Benjamin D. Solomon

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.

Bone2Gene uses deep learning to classify rare bone diseases from hand radiographs, achieving high accuracy for distinct disorders but lower for overlapping ones.

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 paper introduces GestaltGAN, a generative model that creates photorealistic portraits of individuals with rare genetic disorders while preserving privacy.

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.

This paper reviews host genetic factors associated with coronavirus susceptibility in humans and animals, identifying significant loci and highlighting limitations in current studies.

The paper identifies maternal age-related mutation clusters in germline de novo mutations linked to double-strand break processes.