Unpacking the Black Box: Why Explainable AI Matters
Artificial intelligence is reshaping healthcare, but its most consequential risks aren’t tubes of metal marching toward dystopian futures. They’re systems whose decisions are opaque, creating trust gaps when lives are on the line. Dr. Sakib Mostafa’s work centers on explainable AI—the science of making AI decisions understandable so clinicians can trust, validate, and improve them.
A Journey Fueled by Curiosity and Caution
Growing up in Bangladesh, Mostafa grappled with the tension between wonder and fear surrounding technology. “I’m the kind of person who really likes to face the fear rather than running away from it,” he recalls. That mindset has propelled him through a career focused on dissecting how AI arrives at its conclusions, not just what conclusions it reaches.
From Leaves to Lesions: A PhD That Probed the Black Box
During his PhD at the University of Saskatchewan, Mostafa and his team examined a plant-leaf disease model only to discover the system was fixating on edge outlines rather than the full image content. This eye-opening finding underscored a crucial lesson: accuracy alone isn’t enough. For AI in medicine, clinicians must understand the reasoning behind a decision to act on it.
Stanford: Translating Explainability into Cancer Care
Today, at Stanford University’s Department of Radiation Oncology, Mostafa leads efforts to build AI tools that detect cancer by integrating diverse data streams—genomics, medical images, and patient histories. Like a human diagnostician, the system weighs multiple data types to reach a diagnosis, but with the added rigor of explainable reasoning that clinicians can audit and learn from.
Why Explainability Improves Performance
Mostafa emphasizes that explanations aren’t a luxury feature; they’re a mechanism to improve the models themselves. By identifying which data portions drive incorrect predictions, researchers can refine inputs, adjust training strategies, and ultimately build more reliable systems. “If we create an explanation of a model, we can improve the model,” he notes. This loop—explanation leading to correction—helps bridge the gap between raw performance and trustworthy clinical utility.
Building a Futuristic Diagnostic Aid
The team’s ambition extends beyond simply signaling cancer’s presence. They aim to determine cancer stage and type while uncovering patterns across data types that traditional methods might miss. Such a tool could one day pilot at Stanford Hospital, augmenting physicians’ expertise rather than replacing it.
Trust, Safety, and the Human in the Loop
As AI moves into high-stakes areas like law enforcement-adjacent fields and medicine, the emphasis on transparency grows louder. A medical decision supported by inexplicable AI is not merely a technical flaw; it’s a risk to patient safety and clinician confidence. Mostafa’s work champions a future where AI serves as a knowledgeable collaborator, with traceable reasoning that clinicians can interrogate and, if needed, challenge.
What Comes Next
The end goal is practical: a robust, explainable AI system capable of multi-modal cancer detection, precise staging, and nuanced typing. If successful, this technology could transform diagnostics by delivering faster, more accurate assessments while preserving the essential human judgment at every step of care.
“That’s the end goal for us,” Mostafa says. In bridging fear and function, his research shows how explainable AI can turn a black box into a trusted partner in the fight against cancer.
