By Meseret Mamuye, Yordanos Sintayehu from (AHRI), and Christine Ger Ochola (APHRC)

Artificial Intelligence (AI) is no longer a future concept in healthcare; it is already shaping how diagnoses are made, treatments are planned, and health systems operate. Technologies such as machine learning, natural language processing, and predictive analytics are increasingly embedded in clinical and public health decision-making.

In principle, these technologies offer enormous promise. AI can support faster and more accurate diagnoses, particularly through medical image analysis and clinical decision support tools. It also enables more personalized care by tailoring treatment plans based on individual health and genetic data. For overstretched health systems, AI-driven tools can improve efficiency by streamlining workflows, strengthening disease surveillance, and supporting population-level public health responses.

In Africa, where shortages of healthcare workers and limited resources remain persistent challenges, these benefits are often presented as transformative. Recent evidence suggests AI is expected to play a growing role in strengthening healthcare delivery across the continent. However, the assumption that innovation alone will close systemic gaps deserves closer scrutiny.

The rapid adoption of AI in healthcare, especially in low- and middle-income countries, has outpaced the development of ethical, legal, and governance safeguards. This imbalance creates a critical risk: technologies designed to improve care may instead amplify harm if deployed without adequate oversight.

One of the clearest examples is the increasing use of large language models (LLMs) in health-related applications. These systems generate outputs based on statistical patterns rather than factual verification. As a result, they can produce responses that sound authoritative but are incorrect, a phenomenon commonly described as “hallucination.” In clinical contexts, such errors are not trivial. Inaccurate recommendations or misleading summaries can directly compromise patient safety (Gibson & Tang, 2025).

Yet focusing only on incorrect outputs understates the ethical challenge. In African healthcare settings, where regulatory frameworks and data protection mechanisms are often weak or inconsistently enforced, the more serious risks lie in data governance, privacy protection, and accountability.

Consider, for instance, a scenario in which a  hospital adopts an AI diagnostic tool to improve efficiency and reduce costs. Due to budget constraints, patient data are transferred to a third-party cloud service with minimal encryption and limited contractual safeguards. This is not an unusual situation in low-resource settings, where affordable digital infrastructure is prioritized over robust security.

A single vulnerability leads to a data breach. Sensitive patient records such as names, locations, HIV status, cancer diagnoses, or mental health conditions are exposed. While the institutional consequences include regulatory penalties and reputational damage, the human consequences are far more severe. Patients face stigma, workplace discrimination, or denial of insurance coverage, not because of a medical error, but because ethical protections failed.

This is not a hypothetical technical problem. It reflects a governance failure. When ethical standards are treated as secondary to speed, cost, or innovation, harm becomes predictable rather than accidental.

Transparency and informed consent further complicate AI deployment in African healthcare. Many AI systems operate as “black boxes,” offering limited explanations for their outputs. In contexts where health literacy varies widely, meaningful patient consent cannot be reduced to complex end-user agreements or imported legal templates. Without clear communication and accountability, trust in both technology and healthcare institutions erodes.

Bias is another critical concern. AI models trained on data from high-income countries or narrowly defined populations often fail to perform accurately in African settings. Evidence shows that models built on incomplete or unrepresentative datasets can reinforce existing health inequalities rather than reduce them (Udegbe & Ekesiobi, 2024). A diagnostic tool that performs well for populations in Europe or North America may deliver delayed or inaccurate results for African patients, particularly women and marginalized groups.

The absence of strong institutional oversight magnifies these risks. In 2021, the World Health Organization (WHO) released ethical principles for AI in health. These principles emphasize transparency, fairness, accountability, and human-centered design. However, many African countries lack dedicated regulatory organizations and enforcement mechanisms to put these principles into practice (WHO, 2021). This governance gap leaves healthcare institutions to navigate ethical decisions without clear guidance or accountability structures. Ethical AI governance in healthcare cannot be optional or externally imposed. It must be locally grounded, institutionally enforced, and led by senior leadership. Policymakers, developers, healthcare providers, and researchers share responsibility for ensuring that AI tools are safe, equitable, and appropriate for the contexts in which they are deployed.

In African health systems, where resources are limited and public trust is fragile, the cost of ethical failure is especially high. Innovation that undermines patient dignity, privacy, or equity is not progress. If AI is to strengthen healthcare genuinely, ethics must be embedded at every stage, from data collection and model development to deployment and evaluation. Only through inclusive data practices, strong governance frameworks, and meaningful oversight can African healthcare systems harness AI’s potential without sacrificing trust, safety, and fairness.