David Uche Communications Officer, ASLM

Understanding AI’s Role in Medical Diagnostics

AI refers to the ability of machines to simulate human intelligence processes such as learning, reasoning, and decision-making. In medicine, AI-powered tools are increasingly used to improve diagnostic accuracy, accelerate decision-making, and enhance patient outcomes. Technologies such as machine learning, computer vision, and natural language processing are at the core of these innovations.

There are three main types of AI:

• Narrow AI (task-specific intelligence),
• General AI (multi-task intelligence),
• Super AI (hypothetical human- level AI with self-awareness).

Applications Across Disciplines

Radiology benefits from AI’s ability to analyze imaging data from X-rays to CT scans, and MRIs more efficiently than traditional human review.

Pathology is seeing a shift toward digital diagnostics, where AI interprets tissue slides and identifies abnormalities with remarkable precision.

Genomics, the central focus  of the presentation, is experiencing a transformation as AI facilitates:

• Disease prediction and diagnosis,
• Personalized medicine,
• Drug discovery and gene editing,
• Real-time tracking of infectious disease

Real-World Use Cases From Rare Diseases to ICU Surveillance

AI-driven genomics has helped diagnose rare diseases, 95% of which currently have no cure and have reduced diagnosis time from years to months. Genome-wide association studies (GWAS) are also enabling early detection and One standout example is AlphaFold, an AI system developed by DeepMind, which predicts 3D protein structures and is revolutionizing structural genomics and vaccine development.

Benefits and Future  Outlook

AI improves diagnostic accuracy, speed, and cost-efficiency, and ensures consistency by reducing human error. However, challenges like data privacy, model bias, and regulatory limitations must be addressed to fully realize its potential.

The future of AI in diagnostics includes:

• Personalized medicine tailored to individual genetic profiles,
• Real-time monitoring via AI- powered wearables,
• Remote diagnosis in rural and underserved regions, and
• AI-assisted decision-making for clinicians.

Conclusion

AI is not just a futuristic tool, it is already transforming medical diagnostics by enabling faster, more accurate, and more personalized healthcare solutions. With ongoing innovation and investment, AI will continue to bridge diagnostic gaps, improve outcomes, and empower health systems across Africa and beyond.