AI in Healthcare Diagnostics

In this newsletter:

• AI in Healthcare Diagnostics

• MTN-Airtel Network Sharing

• Data Centre Power & Sustainability

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AI News Headlines:

AI in Healthcare Diagnostics
AI-driven diagnostic tools now achieve 90%+ accuracy in specialties like radiology and oncology, outperforming human clinicians in early detection (e.g., breast cancer detection at 91% vs. 74% for radiologists)1. Key advancements include:

• Multimodal data integration: Combining imaging (MRI/ultrasound), genomic data, and clinical notes via NLP to create personalized treatment plans.

• Real-time CDSS: Systems analyze ECG, lab results, and vital signs to flag critical cases like pulmonary embolisms, reducing diagnostic errors by up to 15%.

• Precision medicine: AI identifies genetic variants in medulloblastoma, enabling targeted therapies, while platforms like Epic’s AI agents optimize treatment paths.

MTN-Airtel Network Sharing
Telecom giants MTN and Airtel are sharing infrastructure in Uganda and Nigeria to reduce costs and expand rural coverage. While not explicitly AI-focused, this collaboration could indirectly support AI/ML workloads by improving connectivity for data-intensive applications like remote diagnostics or IoT-driven healthcare.

Data Centre Power & Sustainability
Rising energy demands for AI/cloud computing are driving the PPA market, projected to grow at 32.8% CAGR through 2034. Key developments:

• 24/7 renewable matching: Data centers aim to align energy use with renewable sources, mitigating volatility.

• Kao Data’s AI infrastructure: Partnering with Ori to deploy NVIDIA H200 GPUs in the UK, powered by 100% renewable energy, supporting scalable AI workloads.

Emerging Trends

• Interoperability: Efforts to integrate AI tools with GP systems (e.g., cardiovascular risk calculators) and aged care data platforms.

• Ethical AI: Focus on bias mitigation and transparency, with frameworks like Infosys’ Responsible AI Suite gaining traction.

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Zulkar Naim

Founder & CEO of OpenPedia

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