early diagnosis support, remote consultations, medical triage, and health data analysis. But in underdeveloped countries, the biggest challenge is usually not the technology itself. It’s infrastructure, connectivity, training, cost, and trust in the systems being introduced. Starting with small, practical use cases and basic training often works better than trying to implement large AI solutions immediately. Saving Changes...
AI can help in underdeveloped countries, but only if it is introduced as a practical support tool, not as a sophisticated technology project. The first priority should be solving real healthcare problems such as shortage of trained staff, delayed diagnosis, medicine stock-outs, poor recordkeeping and limited access to specialists. Trust me as a son of the soil, I'd worked with EHealth Pakistan and several UN agencies including IOM, UNDP and Agha Khan Foundation to build TeleHealth in Northern Areas. Especially during winters when the roads get cut off due to blizzards, land slides or excessive snow. After all we are talking about a country which has most glaciers on that latitude.
A workable approach is to start with simple, high-impact uses. AI can support frontline health workers with triage guidance, symptom checking, appointment reminders, vaccination follow-up, translation and basic clinical decision support. It can also help facilities manage operations by forecasting medicine demand, flagging supply shortages and organizing patient data more efficiently. In many places, these operational improvements create more value than advanced diagnostic systems. I think you should study IDX as a prime example of how they have automated and a big potential for the AI implementation.
Because there is little know-how, the technology must be designed for low digital literacy. That means local language support, voice-based or mobile-friendly interfaces, clear prompts and minimal complexity. Healthcare staff should not need technical expertise to use it. Training should be short, practical and repeated through local champions who can help others adopt the system.
Infrastructure is another major issue, so AI solutions should be lightweight and resilient. Tools should work on basic smartphones or low-cost computers and ideally continue functioning with weak internet or intermittent electricity. Offline-first systems and low-bandwidth platforms are often more realistic than cloud-heavy solutions. They have 5G but as you move away from city centres the coverage and speed attenuates. Human oversight is essential. AI should assist doctors, nurses and community health workers, not replace them. In settings where trust is fragile and expertise is limited, staff need clear guidance on when to rely on the tool and when to escalate to a clinician. This reduces the risk of misuse and builds confidence gradually. The long-term success depends on capacity building. Introducing AI should include digital education, basic data literacy, and local ownership. If outside organizations bring the system but local teams do not understand or maintain it, the solution will fail after the pilot stage. Sustainable progress comes from transferring knowledge, not just deploying software.
There also needs to be attention to ethics, privacy and fit-for-context. Patient data must be protected, outputs must be checked for accuracy and the system must reflect local languages, diseases, workflows and cultural realities. A tool built for a large urban hospital in a developed country may not fit a rural clinic with one nurse and limited connectivity.
So the best answer in my opinion is this: "AI can improve healthcare facilities in underdeveloped countries by helping with diagnosis support, patient communication, training, records and supply management, but it should be introduced slowly, locally and with strong human support. The most effective strategy is to begin with one or two simple use cases, train local healthcare workers well, measure results and scale only after the system proves useful in that environment. Saving Changes...
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