GestaltGAN: synthetic photorealistic portraits of individuals with rare genetic disorders.

The facial gestalt (overall facial morphology) is a characteristic clinical feature in many genetic disorders that is often essential for suspecting and establishing a specific diagnosis. Therefore, publishing images of individuals affected by pathogenic variants in disease-associated genes has been an important part of scientific communication. Furthermore, medical imaging data is also crucial for teaching and training deep-learning models such as GestaltMatcher.

Artificial intelligence in clinical genetics.

Artificial intelligence (AI) has been growing more powerful and accessible, and will increasingly impact many areas, including virtually all aspects of medicine and biomedical research. This review focuses on previous, current, and especially emerging applications of AI in clinical genetics. Topics covered include a brief explanation of different general categories of AI, including machine learning, deep learning, and generative AI.

Evaluating large language models on medical, lay-language, and self-reported descriptions of genetic conditions.

Large language models (LLMs) are generating interest in medical settings. For example, LLMs can respond coherently to medical queries by providing plausible differential diagnoses based on clinical notes. However, there are many questions to explore, such as evaluating differences between open- and closed-source LLMs as well as LLM performance on queries from both medical and non-medical users.

Approximating facial expression effects on diagnostic accuracy via generative AI in medical genetics.

Artificial intelligence (AI) is increasingly used in genomics research and practice, and generative AI has garnered significant recent attention. In clinical applications of generative AI, aspects of the underlying datasets can impact results, and confounders should be studied and mitigated. One example involves the facial expressions of people with genetic conditions.

The spillover effect of mandatory renewable portfolio standards.

Renewable Portfolio Standards (RPSs) are one of the most prevalent and impactful clean energy policies implemented by states in the United States. This paper investigates the regional spillover effect of RPS policies using a directed dyad panel dataset of renewable electricity generation in US states from 1991 to 2021. Regional spillover effect is measured in two ways: by considering the influence of an RPS enacted in neighboring states and in states in the same regional transmission organization or independent system operator region.

Recognition of Genetic Conditions After Learning With Images Created Using Generative Artificial Intelligence.

Importance: The lack of standardized genetics training in pediatrics residencies, along with a shortage of medical geneticists, necessitates innovative educational approaches.

Objective: To compare pediatric resident recognition of Kabuki syndrome (KS) and Noonan syndrome (NS) after 1 of 4 educational interventions, including generative artificial intelligence (AI) methods.

Design, Setting, And Participants: This comparative effectiveness study used generative AI to create images of children with KS and NS.

Generative Methods for Pediatric Genetics Education.

Artificial intelligence (AI) is used in an increasing number of areas, with recent interest in generative AI, such as using ChatGPT to generate programming code or DALL-E to make illustrations. We describe the use of generative AI in medical education. Specifically, we sought to determine whether generative AI could help train pediatric residents to better recognize genetic conditions.

Artificial intelligence and the impact on medical genetics.

Virtually all areas of biomedicine will be increasingly affected by applications of artificial intelligence (AI). We discuss how AI may affect fields of medical genetics, including both clinicians and laboratorians. In addition to reviewing the anticipated impact, we provide recommendations for ways in which these groups may want to evolve in light of the influence of AI.

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

Large-language models like ChatGPT have recently received a great deal of attention. One area of interest pertains to how these models could be used in biomedical contexts, including related to human genetics. To assess one facet of this, we compared the performance of ChatGPT versus human respondents (13,642 human responses) in answering 85 multiple-choice questions about aspects of human genetics.

The future of commercial genetic testing.

Purpose Of Review: There are thousands of different clinical genetic tests currently available. Genetic testing and its applications continue to change rapidly for multiple reasons. These reasons include technological advances, accruing evidence about the impact and effects of testing, and many complex financial and regulatory factors.

Prediction of HLA genotypes from single-cell transcriptome data.

The human leukocyte antigen (HLA) locus plays a central role in adaptive immune function and has significant clinical implications for tissue transplant compatibility and allelic disease associations. Studies using bulk-cell RNA sequencing have demonstrated that HLA transcription may be regulated in an allele-specific manner and single-cell RNA sequencing (scRNA-seq) has the potential to better characterize these expression patterns. However, quantification of allele-specific expression (ASE) for HLA loci requires sample-specific reference genotyping due to extensive polymorphism.

Age-related survey of clinical genetics literature and related resources.

Genetic conditions affect people throughout their entire lifespan; however, many clinical geneticists focus on the care of pediatric individuals. We analyzed the medical literature and related resources to help assess to what extent adults with genetic diseases were represented. This included general literature searches of PubMed (from 2001 through 2022), specific databases (the FDA orphan drug list and the Clinical Genomic Database) related to management and direct treatment of genetic conditions, and textbooks and morphology guides relevant to the diagnosis of genetic conditions.

Whither social media and clinical genetics?

Social media has become ubiquitous in daily life, and increasingly impacts medical and scientific fields, including related to clinical genetics. Recent events have led to questions about the use of certain social media platforms, as well as social media more generally. We discuss these considerations, including alternative and emerging platforms that can offer forums for the clinical genetics and related communities.

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

Large-language models like ChatGPT have recently received a great deal of attention. To assess ChatGPT in the field of genetics, we compared its performance to human respondents in answering genetics questions (involving 13,636 responses) that had been posted on social media platforms starting in 2021. Overall, ChatGPT did not perform significantly differently than human respondents, but did significantly better on memorization-type questions versus critical thinking questions, frequently provided different answers when asked questions multiple times, and provided plausible explanations for both correct and incorrect answers.

Perspectives on the future of dysmorphology.

The field of clinical genetics and genomics continues to evolve. In the past few decades, milestones like the initial sequencing of the human genome, dramatic changes in sequencing technologies, and the introduction of artificial intelligence, have upended the field and offered fascinating new insights. Though difficult to predict the precise paths the field will follow, rapid change may continue to be inevitable.

Investigating Determinants and Evaluating Deep Learning Training Approaches for Visual Acuity in Foveal Hypoplasia.

Purpose: To describe the relationships between foveal structure and visual function in a cohort of individuals with foveal hypoplasia (FH) and to estimate FH grade and visual acuity using a deep learning classifier.

Design: Retrospective cohort study and experimental study.

Participants: A total of 201 patients with FH were evaluated at the National Eye Institute from 2004 to 2018.