A Practical Gateway to Medical AI Session 1: Build Your First Clinical AI Concept Map--Connect RWD, RWE, and EBM with LLMs and AI agents.
Medical AI terms are everywhere.
Lost in medical AI papers, talks, or collaboration meetings?
Build your map in 35 minutes.
Medical AI is entering a new phase.
It is no longer only about building prediction models or testing algorithms. Increasingly, AI is becoming part of how clinicians and researchers read medical papers, prepare clinical data, define patient groups, extract information from notes, evaluate evidence, write grants, and collaborate with technical teams.
At the same time, the language of medical AI is becoming harder to follow.
In papers, talks, grant reviews, and collaboration meetings, clinicians now encounter terms such as RWD, RWE, EBM, LLMs, RAG, embeddings, hallucination, AI agents, clinical notes, cohorts, phenotypes, and evidence generation. These terms are often explained separately, making it difficult to see how they connect to actual clinical research workflows.
This 4-session mini-series is designed as a practical gateway into Medical AI.
It does not aim to turn clinicians into AI engineers. Instead, it helps clinicians, researchers, and trainees build a working concept map for understanding how AI may fit into the journey from routine clinical data to usable medical evidence.
In Session 1, we will build the big picture:
clinical data → real-world data → real-world evidence → evidence-based medicine
We will use this map to discuss where LLMs, RAG, and AI agents may fit, what they can support, and what still requires clinical judgment, study design, validation, and domain expertise.
This is not a coding workshop. This is not a product demo. This is a practical concept-map session for people who want to understand the language, logic, and workflow behind Medical AI conversations.
No coding background is required.
It is especially useful for people who:
Are curious about AI but do not want a heavy technical course
Work with patient data, EHR, clinical notes, registries, or outcomes
Want to understand AI terms used in medical papers, talks, and grants
Join AI-related clinical research or collaboration meetings
Want a clearer framework for talking with technical teams
What You’ll Get from Session 1
By the end of this 35-minute session, you will have:
A starter map of most commonly-used AI terms in medical papers and talks
5 questions to ask when reading medical AI papers
Define key terms commonly encountered in medical AI discussions, including RWD, RWE, LLMs, and agents.
Explain how AI tools may support different steps in the pathway from clinical data to evidence generation.
Identify common limitations and validation questions when interpreting AI-related claims in clinical research settings.
Formulate clearer questions for technical collaborators when discussing AI-enabled clinical or research projects.
No coding background is required.
4-Session Mini-Series Roadmap
(Session 1: in person; Sessions 2–4: online; Each session is independent. Welcome to attend one, several, or all sessions.)
1: Build Your First Clinical AI Concept Map
Connect RWD, RWE, EBM, LLMs, and AI agents through one starter map.
2: Find Research Signals in Clinical Data with LLMs
Use LLMs to extract variables, cohorts, and phenotypes from clinical notes.
3: Spot Where Medical AI Gets It Wrong
See how hallucination, bias, and workflow gaps create clinical safety risks.
4: Decode AI Agents in Clinical Research Workflows in 2026
What AI agents can support, and what they cannot replace
Who This Is For
This workshop is designed for:
Physicians · Clinicians · Trainees · Clinical Researchers · Healthcare Professionals · Students in Medicine, Public Health, Nursing, and Biomedical Fields
(No coding background required.)
Clinicians curious about AI but not looking for a heavy technical course
Clinical researchers working with patient data, EHR, notes, registries, or outcomes
Trainees, residents, fellows, and students interested in medical AI
Researchers who want to better understand AI terms used in medical papers and collaboration meetings
Anyone who wants a clearer framework for discussing clinical AI with technical teams
Speaker:
Litong Jiang, PhD, focuses on applying large language models to improve clinical communication and decision-support workflows. She is currently leading the development of an LLM-based clinical tool that is being piloted in a hospital in Beijing. In 2024–2025, Dr. Jiang mentored a capstone student in the Harvard Medical School Master of Bioethics program. She was also a second-prize winner at MIT GrandHack 2025 and a Google Women Techmaker Scholar in 2018.
Format
A four‑session workshop series: Each session is independent, so you are welcome to attend one, several, or all sessions. No coding background is required.
35 minutes per session
Session 1: in person
Sessions 2–4: online
Free to attend
For Session 1
Free in-person workshop
35-minute concept-map talk
Light lunch
Raffle
Post-event networking
No coding required
Time and Location
Date: June 11 2026
Time: 1:30 PM–2:40 PM ET
Talk: 1:45–2:20 PM ET
Location: Harvard Medical School Area, Boston, MA 02115
Schedule:
1:30 PM — Pizza / light lunch
1:45 PM — 35-minute talk
2:20 PM onward — Drawing, networking and discussion
Participant Bonus:
As part of registration, you may submit one clinical question, data task, or research idea for AI-supported guidance. Selected submissions may receive a brief follow-up guide after the workshop with suggested AI tools, workflow ideas, and practical setup directions. Please do not include any patient-identifiable information or sensitive patient data.
Community Support :
The Harvard Medical School Chinese Scholars and Scientists Association (HMSCSSA)
Notice:
By attending this event, you consent to being photographed or recorded for documentation and promotional purposes. Informational only. Speakers’ views are their own and do not represent the views of Harvard Medical School.
Public event information may be shared with attribution. Workshop slides, handouts, concept maps, recordings, and teaching materials are created by Litong Jiang for this clinical AI workshop series. They may not be reproduced, adapted, uploaded, or reused for another course, workshop, or commercial purpose without prior written permission.