Posters

Poster topic groups:

• LLMs in Action
• Impact of AI on Human Decision Making
• Governance, Compliance, and RCTs
• Bias and Fairness
• Distribution Shift
• LLMs for Clinical Data Informatics
• Designing Human Centered AI
• Early Detection and Opportunistic Screening
• Trust and Explainability
• Development and Evaluation

A two-stage predictive model using a large language model: LLaMA for real world medication side effects

Gunturkun et al.

Rubrics to prompts: Deploying automatic, expert-level grading of medical student encounter notes with zero-shot large language models

Jamieson et al.

CORAL: Expert-curated medical oncology reports to advance language model inference

Sushil et al.

Guidelines for rigorous evaluation of clinical LLMs for conversational reasoning

Johri et al.

Identifying reasons for contraceptive switching from real-world data using large language models

Miao et al.

Using a large language model prompt engineering platform to automate clinician in-basket responses to patient portal messages

Budko et al.

Automating clinical coding of HPO for rare and undiagnosed diseases with large language models via ontological descent

Patton et al.

Prompt engineering GPT-4 to answer patient inquiries: A real-time implementation in the electronic health record across provider clinics

Patterson et al.

PHI-free clinical note de-identification

Jiang et al.

A deep learning sepsis prediction model improves survival and quality of care

Boussina et al.

Artificial intelligence characterizes neonatal sedation and EEG abnormalities from video data

Gleason et al.

Vision transformer-based decision support for neurosurgical intervention in acute traumatic brain injury: Automated Surgical Intervention Support Tool (ASIST-TBI)

Malhotra et al.

From alert to mortality reduction: Evaluating clinician actions following implementation of a deterioration prediction model in the Kaiser Permanente Advanced Alert Monitor Program

Colacci et al.

A collaborative approach to catalyzing the adoption of human-centered AI in healthcare

Mahvar et al.

When the model trains you: A case study in induced belief revision from a machine learning model’s development

Erdman et al.

Artificial intelligence for pediatric bone age assessment: Proof of concept for embedded model facts into clinical user interface

Fine et al.

Impact of EHR-integrated generative AI draft replies on InBasket work by physicians

Tai-Seale et al.

From governance to trust: The influence of health law, policy and governance on trust in emerging healthcare AI innovation

Hoffman-Peterson et al.

Pre-implementation medical algorithmic audit of artificial intelligence as a medical device to inform user training design

Hogg et al.

Embedding algorithmic auditing into the UK national health service: A multistakeholder, collaborative monitoring framework for assuring regulatory compliance, fairness, and safety

Kale et al.

Artificial intelligence integration in healthcare: A survey of perspectives and trends among U.S. health system leaders

Guleria et al.

Enhancing renal transplant management with AI: International multicenter RCT interim results

Loupy et al.

Deep learning on chest radiographs: A window for predicting future COPD risk

Doroodgar Jorshery et al.

Fairness-Aware Internable Modeling (FAIM) for trustworthy machine learning in healthcare

Liu et al.

Evaluating sociodemographic bias in an AI algorithm to detect cognitive impairment in electronic health records (EHR)

He et al.

Coding inequity: Assessing GPT-4 potential for perpetuating racial and gender biases in healthcare

Zack et al.

Attention-guided fair AI modelling for skin cancer diagnosis

Liu et al.

Towards trustworthy AI: Measuring reliability of machine learning predictions on multiple sclerosis patients

Parimbelli et al.

Segmentation-guided attention for medical out-of-distribution image classification

Rizhko et al.

Accuracy of vision-language models on challenging medical cases

Buckley et al.