AI and Machine Learning for Early Disease Detection and Prevention
Introduction
There is hardly any sector or divide of healthcare that is shielded from the influence of machine learning involvement, particularly the advancement of artificial intelligence (AI). In our daily lives, we are witnessing artificial intelligence improve diagnostic medicine, and preventive, and therapeutic medicine. The influence of this is that in years to come, we will have a collaborative approach to medical scholarship between AI/ML and human beings.
Disease prevention as a critical component of epidemiology research places primacy on disease detection. When diseases are detected, and a course of action is followed early enough, there is a stronger chance of a better prognosis, treatment procedure, and, most importantly, a well-defined protocol for the prevention of future occurrence.
Brief overview of the importance of early disease detection and prevention
One of the advantages of disease detection is the possibility of early intervention, reduced cost of treatment, and increased chances of recovery. With early detection of diseases like epidemics, we can control the spread and reduce the overall burdens on healthcare systems.
In an article by The Lancet, a weekly peer-reviewed general medical journal, titled “Modelling the effect of early detection of Ebola,” there were critical factors highlighted as responsible for curbing the spread of the viral hemorrhagic disease. The initial responses in the affected regions focused primarily on establishing critical and responsive infrastructure that improves local capabilities to monitor contact surveillance, effectively isolate infectious people, educate people on the risk factors and mode of transmission, and provision of supportive treatment especially in the pre-symptomatic stage.
The importance of early detection cannot be played down. In extreme cases, with early detection, individuals can maintain a better quality of life by managing the disease condition to prevent them from progressing to advanced stages. This provides room for personalized treatment plans to help individuals effectively navigate the phases of treatment outcomes (like adverse reactions to chemotherapy and organ transplant) and other challenges posed by their health condition. This will, in turn, minimize the impact of the diseases on their overall well-being and daily functionality.
Impact of early detection on patient outcomes and healthcare costs
As we discussed earlier, there is a net positive impact of early detection of disease on individuals, which trickles down firstly to healthcare costs. But there is a very basic question that must be considered while discussing the benefits of early detection, and that is the rising cost of even the most basic procedures, emergency medical procedures, and even minimally invasive surgeries globally.
According to the World Health Organization (WHO), in an article titled, “Can people afford to pay for health care? New evidence on financial protection in Belgium,” the country, Belgium, ranks highest in Europe on Catastrophic health spending. This is a situation where a household can no longer afford to meet basic needs — food, housing, and heating — because of having to pay out of pocket for health care. In the report, nearly 260,000 households in Belgium experienced catastrophic health spending in 2020, the latest year of data available. This corresponds to 5.2% of all households, but the number goes up to 8% for households headed by unemployed people and 12% for households in the poorest fifth of the population.
This is worst in Sub-Saharan Africa as data on this is largely opaque and when available, doctored to suit the government’s narrative. Contrary to what is common in Europe and the U.S particularly where the government spends more money per person on healthcare than many countries that fund universal programs, this spending concentrated that the top 1% of spenders account for more than 20% of total healthcare expenditure, in Africa, the reverse is the case.
In Nigeria, Africa’s most populous country’s 2024 Federal Health Budget, the total sum allocated out of the overall annual expenditure of $18 billion is $822 million inclusive of the $84 million provided for the Basic Health Care Provision Fund (BHCPF). A paltry 4.47% of the proposed budget expenditure.
While not delving deeper into the rot of healthcare financing by governments in Africa, it is important to remember the salient points discussed in Walter Rodney’s book, How Europe Underdeveloped Africa. In it, he highlighted the exacerbating underdevelopment and hindering efforts at economic self-sufficiency as one of the manifest signs of the underdevelopment of Africa by Europe. Today, this is reflected in our healthcare spending and budgetary allocation for expenditure, especially as medical tourism now takes a huge chunk of the foreign reserves of most African countries. A look at the data below belies a contrast between Africa and other developed nations. But let’s get back to our main discourse.
What are the Challenges in traditional methods of disease detection?
Without recourse to the advancement of healthcare, vis-à-vis the reliance on artificial intelligence, machine learning, and modern medical interventions, the medieval methods of disease detection largely relied on base instincts and guesswork. The inroads made by orthodox medical practice prevailed through quantifiable research liaising with experience in disease management. To highlight the challenges of these methods, it is important to note that practical solutions attenuate the prevailing crippling effects of these traditional methods in the past.
To start with, the low-hanging difficulty is to look at the time-consuming processes that make up the traditional methods. From sample collection, transportation to laboratories, and manual analysis, without fail endanger the critical components of healthcare delivery (diagnosis and treatment). If samples are not properly collected, there is a chance of producing any false positive or false negative errors. In another vein, a delay in the release of sample collection can also result in missed diagnosis and or treatment initiation.
Highlighting successful implementations of AI for early disease detection
Several startups are leading the race in the early detection of disease. Some of them are far advanced in operation and others are in the early stages of this operation. Ultimately, the end goal of leading patients to better outcomes and the provision of more efficient healthcare delivery is met.
Arkangel AI works as an AI as a service platform (refer to my article on Healthcare as a Service, HaaS) to transform medical data from healthcare institutions. The AI tool creates AI algorithms automatically for early disease detection and scales up its operation to include companies in the pharmaceutical industry, healthcare operations, and even hospitals.
In the detection of diabetes-linked retinopathy, IDx Technologies pioneers cutting-edge artificial intelligence solutions that are tailored for the early detection of diabetic retinopathy. Diabetic retinopathy is the most prevalent condition responsible for significant vision impairment. Their groundbreaking FDA-approved system, IDx-DR, harnesses the power of advanced algorithms to independently analyze retinal images. These algorithms act by swiftly identifying key indicators of the disease, empowering healthcare professionals to intervene promptly, and offering patients timely access to life-changing treatments.
Data privacy has led the discussions around advancement in AI, but in line with these concerns, Owkin combines AI and federated learning (which operates on the principles of singularization of data by training a central model across decentralized servers) to analyze healthcare data. In doing so, it upholds the utmost standards of privacy and security. Through their platform, they contribute to early disease detection and personalized medicine by discerning patterns within extensive datasets sourced from diverse entities, including hospitals and research institutions.
What’s the future of AI and machine learning in improving healthcare outcomes?
With continued research in disease detection, especially the deployment of AI, we are all in for proper optimization of healthcare operations. That’s the end goal. AI and specific machine language models can optimize healthcare operations by automating administrative tasks, predicting patient flow, and optimizing resource allocation. This improves efficiency, reduces costs, and enhances patient satisfaction by minimizing wait times and streamlining workflows. The consequence is that This leads to shorter wait times, smoother workflows, and ultimately, better healthcare outcomes for patients.
