AI and Innovation in Medicine Distinction Pathway (AIMDP)

Overview

The AIMDP cultivates residents’ fluency in artificial intelligence (AI), machine learning (ML), and innovative technologies in healthcare. This pathway is designed for trainees who wish to explore, evaluate, and lead in the ethical, clinical, and operational integration of AI into medical education, clinical practice, and research innovation.

Goals

Build foundational knowledge of AI/ML principles in healthcare.
Develop critical appraisal skills for AI tools and clinical research.
Foster hands-on experiences with AI-enhanced clinical workflows.
Promote leadership in the ethical use, governance, and patient communication around AI.

Curriculum Domains & Credit Requirements (85 credits total)

A. Didactics & Foundations (20 credits)

Core AI Curriculum (6 credits): Workshops on AI in medicine, software, statistics, ethics, and diagnostics.
Online Modules (max 4 credits): Asynchronous learning from selected modules and classes, with reflections.
Journal Club (4 credits): Present (3 pts) or attend (1 pt) AI-focused journal discussions.
Conferences/Grand Rounds (4 credits): Attendance at relevant AI and informatics symposia.
AI Ethics Forum (4 credits): Socratic seminars addressing bias, equity, and policy in AI.

B. Experiential Learning (20 credits)

AI Bootcamp/Workshops (10 credits): Immersive training in AI tools, model types, and clinical integration.
Live Coding/Simulation (4 credits): Attend or co-lead sessions using Jupyter/Python.
Clinical Shadowing with AI (3 credits): Direct observation or use of AI tools in patient care, with reflection.
AI Appraisal Practicum (2 credits): Structured critique of clinical AI studies.
Mentored Data Analysis (6 credits): Work with a mentor to apply models to anonymized data.

C. Scholarship & Application (25 credits)

Clinical AI Research (5–15 credits): Hypothesis-driven or implementation projects.
Educational Resource Creation (5–15 credits): Build curricula, guides, or media to support AI literacy.
Conference Presentation (5–10 credits): Poster or oral presentation of scholarly work.
Peer-Reviewed Publication (15 credits): Submit an original article on AI in medicine.
Practice Reflection (2 credits): Case-based reflections on AI’s clinical role.
Elective (up to 10 credits): Personalized, approved projects aligned with program goals.

D. Leadership & Dissemination (20 credits)

Peer Mentorship (5–10 credits): Guide incoming residents or facilitate group learning.
AI Case Conference (5 credits): Organize faculty-guided case discussions.
Policy & Governance (5–15 credits): Join a hospital committee with policy deliverables.
Community Education (5 credits): Lead outreach on AI and health literacy.
Capstone Presentation (5–10 credits): Final synthesis of pathway experience, with optional project.

Faculty Leadership and Advisory

Distinction leads and faculty advisors come from Internal Medicine and its subspecialties, Medical Education, Digital Ethics, Informatics and Diagnostics. Residents are matched with a distinction advisor early and meet periodically to ensure progress.

Questions?

Reach out to the AIMDP distinction director Dr. Shaili Gupta to learn more.

Shaili.gupta@yale.edu

Itemized Credit Requirements List

A. Didactics & Foundations (20 credits required)

Experience	Description	Credits Per Activity	Minimum Credits required
Core AI Curriculum	AI curricular sessions serve as the backbone education in AI. Sessions feature workshops and didactics led by core distinction faculty on AI in Medicine, Medical Software Biostatistics, Diagnostics, and Ethics. Attendance at 6 sessions is required for AIMD completion. As these sessions cover foundational skills, attending at least 3 sessions in your first year in the distinction is encouraged.	1	6
Online Modules	Structured online learning (with reflection log required). Links of suggested modules will be shared periodically.	1 per module (max 4)	2
Journal Club (AI in Healthcare papers)	Participation in at least 2 journal clubs focused on AI. Altogether 3-4 JCs each year will be encouraged for AI related papers. You earn 3 points for presenting the JC, and 1 point each for attending another presenter’s.	1-3 per session	4
Grand Rounds / Conference Attendance	Attend structured conferences e.g. at BIDS related to AI/ML, Health Informatics, or Data Ethics	1 per session	4
AI in Medicine Ethics Forum	Socratic seminar on ethical, legal, and bias implications in AI	2 per session	4

B. Experiential Learning (20 credits required)

Experience	Description	Credits Per Activity	Minimum Credits required
AI Bootcamp / Workshop	Introductory workshops on foundational AI tools, model types, use cases including application in clinical reasoning. Attending at least 2 sessions is required. Reflection pieces required for credit.	5	10
Live Coding Session or Simulation	Attend or co-lead session using Jupyter/Python to demonstrate model validation	4
Clinical Shadowing with AI Tool Use	Observe or engage in patient care involving EHR-integrated AI tools (Report required: including patient feedback, user feedback, and your critical summary)	3 per session	3
AI Appraisal Practicum	Critically appraise an AI clinical study using structured tool	2 per appraisal	2
Mentored Data Analysis/Model Use*	Work with a mentor to explore or test an AI model using anonymized clinical data	6

C. Scholarship & Application (25 credits required)

Experience	Description	Credits Per Activity	Minimum Credits required
Research in Clinical AI*	Hypothesis-driven or implementation research in clinical AI	5-15
AI Curriculum or Educational Tool Creation*	Develop a resource (curriculum, guide, video) for AI literacy in clinical care	5-15
Poster/Presentation – Local or National	Present scholarly AI work at a conference	5 (poster)/ 10 (oral)
Peer-Reviewed Publication	Submit article on AI in Medicine topic	15
AI-in-Clinical Practice Reflection	Written reflection on AI’s clinical role from a real-world case in clinical practice	2 per submission	2
Alternate Elective (approved)	Personalized AI elective related to program goals	Up to 10

D. Leadership & Dissemination (20 credits required)

Experience	Description	Credits Per Activity	Minimum Credits required
AI Pathway Peer Mentor	Mentor incoming residents or lead AI learning group	5-10	5
Lead AI Case Conference	Organize and lead discussion with faculty guidance	5
AI Policy & Governance Committee*	Participate in hospital or department AI policy committee with a discernible product/policy outcome	5-15
Community/Patient-Facing Education*	Lead or contribute to outreach or presentations on AI in health literacy	5	5
AIM Capstone Presentation	Written reflection on AI’s clinical role from a real-world case in clinical practice	5-10	5

*indicates Capstone project categories. Must be a mentored project. Mentor will need to support justification as capstone project.

Frequently Asked Questions

Who should apply to the AIMDP?: Residents interested in the ethical, clinical, and technical dimensions of AI in medicine—including those with backgrounds in computer science, digital health, health equity, or policy—are encouraged to apply. Prior coding experience is not required, although it may be valuable if your interest and capstone project involve significant amount of coding.
Do I need research experience to join?: No. While research is one of the elective components, the pathway supports a wide range of engagement styles including education, quality improvement, ethics, and community outreach.
How is this different from a fellowship in informatics?: AIMDP is a distinction—not a subspecialty or formal certification. It allows residents to explore and lead in this area during residency training, without delaying clinical progression.
What does a capstone project involve?: Capstones are mentored projects that synthesize your experience. They can be research-based, educational, policy-driven, or community-oriented. You’ll present your work at the end of the pathway.
How many credits do I need?: You must complete 85 credits, distributed across four categories: Didactics & Foundations, Experiential Learning, Scholarship & Application, and Leadership & Dissemination.