First published 10/3/2026, 10:12:15 PM · Last updated 10/3/2026, 10:12:15 PM

A Multi-Disease Prediction System with Integrated Explainable AI (XAI)

IEEE proceedings article describing a multi-disease prediction system that integrates explainable AI (XAI). Publisher metadata identifies the content as a proceedings article but provides no further methodological or impact details.

Illustrative image: A visual example of a clinical decision support system for quantitative analysis of whole-slide images (WSI) of tissue biopsy samples. This system has the following key components: quality control to ensure only high-quality data are processed, image description to convert WSI into quantitative features, prediction modeling to develop quantitative diagnostic models, and exploratory analysis to interpret the image feature space. Two case studies are included as examples of predictive modeling and — Unknown author Unknown author/Wikimedia Commons, CC BY 3.0

Categories: science-and-space, technology, public-health

Generated scores

Scores are based on the cited reporting and use a 1–10 scale. Read the methodology.

Confidence: 6/10How certain the assessment is based on the clarity and evidence in the cited reporting.
Geographic reach: 1/10How widely the event reaches, from a local area (1) to worldwide impact (10).
Global importance: 3/10The event's broader significance, from limited importance (1) to major global importance (10).
Impact magnitude: 3/10The scale or severity of the real-world impact, from minor (1) to extreme (10).
Positivity: 7/10How harmful or beneficial the event is, from strongly harmful (1) to strongly positive (10).
Urgency: 2/10How time-sensitive the event is, from low urgency (1) to immediate attention needed (10).

Why it matters

Presents a published system that combines disease prediction with explainable AI in a scholarly proceedings record.

Sources

A Multi-Disease Prediction System with Integrated Explainable AI (XAI)Metadata provided by Crossref. Usage terms

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