The Radiologist's Co-Pilot

This is a conceptual UI/UX project for an AI-assisted diagnostic imaging platform. The design provides radiologists with a "co-pilot" that uses AI to highlight potential anomalies in medical scans, aiming to increase diagnostic accuracy and efficiency. The core of this project is not just presenting AI data, but designing a system built on trust, explainability, and ultimate clinician control.

Radiologists are expert users facing immense pressure. They review hundreds of scans daily, leading to fatigue and an increased risk of perceptual error—missing small but critical findings. While AI holds the promise to help, most tools are treated with skepticism. They are often perceived as a "black box," creating two major UX challenges:

Lack of Trust: If a clinician doesn't understand why an AI is making a suggestion, they won't trust it.

Loss of Agency: Tools that feel like they are automating a decision, rather than augmenting the expert, are often rejected as intrusive and unsafe.

My objective was to design an interface that seamlessly integrates AI into the radiology workflow by making it feel like a trusted, transparent assistant, not a replacement.

For the User (Radiologist): To reduce cognitive load, increase diagnostic confidence, and decrease review time, all while maintaining full clinical authority.

For the Patient: To benefit from a second, tireless "set of eyes" on every scan, leading to potentially earlier and more accurate diagnoses.

Role: UX/UI Designer

Skills: Human-AI Interaction, UX Strategy, Information Architecture, UI Design, Prototyping

My entire strategy was built on one principle: Design for Trust and Control. To achieve this, I focused on three pillars of human-AI interaction design.

Augment, Don't Automate: The AI's role is to draw attention, not to make a diagnosis.

Make it Explainable: Provide insight into the AI's confidence and reasoning.

Keep the Human-in-the-Loop: The radiologist must always have the final, un-gated decision.

The final design is a dark-mode interface (standard for radiology reading rooms) that presents AI findings as an interactive layer, not a final verdict.

This is the core of the human-AI interaction. Each potential finding is presented on a card with two critical features:

The Confidence Score: Each finding is paired with a percentage (e.g., "Confidence: 89%"). This is crucial. It tells the radiologist that the AI is making an educated guess, not stating a fact, thereby respecting the radiologist's superior expertise.

The "Human-in-the-Loop" Buttons: Every card has clear [Confirm Finding] and [Dismiss] buttons. This gives the radiologist explicit control to accept or reject the AI's suggestion, reinforcing their role as the ultimate decision-maker before anything is added to the final report.

This design approach is projected to:

Increase AI Adoption by building trust through transparency and user control.

Improve Diagnostic Accuracy by reducing the risk of perceptual errors.

Enhance Radiologist Efficiency by helping them focus their attention more effectively.

The future of AI in medicine is not a battle of Human vs. Machine, but a partnership. The success of this partnership hinges on thoughtful, empathetic design that empowers clinical experts. This project explores how to build that bridge, creating tools that are not only powerful but also profoundly trustworthy.