Search Results for author:
Found 9 papers, 2 papers with code
OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models
We present OpenMedLM, a prompting platform which delivers state-of-the-art (SOTA) performance for OS LLMs on medical benchmarks.
Standing on FURM ground -- A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems
Our novel contributions - usefulness estimates by simulation, financial projections to quantify sustainability, and a process to do ethical assessments - as well as their underlying methods and open source tools, are available for other healthcare systems to conduct actionable evaluations of candidate AI solutions.
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records
The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care.
EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models
The success of foundation models creates new challenges for healthcare ML by requiring access to shared pretrained models to validate performance benefits.
Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery
Our objective was to determine whether two LLMs can serve information needs submitted by physicians as questions to an informatics consultation service in a safe and concordant manner.
The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs
The successes of foundation models such as ChatGPT and AlphaFold have spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations.
DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record
Machine learning (ML) applications in healthcare are extensively researched, but successful translations to the bedside are scant.
MoleHD: Ultra-Low-Cost Drug Discovery using Hyperdimensional Computing
In this paper, we propose a viable alternative to existing learning methods by presenting MoleHD, a method based on brain-inspired hyperdimensional computing (HDC) for molecular property prediction.
HDXplore: Automated Blackbox Testing of Brain-Inspired Hyperdimensional Computing
In this paper, we systematically expose the unexpected or incorrect behaviors of HDC models by developing HDXplore, a blackbox differential testing-based framework.
