Master track
The Doctor, the Machine and the Method
Healthcare AI for those who decide at the bedside
This is the Academy master track. It organizes the full curriculum, while the initial paid offer ships as practical mini-courses, starting with Critical Appraisal of AI in Healthcare.
First paid product
Critical Appraisal of AI in Healthcare, derived from the opening block of the track.
Curriculum
What becomes mini-courses
Module 1
Critical Foundation
By the end of this module, students can read a clinical AI paper, identify biases in a demonstrated model, and distinguish a well-validated tool from a poorly validated one.
Module 1
Critical Foundation
By the end of this module, students can read a clinical AI paper, identify biases in a demonstrated model, and distinguish a well-validated tool from a poorly validated one.
Critical Evaluation of Healthcare AI
The foundation of foundations - without a critical eye, everything else is theater.
Machine Learning and Deep Learning Fundamentals
The greatest conceptual distance for the target audience. Translating engineering language into clinical reasoning.
Healthcare Data
Where the data that feeds clinical AI models comes from and why it matters.
Module 2
Applied Technical Competencies
By the end of this module, students can participate as a technical interlocutor in meetings about developing or acquiring a clinical AI tool.
Module 2
Applied Technical Competencies
By the end of this module, students can participate as a technical interlocutor in meetings about developing or acquiring a clinical AI tool.
Clinical Model Validation Metrics
Sensitivity, specificity, AUC and beyond - the metrics that determine model reliability.
NLP and Large Language Models in Healthcare
The most relevant vector for current practice - how to interact with LLMs in daily clinical work.
Computer Vision in Medicine
Medical imaging + AI: radiology, pathology, dermatology and beyond.
Clinical Decision Support Systems (CDSS)
Module 3
Implementation and Governance
By the end of this module, students can plan the introduction of an AI tool into a real clinical workflow while meeting regulatory requirements.
Module 3
Implementation and Governance
By the end of this module, students can plan the introduction of an AI tool into a real clinical workflow while meeting regulatory requirements.
Ethics, Regulation and Governance
LGPD, ANVISA, FDA - the regulatory framework that physician-leaders need to master.
Implementing AI in Real Clinical Workflows
Where 80% of projects fail - change management and post-implementation monitoring.
Prompt Engineering and Effective Model Interaction
How to use language models effectively, safely and reproducibly in clinical routine.
Module 4
Leadership and Strategy
By the end of this module, students can structure and defend an institutional AI roadmap within their institution.
Module 4
Leadership and Strategy
By the end of this module, students can structure and defend an institutional AI roadmap within their institution.
Health Economic and Impact Assessment
The argument that convinces administrators - ROI, cost-effectiveness and impact assessment.
Leading Healthcare AI Projects
From executor to protagonist - how to structure and defend an institutional AI roadmap.
Next step
First mini-course: critical appraisal
The opening block becomes a smaller, practical, testable offer before any full-track promise. Join the list for the founder Dev Pass cohort.