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| Lydia Ehisuoria Ohonsi, Esq. |
INTRODUCTION
Generative AI is a specialised branch of artificial intelligence that uses advanced machine learning algorithms to create new, original content such as text, images, videos, or synthetic data. In healthcare, generative AI generates synthetic medical data that closely mimics real-world medical information, which can be used to train AI models, simulate clinical trials, automate clinical documentation, and personalise patient care. This technology can help improve diagnostic accuracy, automate routine administrative tasks, enhance patient engagement, and reduce healthcare costs by streamlining workflows. Generative AI excels at making sense of unstructured healthcare data like doctors' notes, medical images, and audio consultations, providing healthcare professionals with more efficient and precise tools for decision-making and care delivery.
An overview of potential benefits, risks, and recommendations for responsible adoption in Nigeria:
Potential Benefits of Generative AI in Nigerian Healthcare
- Improved Patient Outcomes: Generative AI can assist in accurate diagnoses, personalised treatment plans, and better disease management, contributing to enhanced health outcomes. AI-powered diagnostic tools are already helping to detect conditions such as breast cancer earlier and more accurately in Nigeria.
- Addressing Healthcare Workforce Shortages: Nigeria faces a critical shortage of healthcare professionals. Generative AI can alleviate this by automating routine tasks, supporting decision-making, and extending healthcare services to underserved areas, thereby reducing workload and improving access.
- Operational Efficiency: Automation and digitalisation of healthcare workflows can streamline administrative tasks, reduce errors, and improve resource allocation, making healthcare delivery more efficient.
- Data-Driven Decisions: AI systems can analyse vast health data to provide actionable insights that support proactive interventions and improved healthcare governance
- Innovation and Capacity Building: Adoption of generative AI encourages technological advancement and innovation, promoting local capacity development in AI healthcare applications
Risks of Generative AI in Nigerian Healthcare
1. Data Privacy and Security Concerns: AI systems process sensitive personal health data, raising concerns about data misuse, breaches, and inadequate patient privacy protections.
2. Bias and Inaccuracy: AI models trained on biased or low-quality data risk producing inaccurate or discriminatory outcomes, potentially harming patients.
3. Ethical and Trust Issues: Lack of transparency in AI decision-making and possible disruption of the patient-provider relationship can reduce trust and acceptance among patients and healthcare workers.
4. Infrastructural Challenges: Limited digital infrastructure and connectivity in parts of Nigeria may hinder effective AI deployment and access.
5. Regulatory and Liability Gaps: Absence or insufficiency of clear regulations around AI in healthcare can delay adoption or lead to misuse without accountability mechanisms.
Recommendations for safe and effective use of generative AI in the medical sector in Nigeria.
1. Strengthen Data Protection: Implement strict data governance frameworks aligned with Nigeria’s NDPR to protect patient privacy and secure healthcare data.
Ensure AI Transparency and Accountability: Involve medical professionals, patients, and ethicists in AI system design to improve transparency, trust, and ethical compliance. Regular audits and impact assessments should be mandated.
2. Invest in Infrastructure and Capacity Building: Enhance healthcare digital infrastructure and train healthcare workers and AI developers on AI technologies, ethics, and best practices to drive adoption and innovation.
3. Develop Regulatory Frameworks: Establish clear guidelines and oversight bodies for AI use in healthcare to manage risks, ensure patient safety, and provide mechanisms for redress in case of harm.
4. Promote Inclusive and Bias-Free AI: Use diverse and representative datasets to train AI models and continuously monitor for biases to promote equitable healthcare outcomes.
5. Develop Robust Regulatory Frameworks: Establish clear guidelines for data privacy, AI validation, liability, and ethical use tailored to Nigeria's healthcare context.
6. Improve Data Infrastructure: Invest in data collection standards, data governance frameworks, and infrastructure improvements to support reliable AI applications.
7. Capacity Building: Train healthcare workers and AI specialists to build local expertise and ensure proper AI system deployment and maintenance.
8. Ensure Transparency: AI systems should be designed for transparency so that both patients and providers can understand how AI decisions are made, fostering trust.
9. Include Stakeholders: Involve medical professionals, patients, policymakers, and AI developers collaboratively in AI technology development and deployment for better alignment with healthcare needs.
10. Implement Cybersecurity Measures: Strengthen cybersecurity to protect patient data and maintain the integrity of AI applications.
11. Address Bias: Develop ethical guidelines and bias mitigation strategies for AI, ensuring fairness and equity in medical AI outputs.
Generative AI holds promising potential for advancing healthcare service delivery in Nigeria by enhancing diagnostic tools and communication. Still, its successful adoption depends on addressing these significant risks through coordinated efforts in regulation, capacity building, infrastructure, and ethics.
In conclusion, generative AI can transform Nigeria’s healthcare sector by improving diagnostics, efficiency, and access, especially amid workforce challenges. However, realising these benefits requires proactive steps to address data privacy, ethics, infrastructure, and regulatory measures to foster safe, trusted, and effective AI healthcare solutions in Nigeria.
October, 2025.
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