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Unlocking the Potential of Artificial Intelligence for Healthcare in Africa

Artificial intelligence (AI) is the imitation of human acumen in machines that are typically programmed to imitate human actions. It is an interdisciplinary science with numerous approaches of machine learning in the technology industry. The utilization of AI in healthcare is significantly helping medical practitioners in many facets of patient care, including administrative procedures [1]. As of 2020, the use of AI in the USA and Canada has cut healthcare expenses by 25% and 12% respectively [2], allowing the underlying healthcare providers to dedicate more of their limited resources to patient care matters. AI deep learning algorithms have also saved many lives in America and Europe by decreasing the diagnosis-treatment-recovery cycle for patients. Africa is no different.
AI technology has great potential to transform healthcare in Africa by automating medical procedures. Often seen as a futuristic promise, AI is quickly disrupting healthcare in Africa, and the continent needs to brace itself for this unavoidable force. The potential of AI for healthcare in Africa is an ongoing discussion, particularly in addressing the global burden of diseases, currently at 25%. With the automation of medical procedures, AI can help health professionals do more with limited resources. There is much to learn from organizations that are transforming health outcomes in Africa, particularly those already piloting certain AI applications to establish effectiveness.

Many organizations are already adopting AI for healthcare in Africa. The following are a few examples of where AI is being used in the continent:

• minoHealth AI Labs in Ghana is automating radiology by applying deep learning and an algorithm known as a convolutional neural network.
• Philips Foundation has successfully implemented AI software, developed by Delft Imaging, in 11 South African hospitals to help triage and monitor COVID-19 patients via X-ray imaging. Delft Imaging’s AI-based CAD4COVID software, which complements existing COVID-19 diagnostic technologies, estimates the severity and progression of COVID-19 disease based on routinely available chest X-rays.
• South Africa is also currently applying a multinomial logistic classifier-based method to individual resource scheduling, particularly in predicting the duration health employees may stay within public service [3].
• In Tanzania and Zambia, Delft Institute’s CAD4TB software has been used to assess the utilization of the computer-aided analysis of pulmonary tuberculosis from the chest radiographs.
• Ilara Health is also offering accurate and affordable diagnostics to people in rural areas via small, AI-powered diagnostic devices incorporated through a proprietary technology policy and correspondingly distributed openly to the primary care doctors.
• Antara Health is using AI-assisted health technology to make healthcare simple for patients and providers.
• XELPHA Health operating Aphya as the sole mobile-first EMR solution that assists in the detection and optimization of specific devices and hence facilitating active contribution and engagement amidst both patients and providers.

Essential building blocks and barriers for a sustainable AI in Africa
To leverage the opportunities for AI in healthcare in Africa, there is a need to address the main building blocks that are essential to delivering a sustainable AI for African healthcare systems. For example, there needs to be:

• Proper digital infrastructure for storage of data from health facilities.
• A strong data culture within health facilities that values data and makes tools and resources accessible to clinicians.
• Suitable regulations and standards for AI and data science, which will enable regulators to examine AI applications within health before their deployment.
• Adoption of local solutions by comprehending and finding suitable solutions while promoting self-reliance and assisting in cultivating the local ecosystem.
• More targeted funding for AI health start-ups in Africa that links entrepreneurs with corresponding financiers and reduces the risk for private investors.

Some of the AI barriers that need to be overcome are associated with digital infrastructure, data culture, regulations and standards, adoption of local solutions and funding. Lack of capacity among healthcare professionals remains a challenge. Training on and adoption of AI is needed to ensure compliance, especially with regulations [4]. Besides the difficulty in acceptance by patients, clinical adoption is still a major barrier, as many healthcare professionals are still not completely comfortable using AI technologies. However, much can be achieved through capacity building.

What to expect and not to expect from AI in healthcare
The emergence of AI in healthcare continues to spur mixed reactions from experts in terms of what to expect and what not to expect.

Just what can we expect from AI in healthcare? It is anticipated that AI will bring about better-prearranged healthcare logistics, an enhanced drug plan, improved working situations for health professionals, an advanced post-modern era of the art of medicine, prediction of outbreaks and pandemics and modernized data-oriented tasks. Ideally, there is much to expect from AI.
But what can we not expect from AI in healthcare? There are a lot of conversations around what AI cannot bring to healthcare. While AI has a great potential for healthcare in Africa, particularly discovering unusual associations and forecasting public health crises, it is not likely to replace the human factor. AI can handle repeated tasks based on data, but will not provide the compassion and empathy that patients need during treatments. Elements of human reasoning are also missing in AI. One of the biggest questions since the emergence of AI in healthcare is whether AI will replace medical professionals and how it will affect medical specialties. Well, certain repeated medical procedures will definitely be taken over by AI. However, physicians will not be replaced in their entirety. We cannot rely on AI for mental health, for example. Some of the reasons include that it is never tech vs. human (these two complement one another) and AI cannot make medical decisions alone, as it is fully dependent on data.

However, AI has the potential to improve outcomes and decrease the cost of treatment. Through vital technology applications, AI promises high-end diagnostic services and medical procedures. It will advance supply-chain efficiencies with minimal administrative responsibilities and modernize life-saving conformity measures. Amidst the COVID-19 pandemic, AI shows potential to generate new abilities for protection against public-health issues affecting susceptible populations. AI can also make treatment more accessible and affordable. There has long been a severe shortage of mental-health professionals, and since the pandemic, the need for support is greater than ever. For example, users can have conversations with AI-powered chatbots, allowing them to get help anytime, anywhere, often for less money than traditional therapy.
As new coronavirus strain holds the continent in its grip, new tools for COVID-19 detection and monitoring are needed to combat the pandemic. Adopting innovative and alternative technologies that can detect and monitor COVID-19 could be an essential part of overcoming resource limitations and decreasing the spread of existing and new coronavirus strains. AI is likely to play an integral role in this. AI could also provide a platform where patients can ask medical questions and receive instant answers, obtain extra information and reminders concerning taking medications, report information to doctors and achieve supplementary medical help. AI also promises advancements in terms of precision and efficiency leading to relatively fewer human mistakes and reduced doctor visits.
Conclusively, AI is no longer a futuristic promise but an unavoidable eventuality in virtually all sectors. It is already making its way out of research laboratories. Due to its potential, stakeholders and governments around the world are taking steps and collaborating to ensure responsible development and use of AI. There is still much to be learned from organizations that are changing health outcomes around the world, and Africa is no exception.

References:
[1] Alhashmi, S.F., Alshurideh, M., Al Kurdi, B. and Salloum, S.A., 2020, April. A systematic review of the factors affecting the artificial intelligence implementation in the health care sector. In Joint European-US Workshop on Applications of Invariance in Computer Vision (pp. 37-49). Springer, Cham.
[2] Alhashmi, S.F., Alshurideh, M., Al Kurdi, B. and Salloum, S.A., 2020, April. A systematic review of the factors affecting the artificial intelligence implementation in the health care sector. In Joint European-US Workshop on Applications of Invariance in Computer Vision (pp. 37-49). Springer, Cham.
[3] Alhashmi, S.F., Alshurideh, M., Al Kurdi, B. and Salloum, S.A., 2020, April. A systematic review of the factors affecting the artificial intelligence implementation in the health care sector. In Joint European-US Workshop on Applications of Invariance in Computer Vision (pp. 37-49). Springer, Cham.
[4] Guo, J. and Li, B., 2018. The application of medical artificial intelligence technology in rural areas of developing countries. Health equity, 2(1), pp.174-181.

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