Artificial Intelligence in Healthcare and Education

Artificial Intelligence-Based Chatbots in Genetic Counseling Practice: Current Uptake, Utilization, and Perspectives

This study surveys genetic counselors on AI chatbot use in clinical practice, finding limited adoption but potential for workflow efficiency with proper training and regulation.

Benchmarking generative AI tools for literature retrieval and summarization in genomic variant interpretation.

This study benchmarks generative AI tools for summarizing genomic variant literature, finding VarChat and Gpt-4o perform best with expert validation needed for reliability.

Diagnostic Accuracy of Large Language Models for Rare Diseases: A Systematic Review and Meta-Analysis

This paper evaluates the diagnostic accuracy of large language models for rare diseases through a systematic review and meta-analysis, finding substantial variation in performance across benchmarks and high risk of bias in all studies.

Artificial intelligence in clinical genetics: current practice and attitudes among the clinical genetics workforce

The paper surveys US-based clinical genetics professionals to assess their knowledge, use, and attitudes toward AI applications in clinical genetics, finding low knowledge and usage but strong support for education.

Validation of deep learning enabled web based and smartphone optimized application RadAnalyzer to measure vertebral heart size and vertebral left atrial size in dogs

This study validates RadAnalyzer, a deep learning tool, for measuring VHS and VLAS in dogs, showing strong correlation and minimal bias compared to human observers.

Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support tools

This study benchmarks large language models against a traditional diagnostic tool for genetic disease diagnosis using 5,213 case reports, finding LLMs lag behind in accuracy.

Benchmarking Large Language Models for Predictive Modeling in Biomedical Research With a Focus on Reproductive Health

This study evaluates large language models' ability to generate predictive modeling code for reproductive health using standardized datasets, finding R code generation more successful than Python and some models matching top DREAM challenge teams.

Knowledge Connector: Decision support system for multiomics-based precision oncology

The paper introduces Knowledge Connector, a decision support system that integrates multiomics data with clinical information to aid in precision oncology decision-making.

CARDBiomedBench: A Benchmark for Evaluating Large Language Model Performance in Biomedical Research

CARDBiomedBench evaluates LLM performance in biomedical research using a benchmark focused on neurodegenerative diseases, revealing significant limitations in response quality and safety.

CIViC MCP: Integrating Large Language Models with the Clinical Interpretations of Variants in Cancer

The paper introduces CIViC MCP, a system that uses large language models to interface with the CIViC knowledgebase, enabling natural language queries for cancer variant interpretations.

Benchmarking large language models for genomic knowledge with GeneTuring

This paper benchmarks large language models for genomic knowledge using GeneTuring, finding that a custom GPT-4o setup with NCBI APIs performs best.

EYE-Llama, an in-domain large language model for ophthalmology

This paper introduces EYE-Llama, an ophthalmology-specific large language model trained on in-domain data, which outperforms baseline models in medical question-answering tasks.

AI-HOPE: An AI-Driven conversational agent for enhanced clinical and genomic data integration in precision medicine research

AI-HOPE is a conversational AI platform that integrates clinical and genomic data using natural language, enabling analysis without programming expertise and producing statistical insights.

Artificial Intelligence's Contribution to Biomedical Literature Search: Revolutionizing or Complicating?

This study evaluates ChatGPT's utility for biomedical literature search compared to traditional methods, finding limitations in accuracy and consistency but potential with user-support tools.

Assessing Large Language Model Performance Related to Aging in Genetic Conditions

This study evaluates how large language models handle age-related aspects of 282 genetic conditions by generating medical vignettes and dialogues, finding no age-based biases but some performance differences.

Large language models are universal biomedical simulators

This paper introduces SimulateGPT, a text-based simulator using large language models to predict biomedical outcomes without domain knowledge or manual tuning.

Using GPT-4 to write a scientific review article: a pilot evaluation study

This study evaluates GPT-4's ability to generate text, tables, and diagrams for biomedical review papers and suggests improvements for scientific use.

Empowering Personalized Pharmacogenomics with Generative AI Solutions

This study evaluates an AI assistant using GPT-4 and RAG to interpret pharmacogenomic results, showing improved performance over ChatGPT 3.5 in specialized queries.

Deep learning based phenotyping of medical images improves power for gene discovery of complex disease

This paper uses deep learning to phenotype knee osteoarthritis from medical images, improving GWAS power and polygenic risk score prediction compared to traditional methods.

Using artificial intelligence on dermatology conditions in Uganda: A case for diversity in training data sets for machine learning.

This paper evaluates an AI dermatology algorithm on Ugandan skin images, finding low diagnostic accuracy and emphasizing the need for diverse training data.

Analysis of large-language model versus human performance for genetics questions

This study compares the performance of a large-language model (ChatGPT) and humans on genetics questions, finding no significant difference overall but noting better memorization performance and inconsistent answers from the model.

Turing Test Inspired Method for Analysis of Biases Prevalent in Artificial Intelligence-Based Medical Imaging

The paper introduces a modified Turing Test to evaluate AI-based medical imaging systems for diagnostic accuracy and bias detection.

An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot

Symptoma, a digital health assistant, accurately identifies COVID-19 cases with high precision using symptom input and outperforms simple questionnaires.