Benjamin D. Solomon

AI‐driven chatbots have been utilized in healthcare to automate administrative tasks, improve patient education, and expand access to medical information; however, their role in genetic counseling remains underexplored. To investigate the adoption, perceptions, and potential utility of AI‐based chatbots in genetic counseling practice, 217 genetic counselors and genetic counseling students from across North America were surveyed regarding chatbot usage, confidence in their application, and perceived benefits and limitations. While most participants (166/217; 76.5%) reported using general AI chatbots outside of clinical settings, far fewer (18/204; 8.8%) reported using or recommending clinical genetics chatbots in clinical practice. For those that used clinical genetics chatbots, the primary purpose was for communication with at‐risk family members (11/18; 61.1%) and patient education (10/18; 55.6%). Confidence in chatbot technology varied, with highest confidence in gathering family history information (81/199; 40.7%) and lowest confidence in their ability to disclose variants of uncertain significance or positive genetic testing results (5/199; 2.5%). The greatest perceived benefits included reducing repetitive tasks (165/195, 84.6%) and allowing for time for other tasks (141/195; 72.3%), while major concerns revolved around patient comprehension (167/195; 85.6%) and having accurate, up‐to‐date information (145/195; 74.4%). Despite some concern about AI replacing human counselors, most participants reported they felt there was potential for chatbots to enhance workflow efficiency (128/195; 65.6%) if properly integrated and regulated. Limited AI training was identified as a barrier to adoption (16/195; 8.2% received training), highlighting a need for structured education on AI applications in genetic counseling. These findings suggest that AI chatbots hold promise as supplementary tools, but significant challenges must be addressed before widespread implementation in genetic counseling practice.

Purpose of review: Artificial intelligence (AI) is being rapidly but unevenly integrated into many aspects of medicine as well as society more broadly. This review focuses on recent developments in AI through the lens of how it is affecting the field of pediatric genomics. Recent findings: This article describes and explains general definitions of AI and its subtypes, including related to the latest AI applications. The article then outlines ways in which AI is being used and tested in multiple aspects of pediatric genomics, including: identifying individuals who may have genetic conditions and assisting in the diagnostic process; AI implementation in the laboratory-based genetic testing process; how recent AI developments may support patient management as well as discovery and research into novel ways to treat genetic conditions. Overall, AI is being applied to pediatric genomics in many different ways. Summary: The field of pediatrics will undergo dramatic change because of AI. This piece traces different ways in which AI may affect one particular field, pediatric genomics. Due to rapid advances, it is likely that future AI discoveries will lead to additional ways in which AI affects this field.

Introduction: Artificial intelligence (AI) is increasingly prevalent. Patients and clinicians may use AI-based tools in many different languages. Objective: To investigate AI translation tools for descriptions of genetic conditions and how AI identification of genetic conditions is affected by translations. Materials and Methods: We used Neural machine translation (NMT) and large language-model (LLM) translation to translate descriptions of 40 genetic conditions into 191 and 93 languages, respectively. Excluding translations retaining English medical terms verbatim, we respectively focused on 139 and 70 languages. After assessing translations, we assessed the ability of 3 proprietary and 3 open-weight general LLMs to identify conditions in the translations. We analyzed how accuracy was affected by the conditions’ prevalence in the literature, and attributes of the languages (the script, language family, and prevalence of the language in training sources). We also investigated adaptive translation for select languages. Results: We found significant differences in condition identification based on the translation method, condition, language, and prediction model. The accuracy of some models was more affected than others by factors like the conditions’ literature prevalence, language script, family, and language prevalence. Adaptive translation for select languages did not improve translations or diagnostic accuracy with the 3 tested LLMs. However, further analysis with 1 language showed that this approach was more effective with smaller LLMs. Conclusions: AI-based translation has variable performance, which can affect the ability of AI models to recognize genetic conditions. These findings should inform safe medical AI use to support consistent performance in different languages.

Artificial intelligence (AI) is rapidly transforming biomedicine, including genomic medicine. This comment explores three myths and controversies involving AI in genomic medicine, including: how clinicians will use AI; whether AI’s impressive on-paper performance will translate to the real world; how patient preferences may or may not impact AI’s transformation of the field. Based on this discussion, the comment concludes with three recommendations for an AI-based future of genomic medicine.

Background: Diagnosing the over 700 known rare bone diseases (RBDs) is inherently challenging and often requires extensive time and multiple clinical visits. Effective treatment, particularly for RBDs with approved therapies, depends on early and precise identification of the specific RBD type. Image recognition artificial intelligence (AI) has the potential to significantly enhance diagnostic processes and improve patient outcomes. Many of these disorders cause characteristic skeletal changes, especially in the hands, and are associated with growth abnormalities. Consequently, affected children routinely undergo hand radiographs for bone age assessment, making these images a widely available yet underutilized diagnostic resource. Materials and Methods: We retrospectively compiled 5,623 multi-institutional hand radiographs from 2,471 patients with 45 different RBDs and 1,382 unaffected controls. We trained two deep learning models: a binary classifier to differentiate between RBD and non-RBD hand radiographs, and a multi-class classifier covering ten RBDs (or RBD groups), using 5-fold cross-validation. Preprocessing included masking, normalization, and data augmentation. Additionally, we applied occlusion sensitivity mapping to visualize class-specific features and evaluated the learned representations through cosine-based retrieval and UMAP projections of the feature space. Results: The affected versus unaffected classifier achieved a balanced accuracy of 85.5% on the test dataset. The ten-class classifier reached a balanced (top-1) accuracy of 76.6%, with top-3 accuracy exceeding 90%. Disorders with highly distinctive phenotypes, such as achondroplasia, achieved accuracies above 95%, whereas phenotypically overlapping disorders, such as ACAN- and SHOX-related short stature, were more frequently confused. Feature space analysis showed that validation samples clustered closely with their respective training distributions, supporting the consistency and generalizability of the learned embeddings. Conclusion: This manuscript presents a proof of principle for the development of Bone2Gene , a next-generation phenotyping (NGP) tool for the detection and differential diagnosis of RBDs, currently based on hand radiographs. Ongoing efforts focus on expanding the dataset to include additional RBDs or RBD groups in the current multi-class classifier for differential diagnosis and to further evaluate its generalizability. The Bone2Gene study is open to collaboration.

Purpose: Stargardt disease, also called ABCA4-related retinopathy (ABCA4R), is the most common form of juvenile-onset macular dystrophy and yet lacks an FDA approved treatment. Substantial progress has been made through landmark studies like that of the Progression of Atrophy Secondary to Stargardt Disease (ProgStar), but tasks like image segmentation and phenotyping still pose major challenges in terms of monitoring disease progression and categorizing patient subgroups. Furthermore, these methods are subjective and laborious. Recent advancements in machine learning (ML) and deep learning show considerable promise in automating these processes. Methods: This scoping review explores ML applications in ABCA4R, with a focus on segmentation and phenotyping. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology, 15 articles were selected from 264, with 12 focused on the task of segmenting atrophic lesions, retinal flecks, retinal layer boundaries, or en-face imaging. Three studies addressed phenotyping based on electroretinography (ERG), visual acuity, and microperimetry. Results: Several effective approaches were implemented in these studies, including ensemble modeling, self-attention mechanisms, soft-label approaches, and dynamic frameworks that consider extent of tissue damage. Excellent model performance includes segmentation DICE performances of 0.99 and ERG phenotyping accuracies 90% and greater. Smaller datasets and variable presentations present as significant challenges, while advanced methods like Monte Carlo dropout and active learning improve pipeline efficiency and performance. Conclusion: ML techniques are well on their way to automate key steps in ABCA4R evaluation with excellent performance. These emerging methods have the potential to expedite therapeutic innovation and enhance our understanding of ABCA4R.

Objective: To assess whether saliency-based explainable artificial intelligence (XAI) can improve diagnostic accuracy, confidence, and perceived usefulness for recognition of genetic conditions based on facial images. Materials and methods: Forty-four medical geneticists, divided into AI-only (n=23) and XAI-supported (n=21) groups, assessed eighteen images of individuals with and without genetic conditions. Geneticists’ diagnostic accuracy and confidence were recorded before and after viewing an AI classifier’s prediction probability, with or without XAI explanations. Mediation analyses were conducted to interpret geneticist behavior. Results: AI-only and XAI-support improved geneticist accuracy for correct AI classifications, with mean improvements of 0.20±0.13 (AI-only) and 0.19±0.13 (XAI). Incorrect AI classifications decreased accuracy in both groups: −0.20±0.22 (AI-only) and −0.21±0.23 (XAI). Average confidence increased with correct classification: 0.30±0.26 (AI-only) and 0.38±0.71 (XAI) and decreased with incorrect classification: −0.14±0.64 (AI-only) and - 0.14±0.76 (XAI). AI prediction probability were generally considered useful (0.58±1.1 (AI-only) and 0.72±1.3 (XAI)); XAI explanations were viewed less favorably: −0.14±1.3 (saliency maps); −0.19±1.3 (region relevance scores). For incorrect AI classifications, there was a negative correlation between accuracy improvement and perceived AI usefulness (Spearman’s Rho −0.31 (AI-only) and −0.40 (XAI)). Mediation analyses demonstrated that when AI is correct (without XAI), there was a significant mediated effect (0.583, 95% CI [0.044, 1.144]) by the model probability between user confidence and decision to follow AI. Conclusions: The lack of accuracy or confidence improvements together with additional qualitative responses indicate that participants did not integrate saliency-based XAI into their decisions. AI prediction probability had a greater impact on participants’ decision making.

Artificial intelligence (AI) is rapidly transforming numerous aspects of daily life, including clinical practice and biomedical research. In light of this rapid transformation, and in the context of medical genetics, we assembled a group of leaders in the field to respond to the question about how AI is affecting, and especially how AI will affect, medical genetics. The authors who contributed to this collection of essays intentionally represent different areas of expertise, career stages, and geographies, and include diverse types of clinicians, computer scientists, and researchers. The individual pieces cover a wide range of areas related to medical genetics; we expect that these pieces may provide helpful windows into the ways in which AI is being actively studied, used, and considered in medical genetics.