A patient arrives with a familiar story: years of seemingly unrelated symptoms, a family history that hints at something inherited, and a genome that contains more possibilities than explanations. For decades, genetic counselors have served as interpreters between the complexity of DNA and the lived experiences of patients. Now, a new collaborator has entered the room.
Artificial intelligence promises to uncover hidden patterns, accelerate variant interpretation, revisit unsolved cases, translate information across languages and offer new forms of support — but it also asks us to reconsider how we define expertise, judgment, and the human role in medicine.
In this issue, we dive into the rapidly evolving intersection of AI and genetics, where breakthroughs are reshaping genomic discovery. We examine how AI is advancing rare disease diagnosis and genomic reanalysis, how new reasoning models may transform clinical decision-making and how AI-powered tools might extend support to patients navigating uncertainty. We also confront the more difficult questions: Can AI-generated exam questions truly measure competence? Can these systems perform equitably across languages and populations? As we increasingly turn to machines for assistance, do we risk outsourcing the very critical thinking skills that allow us to challenge assumptions, recognize nuance and advocate for our patients?
The promise of AI in genetic counseling is not simply a question of what technology can accomplish, but of how thoughtfully we choose to use it. The articles in this issue explore that delicate balance — between innovation and oversight, efficiency and reflection, automation and the distinctly human expertise at the heart of genetic counseling
indicates open access
Guest Editor: Daria Ma, MS LCGC MSHS
Contributing Editors: Katya Orlova, MPH, CGC, PhD, KT Curry, MS, CGC, Marlena Ahn, MGCS, CGC, Lara Sucheston-Campbell, PhD, MS, Amy Lemke, PhD, MS, Ping Gong, MS, CG
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NEWS & OPINION
AI IN VARIANT ANALYSIS: FAST TRACK TO GENETIC DIAGNOSES
Wilk EJ et al. 202
As genomic sequencing becomes more widely available, interpreting complex genetic variants remains a key challenge, particularly for rare disease diagnosis. This article highlights the potential of artificial intelligence (AI) to improve variant interpretation by supporting phenotyping, prioritization and clinical decision-making, helping reduce diagnostic delays. It also discusses the limitations and implementation challenges of AI — including bias, privacy and explainability — and emphasizes the importance of human oversight to ensure AI is used safely and effectively in genomic medicine.
Tags: Genetic Testing, Overview, ELSI
LARGE REASONING MODELS AS THINKING MACHINES FOR MEDICINE
Zhou et al. 2026
This perspective introduces medical reasoning artificial intelligence (MRAI), a new generation of AI designed to move beyond pattern recognition toward reasoning through complex clinical problems in partnership with healthcare professionals. The article also explores the technologies needed to build these systems, strategies for evaluating their performance and safety, and the importance of maintaining human oversight to ensure trustworthy, ethical implementation.
Tags: Medicine, Overview
WHEN GENOMIC REANALYSIS LEAVES THE LABORATORY — CLINICAL GENETICS IN THE AGE OF CONSUMER AI
Finlayson and Rehm 2026
This commentary discusses how consumer AI is reshaping genomic reanalysis for patients with rare diseases, highlighting evidence that a general-purpose AI platform identified new diagnoses in approximately 4.8% of previously unsolved cases with minimal expert input. The authors argue that patient-led AI-assisted genomic reanalysis is likely to become increasingly common and propose frameworks for privacy protection, data sharing and accredited clinical interpretation to ensure AI-generated findings can be safely and effectively incorporated into routine genetic care.
Tags: Genetic testing, ELSI
AI IN GENOMIC MEDICINE IS ADVANCING — BUT INSTITUTIONS NEED GOVERNANCE, NOT HYPE
Forbes 2026
In this commentary, Dr. Habib Al Souleiman argues that the main challenge on AI-driven genomic medicine is not technical capability, but institutional readiness, particularly around data quality, explainability and cross disciplinary coordination. Without strong governance framework, AI in genomics may fail to translate into reliable clinical value.
Tags: Public health, ELSI
IS AI DULLING OUR MINDS?
The Harvard Gazette
We asked ChatGPT, “Can AI make us dumber or smarter?” Its answer: “It depends on how we use it — as a crutch or as a tool for growth.” So, what do the experts think? Read the article to to explore perspectives from faculty across multiple disciplines on how AI may strengthen — or weaken — our critical thinking. Which viewpoint do you agree with? Read the article and decide for yourself.
Tags: Chatbot, Education
RESEARCH
EFFICACY OF RELATIONAL AGENTS FOR LONELINESS ACROSS AGE GROUPS: A SYSTEMATIC REVIEW AND META-ANALYSIS
This systematic review found that AI-powered relational agents can moderately reduce loneliness, suggesting they may offer scalable psychosocial support. For genetic counselors, these tools could complement patient care by providing support between visits or for individuals with rare genetic conditions, while reinforcing that AI cannot replace the empathy, clinical judgment and therapeutic relationship central to genetic counseling.
Tags: Chatbot
DISPARATE LANGUAGE AND MODEL EFFECTS ON AI-BASED TRANSLATION AND RECOGNITION OF GENETIC CONDITIONS
This study evaluated how AI-based translation affects the ability of large language models to recognize genetic conditions by translating descriptions of 40 disorders into dozens of languages using both neural machine translation and LLM-based translation. The authors found that diagnostic accuracy varied substantially depending on the translation method, language and AI model, with language characteristics such as script and training prevalence influencing performance. These findings highlight the need to rigorously evaluate multilingual medical AI systems to ensure reliable and equitable performance across diverse languages and populations.
Tags: Overview
NAVIGATING OPPORTUNITIES AND CHALLENGES OF GENERATIVE AI IN HIGHER EDUCATION: TOWARDS RESPONSIBLE, EQUITABLE AND HUMAN-CENTERED INTEGRATION
Coman et al. 2026
This is a systematic review of recent literature on generative AI in higher education and argues that AI offers significant opportunities for improving teaching and learning while also introducing important risks that require careful governance. It highlights benefits such as personalized learning, adaptive feedback and support for self-regulated learning, alongside challenges including academic integrity concerns, data privacy risks, equity issues and uneven institutional readiness.
The authors conclude that successful integration of generative AI requires a human-centered, ethical and equity-focused approach, emphasizing AI literacy, stakeholder collaboration and strong institutional policies to ensure responsible use in universities.
Tags: Education, ELSI
CAN LARGE LANGUAGE MODELS GENERATE EXAM QUESTIONS COMPARABLE TO HUMANS? A SYSTEMATIC REVIEW AND META-ANALYSIS STUDY IN MEDICAL EDUCATION
Alani et al. 2026
Can AI write exam questions as effectively as experienced educators? This first systematic review compares AI-generated and human-written multiple-choice questions, revealing comparable psychometric performance while highlighting why expert oversight, fairness and quality assurance remain essential for high-stakes assessments.
Tags: Education
ARTIFICIAL INTELLIGENCE IN GENOMICS: A COMPREHENSIVE SURVEY OF METHODS, RESOURCES, CHALLENGES AND PROSPECTS
Mahmud and Banerjee 2026
This review examines how artificial intelligence (AI) technologies — including machine learning, deep learning, natural language processing, large language models and explainable AI — are advancing genomics. It highlights applications in gene sequencing, variant detection, gene expression analysis, personalized medicine and genome editing, while discussing challenges related to data quality, interpretability, ethics and scalability.
Tags: Overview
REGULATION, POLICY, GOVERNANCE
ETHICAL CONCERNS AND STRATEGIES FOR IMPLEMENTING ARTIFICIAL INTELLIGENCE IN HEALTHCARE: A REVIEW OF EMPIRICAL STUDIES
Wubineh et al. 2026
The article synthesizes empirical research on AI deployment in healthcare and identifies major ethical challenges, including issues of transparency, bias and fairness, privacy and data security, accountability and regulatory oversight. It also reviews strategies to address these concerns, emphasizing the need for strong governance frameworks, ethical guidelines and stakeholder collaboration to ensure responsible AI implementation.
Authors conclude that while AI has strong potential to improve healthcare outcomes, its safe adoption depends on addressing ethical risks that affect trust, equity and clinical responsibility.
Tags: ELSI, Medicine
STATES CONTINUE EFFORTS TO REGULAR AI IN HEALTHCARE: A REVIEW OF LEGISLATION PASSED IN 2026
Silverboard et al 2026
In 2026, states have taken the lead in regulating the use of AI in healthcare by introducing laws that increase transparency, require human oversight for important clinical and insurance decisions and strengthen protections for patients. New regulations also address the use of AI in clinical care, chatbots and therapeutic settings by requiring informed consent, safety safeguards and appropriate oversight, while regulatory "sandbox" programs allow emerging AI technologies to be evaluated under supervised conditions before broader implementation. Read the full article to learn how your state is approaching AI regulation and what these new policies could mean for you and your work place.
Tags: ELSI, Chatbot, Insurance
EVENTS
Focused on emerging techniques, trends and applications of AI and machine learning in precision medicine. Each day the program will include one 30-minute introductory keynote presentation, followed by two 2-hour sessions of talks and a moderated discussion.
- Sep 24 and 25 9:30 a.m. – 3 p.m. ET
- Eligible for six continuing education credits
- Regular member: $95; Non-member $120
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NSGC AI/ML Subcommittee
The AI/ML Subcommittee is part of the National Society of Genetic Counselors’ (NSGC) Genomic Technologies Special Interest Group (SIG). We are dedicated to exploring and advancing the integration of AI and ML technologies to enhance the field of genetics and support the evolving role of genetic counselors.
NSGC AI/ML Subcommittee The AI/ML Subcommittee is part of the National Society of Genetic Counselors’ (NSGC) Genomic Technologies Special Interest Group (SIG). We are dedicated to exploring and advancing the integration of AI and ML technologies to enhance the field of genetics and support the evolving role of genetic counselors.