The British Computer Society (BCS) Data Management Specialist Group has officially kicked off its inaugural Early Career Spotlight webinar series, featuring rising artificial intelligence engineer Ayodeji Atte.
The premier session highlighted Atte’s award-winning master’s research from Birmingham City University: XAILUNG, an intelligent medical system designed to change how AI is used in lung cancer prediction.
Moving Past the “Black Box” of AI
Many modern AI systems are treated as “black boxes” they give a final answer or diagnostic percentage, but doctors cannot see how the computer reached that conclusion. In critical fields like healthcare, this opacity creates major safety and trust concerns.
Atte’s XAILUNG framework solves this by putting transparency first. The system simultaneously analyzes three completely different types of information: 3D chest CT scans, 2D pathology slide images, and a patient’s basic clinical data. Instead of just delivering a final risk score, the system features a live dashboard that maps out exactly which pieces of medical data influenced its decision.
“A Forethought, Not an Afterthought”
The presentation drew high praise from tech professionals and researchers attending the session. Attendees noted that the framework represents a significant shift from making AI models purely accurate to making them safe, traceable, and practical for real-world doctors.
During the webinar, Atte shared a core engineering philosophy that resonated deeply with the audience:

“Every time you’re designing an AI system, treat explainability as a forethought, not an afterthought.”
By building explainability directly into the foundation of the technology, the XAILUNG framework allows clinicians to question, audit, and truly trust the system’s insights rather than blindly relying on automated predictions.
A Template for the Future
Atte openly walked attendees through the entire lifecycle of the project detailing the backend code architecture, how the datasets were handled, and how he resolved processing lag.
The underlying engineering template behind XAILUNG does more than just support medical decisions. It serves as a practical blueprint for Digital Twin Modelling and real-time automation across other major industries like manufacturing and security, where complex physical data streams must be safely monitored by digital systems in real time.
About Ayodeji Atte
Ayodeji Atte is an AI and Machine Learning Engineer who recently graduated with Distinction from Birmingham City University. His work focuses on Explainable AI (XAI), multimodal deep learning, and building transparent full-stack intelligent applications.

