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Review by Mehdi Zoghaib of: “Transforming Healthcare in Africa: The Role of Artificial Intelligence in Combatting Infectious Diseases, Neglected Tropical Diseases, and Antimicrobial Resistance”

Source: ANDIGEMA, A., Tania Cyrielle, N. N., Daniel Laetitia, N., & Ekwelle, E. (2024). “Transforming Healthcare in Africa: The Role of Artificial Intelligence in Combatting Infectious Diseases, Neglected Tropical Diseases, and Antimicrobial Resistance”, Preprint

Keywords: AI, Africa, Healthcare

Abstract: Artificial Intelligence (AI) has the potential to revolutionize healthcare in Africa by addressing critical challenges, including infectious diseases, neglected tropical diseases (NTDs), and antimicrobial resistance (AMR). This review explores AI's applications, benefits, and the challenges of integrating AI into African healthcare systems.

Introduction - Africa faces significant healthcare challenges due to infectious diseases (e.g., malaria, HIV/AIDS), NTDs, and AMR. - AI offers transformative opportunities in early detection, diagnosis, and treatment, especially in resource-limited settings. - Objectives: - Highlight AI's potential in disease control and healthcare delivery. - Address barriers to AI integration, such as infrastructure and ethical considerations. - AI technologies can improve health outcomes and align with sustainable development goals.

2. Role of AI in Infectious Disease Surveillance and Outbreak Detection -

AI facilitates real-time tracking and prediction of disease outbreaks using diverse data sources, such as social media, electronic health records, and news reports.

Examples of successful AI applications:

mPedigree: Verifies the authenticity of medicines to prevent counterfeit drugs.

AfriHealth: Monitors disease outbreaks by analyzing multiple data sources. - PREDICT Program: Identifies pandemic threats in wildlife populations.

- epitrack: Uses mobile data to model disease spread and guide interventions. - AI's adaptability and predictive power enhance outbreak management and resource allocation.

3. AI-Enabled Diagnosis and Treatment

AI-powered tools provide faster and more accurate diagnoses by analyzing imaging scans, genetic information, and health records. - Examples: - AI diagnosing malaria from blood samples. AI improving tuberculosis detection via chest X-rays but also Satellite imagery to manage diseases like schistosomiasis.

AI enables personalized treatment plans by leveraging patient-specific data. - Challenges: - Limited infrastructure for data collection in Africa. - Potential biases in AI models due to incomplete or skewed datasets. - Ethical concerns, such as privacy and transparency in AI algorithms.

4. AI-Driven Drug Discovery and Development

AI accelerates the discovery of new drugs and combats AMR through:

Target identification: Pinpointing pathogen characteristics for drug development. Virtual screening: Rapidly assessing compound libraries for potential drug candidates. Predictive analytics: Monitoring resistance trends and supporting proactive interventions. - Drug repositioning: Identifying alternative uses for existing medications. African projects utilizing AI for HIV drug discovery and NTD treatments show promising results.

5. Challenges and Limitations of AI in Africa - Technological Constraints:

Limited access to computational resources. Shortage of trained AI professionals. Data Accessibility: Lack of robust healthcare infrastructure and inconsistent data collection methods. Need for improved data-sharing frameworks.

Cultural and Ethical Barriers: Resistance to AI adoption due to privacy concerns and lack of trust. Algorithmic biases that could exacerbate health disparities.

Establishing ethical guidelines and sustainable partnerships is crucial for successful AI implementation.

Conclusion

AI presents immense potential to transform African healthcare, improving disease surveillance, diagnosis, treatment, and drug discovery. We have to understand the necessity of addressing ethical, infrastructural, and data-related challenges is essential for realizing AI's benefits. The authors of this paper Recommendates to develop tailored AI solutions for Africa's healthcare context. They Encourage collaborations among governments, tech companies, and local communities. Furthermore, they priorioritize ethical regulations and data governance frameworks. Finally,The future of AI in African healthcare is promising, contingent upon overcoming implementation challenges.

Competing interests

The author declares that they have no competing interests.