


Supporting Surgical Decisions with Verified Medical Imaging Models
Supporting Surgical Decisions with Verified Medical Imaging Models
Supporting Surgical Decisions with Verified Medical Imaging Models
What do we do?
In collaboration with a team of doctors and a PhD researcher, Predictable Machines helped develop and validate an AI model that analyzes radiographs and CT scans to assist in identifying patients who need surgery. Built on a dataset of hundreds of annotated cases, the model wasn’t just trained — it was verified. Our tools were used not only to shape the model’s behavior but also to ensure its decisions matched the expectations of the clinical team. The result: a trustworthy AI that integrates into real medical workflows with confidence and clarity.
In collaboration with a team of doctors and a PhD researcher, Predictable Machines helped develop and validate an AI model that analyzes radiographs and CT scans to assist in identifying patients who need surgery. Built on a dataset of hundreds of annotated cases, the model wasn’t just trained — it was verified. Our tools were used not only to shape the model’s behavior but also to ensure its decisions matched the expectations of the clinical team. The result: a trustworthy AI that integrates into real medical workflows with confidence and clarity.
Industry
Industry
Healthcare
Healthcare
Why is verification important?
Formal verification of AI outputs is essential in medical environments and research because it ensures that doctors are presented with accurate, reliable data for decision-making. By rigorously validating AI systems through mathematical and logical methods, potential errors, biases, or inconsistencies can be identified and corrected before the information is used in clinical practice. This helps doctors trust the AI-generated insights, supports safe and effective patient care, and ensures that medical decisions are based on trustworthy data.
Formal verification of AI outputs is essential in medical environments and research because it ensures that doctors are presented with accurate, reliable data for decision-making. By rigorously validating AI systems through mathematical and logical methods, potential errors, biases, or inconsistencies can be identified and corrected before the information is used in clinical practice. This helps doctors trust the AI-generated insights, supports safe and effective patient care, and ensures that medical decisions are based on trustworthy data.