Is Integrating AI within Healthcare the Right Move for Africa?
The adoption of Artificial Intelligence within the African healthcare ecosystem has emerged as a hot topic of significant interest and debate across Africa. As the continent grapples with its complex healthcare challenges, the promise of AI to transform the fortunes of a flailing healthcare system provides basis for optimism. From enhancing diagnostic precision to optimising patient care and management, AI holds the potential to revolutionise healthcare delivery across the continent. But is this technological leap the right move for Africa?
Africa faces unique healthcare challenges, including a skills shortage, limited access to essential services, and a high prevalence of diseases like malaria [1], cholera [2] and tuberculosis [3]. Existing healthcare models are struggling to meet the requirements of an increasingly complicated healthcare landscape, resulting in sub-optimal health outcomes for both the patient and practitioner.
The Case for AI Adoption in African Healthcare Systems
Whilst AI is not the proverbial silver bullet, it offers several potential benefits and could play a complementary, yet transformative role alongside the existing systems by enhancing productivity and efficiency:
Enhanced Diagnostics: A fundamental attribute of an AI system is the capability to process big data at pace and a scale not humanly attainable. For instance, algorithms can be trained to combine the simple CT scan of a patient and medical history to predict heart disease quicker and more accurately than a human [4], consequently reducing the need for specialised radiological tests which are often limited or unavailable in developing regions.
Improved Efficiency: AI can be used to automate the routine aspects of healthcare operations such as triaging, scheduling and billing, thereby alleviating the administrative burden and freeing up healthcare professionals to focus on direct patient care [5].
Remote Patient Monitoring: Due to the advent of IoT, AI can now be integrated with appliances, gadgets and personal devices such as watches, rings, bracelets, etc, to enable real-time monitoring of patient progress [6]. This enables healthcare providers to maintain a holistic view of patient wellbeing and progress, independent of distance or location.
Data-driven Decision-making: AI is innately data-dependent, and this attribute may be exploited by creating algorithms to scan and process vast volumes of health data and information, which could be used to potentially predict outbreaks, identify interesting trends and enable swift, proactive responses to public health threats [7].
Personalised Patient Treatment and Management: Due to the inherent capability of AI algorithms to learn from data, AI systems may be trained to analyse patient records and information to tailor treatment plans, distinct to individuals, potentially boosting the efficacy of medical interventions [8].
Additionally, AI facilitates novel medical research through its capability to scan and interprete big data from disparate sources at pace, potentially accelerating drug discovery and development, and cost reduction. According to a 2019 Forbes Insight report, global investment in AI healthcare implementations could help save $150 billion in costs by 2026.
Considerations & Challenges
Despite the potential game-changing capability of AI within Africa's healthcare systems, the initial investment required for AI adoption may prove prohibitive and potentially add a layer of complexity to existing systems due to the step-change required in certain contexts and several persistent challenges – most notably infrastructure limitations, data privacy concerns, training and workforce development, and cultural bias.
Technology Infrastructure Limitations: Many regions in Africa lack the technological backbone to implement or tailor the relevant AI applications. For instance, there is inconsistent power supply, unreliable internet provision [9] and even vandalism in certain regions [10].Technological innovations that successfully address this fundamental challenge are equally as critical as the implementation of viable AI applications and require concerted intervention from both public and private sector actors.
Data Quality and Privacy Concerns: AI fundamentally relies on high-quality data; accordingly, it is only as effective as the underlying data used for training. In many cases, data is still captured via manual, laborious, error-prone processes, often requiring significant time investment and subject matter expertise to analyse. Preserving the security and privacy of patient information is critical; yet many African institutions lack robust data governance frameworks [11] to protect sensitive health data, with potential exposure to data breaches, hacking and unauthorised access.
Training and workforce development are also critical considerations. Successful integration of AI requires a competent workforce to exploit and maximise the potential of AI interventions. However, this could impose an added financial burden on already struggling healthcare systems [12]. Upskilling and AI literacy training programmes for healthcare professionals will be essential, not only to operate these systems but also to overcome potential cultural barriers to adoption of emerging technology [13]. Proponents of AI within the African healthcare system should provide a robust cost-benefit profile that makes a strong case for investing in these technologies.
The Way Forward
For AI integration to succeed within the African healthcare context, a multi-pronged, collaborative approach is essential:
Multi-organisational Collaboration: Governments, healthcare organisations, and technology companies must collaborate to install critical infrastructure, address data privacy concerns, and invest in training programs to raise the overall AI literacy level and secure the expertise required to deploy and maintain such systems.
Contextual Adaptation: It is important that AI interventions are designed to reflect the context in which the systems will be implemented. The specific requirements and constraints of the African healthcare system must be accounted for during the design phase, rather than importing models externally developed for foreign contexts.
Implement Discovery Projects: Targeted pilot AI projects in partnership with big-tech companies and innovators should be implemented to uncover valuable insights, discover what is possible and viable within relevant contexts; with a view to wider deployment and adoption.
Ethical Considerations: Robust data governance frameworks, clear guidelines and regulations must be instituted to ensure responsible use of AI within the African healthcare environment. This will forestall potential issues with data privacy, bias and equitability.
In summary, integrating AI into healthcare systems in Africa holds great promise, but it is not a panacea for the continent’s healthcare challenges. Whilst the perceived benefits are potentially astronomical, addressing the accompanying challenges is fundamental to ensuring that the potential of AI interventions is maximised, and positive health outcomes are realised. With purposeful implementation, strategic investment and an unwavering commitment to ethical practices, Africa can successfully harness the power of AI to build more resilient and effective healthcare systems for its people.
As the HealthTech landscape continues to evolve alongside emergent technologies, it is crucial for stakeholders to remain informed, committed and proactive in shaping a future where AI systems and healthcare services integrate seamlessly to realise high-value outcomes for the betterment of the African society.
