Dat Duong

This study evaluates if explainable AI (XAI) improves diagnostic accuracy and confidence in genetic syndrome recognition using facial images, finding mixed results with AI predictions having greater impact than XAI explanations.

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.

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.

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.

This study uses deep learning to assess spinal severity in alkaptonuria, predict treatment status, and compare performance with clinicians.

This study compares human and computer attention patterns when assessing images of genetic conditions using eye-tracking and deep learning saliency maps.

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.

This paper develops a neural network classifier to diagnose genetic syndromes from facial images across different age groups, showing improved accuracy over clinical geneticists.

The paper trains neural networks to classify, generate, and analyze images of rare genetic conditions, showing improved accuracy over experts and demonstrating style-mixing and morphing techniques.

The paper introduces a Transformer-based model for annotating Gene Ontology terms in protein sequences, showing improved accuracy over CNN-based methods and incorporating additional features like BLAST motifs and structural data.

This paper evaluates neural network models for embedding Gene Ontology terms and introduces new models based on GCN, ELMo, and BERT, finding that BERT-based embeddings perform best for rare labels.

The paper assesses the installability and archival stability of computational biology tools, finding many are inaccessible or hard to install, and recommends policy changes to improve these aspects.

The paper introduces two NLP-based methods to measure semantic similarity of Gene Ontology terms using their definitions, achieving better classification accuracy than tree-based methods.

The paper introduces a meta-analysis method to identify eQTLs from multi-tissue datasets, detecting more eQTLs and eGenes than previous approaches.