37:17

MedAI #131: Analyzing and Exposing Vulnerabilities in Language Models | Yibo Wang

01:04:24

MedAI #130: Me-LLaMA: Medical Foundation LLMs for Text Analysis and Beyond | Qianqian Xie

55:58

MedAI #129: Large Scale Multi-Microscope Datasets and their Challenges | Waqas Sultani

51:49

MedAI #127: Improving LLMs for Clinical Named Entity Recognition via Prompt Engineering | Yan Hu

59:47

MedAI #126: Divide & Conquer - Concept-based Models for Efficient Transfer Learning | Shantanu Ghosh

59:00

MedAI #125: Role of Instruction-Tuning and Prompt Engineering in Clinical Domain | Mihir Parmar

58:10

MedAI #124: SleepFM: Multi-modal Representation Learning for Sleep | Rahul Thapa

01:06:51

MedAI #123: Towards Robust Radiogenomic Models for Brain Tumor Characterization | Hassan Mohy-ud-Din

48:02

MedAI #122: Integrating Pathology Images and Genomics Data for Cancer Grading | Xiaohan Xing

57:40

MedAI #121: HECTOR - Multimodal DL model for recurrence risk in endometrial cancer | Sarah Volinsky

01:05:24

MedAI #120: Holistic OR Domain Modeling with Large Vision Language Models | Ege özsoy

43:30

MedAI #119: AI-Driven Advancements in Mammogram Analysis | Aisha Urooj

01:00:39

MedAI #118: Framework for Exposing Vulnerabilities of Clinical LLMs: Breast Cancer | Avisha Das

48:29

MedAI #117: Dynamic Graph Enhanced CL for Chest X-ray Report Generation | Mingjie Li

48:02

MedAI #116: Deep Symmetry-sensitive networks for detecting brain diseases | Arko Barman

01:08:02

MedAI #115: Behave like a Doctor: Clinical Process-Aware Medical Dialogue System | Kaishuai Xu

32:12

MedAI #114: Ambiguous medical image segmentation using diffusion models | Aimon Rahman

01:02:43

MedAI #113: Generative AI for easily synthesizable and structurally novel antibiotics | Kyle Swanson

48:53

MedAI #112: Radiologist-centered AI with Eye Tracking Techniques | Bin Wang

01:06:18

MedAI #111: Self-supervised learning for chest x-ray analysis | Syed Muhammad Anwar

55:03

MedAI #110: AI-driven fast and accurate cell phenotyping in multiplex images | Muhammad Shaban

51:33

MedAI #109: Towards Fundamental Biomedical AI | Che Liu and Zhongwei Wan

49:15

MedAI #108: Data Efficient Learning in medical image segmentation | Yi Lin

01:00:19

MedAI #107: Large Language Models as Universal Medical Forecasters | Zeljko Kraljevic

01:00:36

MedAI #106: Multimodal Clinical Benchmark for Emergency Care (MC-BEC) | Emma Chen

01:12:24

MedAI #105: EHRXQA: A Multi-Modal Question Answering Dataset for EHRs | Seongsu Bae

01:01:57

MedAI #104: Reveal to Revise - How to Uncover and Correct Biases of Deep Models | Maximilian Dreyer

44:17

MedAI #103: Multimodal Brain Age Estimation Using Population-Graph Learning | Margarita Bintsi

01:01:48

MedAI #102: Visual-language foundation model for pathology research and education | Zhi Huang

39:32

MedAI #101: Dual meta-learning framework for longitudinal brain tissue segmentation | Chunfeng Lian

57:20

MedAI #100: Harnessing the Power of AI in Radiology | Ayis Pyrros

55:04

MedAI #99: Embracing Imperfection in Medical Vision | Aishik Konwer

01:02:39

MedAI #97: AI for Personalized Cancer Screening | Adam Yala

01:03:40

MedAI #96: Denoising Diffusion Models for Medical Image Analysis | Julia Wolleb

01:02:03

MedAI #95: Benchmarking Causal Effects from Observational Studies using Data | Zeshan Hussain

57:42

MedAI #94: Scalable Natural Language Processing for Transforming Medicine | Monica Agrawal

59:56

MedAI #93: Toward Universal Medical Image Segmentation | Yunhe Gao

57:44

MedAI #92: Generative Diffusion Models for Medical Imaging | Hyungjin Chung

01:00:00

MedAI #91: Disruptive Autoencoders: Low-level features for Pre-training | Jeya Maria Jose Valanarasu

55:14

MedAI #90: Training and Application of Language Models in Medicine | Keno Bressem

53:08

MedAI #89: MedCLIP - Contrastive Learning from Unpaired Medical Images and Text | Zifeng Wang

57:22

MedAI #88: Distilling Step-by-Step! Outperforming LLMs with Smaller Model Sizes | Cheng-Yu Hsieh

46:41

MedAI #87: Risk-based approaches for treatment effect heterogeneity assessment | Alexandros Rekkas

59:41

MedAI #86: Lessons on the path from Code to Clinic | Yun Liu

55:41

MedAI #85: The EMory BrEast imaging Dataset (EMBED) | Jiwoong Jason Jeong

45:52

MedAI #83: Modeling Multivariate Biosignals With GNNs & Structured State Space Models | Siyi Tang

56:24

MedAI #82: Robust and Reliable Federated Learning for Heterogeneous Medical Images | Meirui Jiang

50:21

MedAI #81: Why did the Model Fail? Attributing Performance to Distribution Shifts | Haoran Zhang

01:01:15

MedAI #80: Foundation Models For Biomedical QA | Elliot Bolton

56:04

MedAI #79: Avoiding Model Aging by Continually Editing & Repairing Deployed Models | Tom Hartvigsen

01:04:31

MedAI #78: Foundation Models for Medical AI | Vivek Natarajan

01:00:33

MedAI #77: Leveraging Heterogeneity in Time-to-Event (Survival) Predictions | Chirag Nagpal

54:34

MedAI #76: Multimodal learning with graphs for biomedical applications | Yasha Ektefaie

01:03:22

MedAI #75: Using AI to Diagnose and Assess Parkinson's Disease | Yuzhe Yang

48:42

MedAI #74: Meerkat: Interactive Data Systems For Unstructured Data & Foundation Models

01:08:44

MedAI #73: Empowering Biomedical Informatics with Open Resources | Houcemeddine Turki

01:02:00

MedAI #72: Large Language Models Encode Clinical Knowledge | Karan Singhal

52:02

MedAI #71: Opioid Use Disorder Prediction Using AI and Existing Risk Models | Sajjad Fouladvand

40:29

MedAI #70: Denoising diffusion models for denoising diffusion MRI | Tiange Xiang

57:29

MedAI #69: Diagnosing failures of fairness transfer across distribution shift | Sanmi Koyejo