A course for pathologists & AI practitioners
Whether you are a pathologist finding your way through the fast-paced, revolutionary field of AI, or an AI practitioner looking to make an impact in healthcare, this course explores the intricate connections between the two. It starts with the medium pathologists know best — the tissue — and applies the recent advances in machine learning to open new ways of exploring and interrogating human biology, with applications across diagnosis, prognosis, and predictive medicine.
Who this is for
Most material assumes you already speak either medicine or machine learning. This course meets you on the side you know and carries you across.
Learn what these systems are, what they actually do to a slide, where they fail, and how to judge a vendor's claims with the same rigour you bring to a diagnosis.
Understand where the data comes from — fixation, staining, scanning — where AI can add value, and why clinical validation and regulation decide whether a model ever reaches a patient.
What you'll be able to do
Map a clinical need to the right AI task — detection, grading, biomarker scoring, prognosis, or prediction — and its deployment strategy.
Take raw slides through tiling, stain normalisation, quality control, and feature extraction into analysis-ready data.
Reason about CNNs, vision transformers, graph networks, and multimodal fusion — and when each earns its place.
Design splits, metrics, and external cohorts that hold up to regulatory-grade scrutiny and real-world drift.
Ship models into the LIS with MLOps, monitoring, and change-control that hospitals and clinicians will actually accept.
Weigh ROI, reimbursement, ethics, and equity so a tool can survive beyond the pilot and reach patients.
Curriculum
Build the technical foundations, understand the domain, then put both to work across real diagnostic and prognostic practice. Tap any lecture to expand.
What digital and computational pathology are, why they are emerging now, and a framework for classifying AI systems by clinical intent and methodological complexity.
The book behind the course
The course follows AI in Pathology: From Implementation to Impact — a complete treatment of the field, from the physics of a stained slide to the economics of a deployed diagnostic. The chapters are your resource for deeper detail and a lasting reference long after the course ends.
Your instructors
The course is led by the book's authors — a team working at the intersection of machine learning and diagnostic medicine.
FAQ
No. The technical track explains methods conceptually, then shows how to choose and use them. Clinicians can follow the reasoning without writing code; engineers get the depth to decide which models to use.
No. The clinical track introduces the slide lifecycle, staining, and diagnostic practice from first principles, so AI practitioners can start from zero on the pathology side.
No — you can buy the book AI in Pathology: From Implementation to Impact separately and use it as a reference, but you don't need it to follow the course.
Self-paced video lectures that follow the eleven-module curriculum, with supporting demos, references to relevant case studies, and checkpoints.
That's the point of the second half. You'll cover validation, MLOps, LIS integration, regulation, and the economics of getting a tool past the pilot stage.
Enrol
Join pathologists and machine-learning practitioners building the next generation of diagnostic tools. Leave your email and we'll send enrolment details.