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AI is an essential tool to transform medical care delivery and improve efficiency.
Let me start by referring to Malthusian population theory: as the number of people increases geometrically while the available resources increase arithmetically, there will be a time when the available resources will not be enough to care for the increasing population.
There is no doubt that every individual requires the services of a healthcare provider in one way or another. Still, both human and material resources are not sufficient for the increasing population of those who depend on the healthcare system.
How do we begin to solve the problem?
There is no better way to approach the problem, especially in this era of information technology proliferation and advances in AI across all areas of human endeavour, than through AI integration.
Global healthcare systems are currently under immense pressure due to ageing populations, rising chronic diseases and workforce shortages. AI presents an opportunity to transform medical care delivery and address these problems.
Careful development and deployment of integrated artificial intelligence in the healthcare system will enhance diagnostic accuracy, optimise treatment plans and improve patient outcomes while reducing healthcare costs.
Leveraging advanced AI technologies such as machine learning, natural language processing (NLP), Internet of Things (IoT) enabled devices and predictive analytics to create a smart, patient-centric healthcare ecosystem is a key part of the solution.
Core components of AI required to mitigate these problems include, but are not limited to, automated diagnostics, personalised treatment recommendations, predictive patient monitoring and operational efficiency tools for healthcare providers.
Countries such as the United States, the United Kingdom, India, China and a few other Asian giants are rapidly integrating AI systems into their healthcare systems for more efficient healthcare management.
In Africa, especially in Nigeria, there is a need for the integration of AI systems in hospitals and clinics across diverse settings, from urban to rural healthcare centres. These technologies can significantly enhance diagnostic accuracy, streamline patient data, and improve overall care delivery.
In recent years, however, there has been a notable migration of medical professionals from African countries to nations such as the United Kingdom, the United States, India, and China not only in search of better living conditions but also in pursuit of advanced career opportunities, access to modern medical technologies, professional growth, and environments equipped with the proper tools and infrastructure to deliver high-quality care.
For many, it’s not just about better pay, it’s about being empowered to practise medicine in a way that is impactful, supported, and sustainable adoption in health care will help to mitigate the effect of this migration of medical professionals “japa” syndrome on those who require their services. In Nigeria, the pressures on the health system make the case for AI particularly urgent. Doctor-to-population ratios remain far below World Health Organisation recommendations.
Tertiary hospitals in cities such as Lagos and Abuja are frequently overcrowded, and many primary healthcare centres in rural communities lack full-time doctors or specialists.
Adopting AI-enabled triage tools and telemedicine platforms could support nurses and community health workers in these facilities by helping them prioritise high-risk patients, interpret basic investigations such as chest X-rays or routine laboratory tests, and receive remote input from consultants in teaching hospitals.
At the system level, predictive analytics applied to Nigerian data on infectious diseases, maternal health, non-communicable diseases and medicine stock levels could help the Federal Ministry of Health and state ministries to plan staffing, outreach campaigns and supply chains more efficiently. To realise this potential, Nigeria will need a clear national policy on AI in health, investment in digital infrastructure and partnerships between universities, teaching hospitals, innovators and regulators to build secure, locally relevant datasets and solutions.
It is high time that underserved areas in Africa, such as Nigeria, supported the initiation of AI integration by allocating appropriate budgets within a reasonable timeline, which will go a long way towards reducing diagnostic errors, improving treatment outcomes and lowering operational costs.
According to the World Health Organisation (WHO), A report released in September 2023 states that in 2021, around 4.5 billion people, more than half the world’s population lacks access to essential healthcare services, and healthcare systems are strained under growing demands. Artificial intelligence (AI) emerges as a transformative force in this space.
The healthcare system in Africa faces significant hurdles such as resource inefficiency, diagnostic errors, inadequate chronic disease management and inequalities in access to care, among others. World Health Organisation WHO (2023) noted that “over 800 million people are facing inadequate healthcare access. Low and middle-income countries often rely on outdated systems, leading to inefficiencies and poor outcomes”. AI has the potential to bridge these gaps by optimising workflows, improving accuracy and reducing costs.
African countries should now focus on the stepwise integration of AI in their healthcare systems, for example, through the integration of AI key technologies.
Machine learning (ML) includes supervised models like random forests for disease risk prediction and unsupervised learning for anomaly detection in patient data.
Computer vision uses convolutional neural networks (CNNs) for analysing X-rays, computed tomography scans and magnetic resonance images, as seen in platforms such as Zebra Medical Vision.
IoT devices and wearables enable real-time patient monitoring via devices like heart rate sensors and glucose monitors, integrated with edge computing for immediate insights. Predictive analytics employs time series models such as long short-term memory (LSTM) networks for forecasting disease progression, for example, in diabetes or cancer.
Robotics and automation include surgical robots like the da Vinci system for precision procedures and AI-driven chatbots for patient engagement and triage.
AI systems in healthcare should be carefully designed with an efficient system architecture. The data layer includes wearable devices, hospital electronic health records (EHRs) and diagnostic imaging data.
The processing layer consists of cloud-based AI engines using frameworks such as TensorFlow and PyTorch for model training and inference.
The application layer includes mobile and web applications for patient and doctor interaction.
The security layer can include blockchain for data integrity and privacy measures that comply with relevant regulations, such as the Health Insurance Portability and Accountability Act of 1996(HIPAA), where applicable. The architecture must be capable of performing data cleaning, feature extraction and augmentation to address class imbalances and ensure high-quality inputs.
Technological outcomes include a fully functional AIHS platform with open-source components for community contributions, AI models achieving over 90 percent accuracy in diagnosis and predictions, and integration with existing hospital systems via APIs for seamless adoption.
Economic and environmental impacts include an estimated 30 percent reduction in diagnostic costs and a potential return on investment of 4:1 within three years, as well as reduced waste in healthcare supply chains through smarter resource allocation.
Social benefits include empowering healthcare providers with AI knowledge tools, reducing disparities in access to quality care, creating jobs in AI system maintenance, data analysis and telemedicine support, and improving diagnostics, treatment accuracy and patient outcomes for communities.
In conclusion, the AI Driven Healthcare System (AIHS) represents a transformative leap in addressing Africa’s global healthcare challenges. By leveraging AI, we can enhance diagnostic precision, improve patient outcomes and foster equitable access to medical care worldwide. Governments and stakeholders in the healthcare system should support efforts to integrate AI in healthcare in order to ensure a healthier and more inclusive future for all.
Oyetola Florence Idowu is a forward-thinking technology professional specialising in digital transformation and the practical application of Artificial Intelligence in organisational settings. Known for her strategic mindset and passion for innovation, she works at the intersection of data, automation, and user-centred design, helping teams adopt emerging technologies safely and effectively. Oyetola is a writer, and award-winning co-author. Her work in AI and digital tech innovation has been featured in both local and international publications.
Oyetola Florence Idowu writes from the UK
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