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Draft:Conversational genomics

From Wikipedia, the free encyclopedia
  • Comment: Declining a second time since it didn't go to the submitter ChrysGalley (talk) 20:43, 20 July 2026 (UTC)
  • Comment: This gets to be a circular logic at some point, but still, it's rather obviously AI generated. ChrysGalley (talk) 20:40, 20 July 2026 (UTC)

Conversational genomics is a descriptive label for the use of natural-language conversational artificial intelligence — including chatbots and large language model (LLM) based agents — to query, interpret, and communicate genomic and clinical genetics information. Rather than requiring users to write code or navigate specialised software, such systems let clinicians, researchers, and patients pose questions in ordinary language and receive summaries, risk assessments, or the results of underlying analyses.

The exact phrase "conversational genomics" is not yet an established term in the peer-reviewed literature; it appears prominently in an industry blog post,[1] while closely related work is published under other names such as "conversational agent",[2] "conversational voice interface",[3] and "generative AI in genetic counseling".[4] The best-documented applications fall into two broad areas: genetic counseling and patient communication, where conversational agents have been studied in randomized controlled trials; and variant and sequence interpretation, where generative-AI assistants summarise evidence or wrap machine-learning models of the genome.

Background

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Interest in conversational interfaces for genomics has been driven partly by a shortage of genetic counselors relative to demand, and by the volume and complexity of genomic data that clinicians and patients must interpret.[5][4] Two technical developments underpin the field.

The first is the maturation of general-purpose large language models capable of holding a dialogue and answering domain questions. Independent evaluations have tested such models on genetics tasks: a comparison of ChatGPT 3.5 and ChatGPT 4 on 68 patient-facing genetics questions found GPT-4 more accurate (mean 4.17 versus 3.38 out of 5) but still prone to outdated information and errors in inheritance calculations.[5] Another benchmark evaluated multiple models on identifying 63 genetic conditions from clinician versus layperson descriptions, finding that closed-source models performed best (GPT-4 around 89–90%) but that accuracy dropped on real-world patient descriptions.[6]

The second is a distinct line of work on sequence-to-function models — deep neural networks that predict molecular activity directly from DNA sequence. Examples include Enformer, which predicts gene expression and regulatory signals from roughly 200 kilobases of sequence context and scores non-coding variant effects;[7] Borzoi, which predicts cell-type-specific RNA-seq coverage;[8] and AlphaGenome, a unified model operating at single-base resolution over a 1-megabase context.[9] These models are not themselves conversational, but conversational-genomics systems increasingly use them as back-end tools.

Reviews distinguish these sequence-based "genome language models", which treat nucleotides as tokens, from natural-language chat and agent systems.[10][11] A related development is the emergence of "agentic" AI systems that combine an LLM with planning, memory, and the ability to invoke external tools. Surveys catalogue dozens of such systems across genomics and biomedicine and enumerate recurring challenges, including unstable reasoning, limited biological grounding, reproducibility, and safety.[12][13] Proposals such as adapting the Model Context Protocol to bioinformatics web services aim to let LLM agents query databases such as GEO, STRING, and the UCSC Cell Browser autonomously.[14]

An early peer-reviewed example of conversational access to genomic data is Melvin, a voice interface built as an Amazon Alexa skill that answers spoken questions about cancer genomics data from The Cancer Genome Atlas.[3] More recent research prototypes couple an LLM with variant-annotation resources; a Google Cloud engineering blog described a multi-agent prototype using Gemini together with the Variant Effect Predictor and gnomAD data to answer natural-language variant-interpretation questions.[1]

Applications

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Genetic counseling and patient communication

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The most developed and rigorously studied application of conversational genomics is in genetic counseling, particularly for hereditary breast and ovarian cancer (HBOC) and other hereditary-cancer syndromes. A 2023 systematic review and meta-analysis in JCO Clinical Cancer Informatics identified seven studies evaluating five distinct chatbots for genetic cancer risk assessment and counseling, reporting high user acceptability and engagement, a pooled completion rate for risk assessment of about 37%, and no association between sociodemographic factors and interaction patterns; it also noted that comparative-effectiveness evidence was limited.[4]

The largest randomized evidence comes from the BRIDGE trial, an equivalence-design randomized controlled trial of 3,073 patients at two US health systems. It found that a chatbot delivering pretest cancer-genetics education achieved completion of pretest genetic services and genetic testing equivalent to appointments with certified genetic counselors (difference 2.0 percentage points, 95% confidence interval −1.1 to 5.0).[15] Earlier feasibility work described real patient interactions with the same automated conversational agent delivering pretest education.[16] A smaller RCT of 37 women newly diagnosed with breast cancer found patient satisfaction and comprehension with chatbot pretest counseling comparable to in-person counseling.[17]

Conversational agents have also been used for family-history collection and risk triage before clinical visits. A real-world deployment of the Gia chatbot recorded 61,070 users completing hereditary-cancer risk assessments before routine women's-health visits, with 89.4% completing the assessment and about 27% meeting testing criteria; the study's authors were affiliated with a commercial genetics-testing company.[18] A separate clinical study of an AI chatbot performing preliminary HBOC screening in a small sample (n=11) reported that its determinations of whether users met NCCN BRCA1/2 testing criteria were consistent with certified genetic counselors' assessments.[19] A qualitative implementation study examined barriers and facilitators to adopting such a screening chatbot in women's-health clinics.[20]

Surveys of practising genetic counselors indicate that chatbot use to date has centred on communication with at-risk family members and patient education.[4] Reviews and perspectives caution that most evidence remains at the feasibility and acceptability stage, with only one large equivalence RCT reported so far.[4][15]

Variant and sequence interpretation

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A second application area is assisting the interpretation and annotation of genomic variants. VarChat is a generative-AI assistant that takes a variant identifier (in HGVS nomenclature or as a dbSNP identifier) and returns a human-readable summary of the relevant literature with linked references, using retrieval-augmented generation and integrating ClinVar data.[21] Other tools address related tasks: AI-CURA is an LLM workflow reported to match clinical experts on ACMG/AMP variant classification for a set of 150 variants,[22] and AutoPM3 extracts a specific ACMG evidence code from the literature.[23] Evaluations of general-purpose models report a mixed picture: one study found ChatGPT accurate on general genetic-testing counseling questions but less accurate on syndrome-specific ones (88.2% correct or comprehensive for HBOC versus 66.6% for Lynch syndrome).[24]

Systems that couple a conversational interface directly to predictive models of the genome have also been described. ChatNT is a multimodal conversational agent that formulates supervised genomics tasks across DNA, RNA, and protein as text-to-text problems, combining a DNA sequence encoder with an LLM decoder so that users can query biological sequences in English.[2] Chorus is an open-source system that exposes multiple sequence-to-function models — including Enformer, Borzoi, and AlphaGenome — through a natural-language and Model Context Protocol interface for variant-effect prediction.[note 1] Independent surveys frame such tools within a broader landscape of generative-AI applications in medical genomics spanning variant identification, annotation, interpretation, and report generation.[25][26]

Limitations

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Independent reviews and evaluations identify several recurring limitations. General-purpose conversational models can produce outdated or incorrect answers, with accuracy declining on syndrome-specific detail and on descriptions phrased by laypeople rather than clinicians.[5][6][24] Agentic systems face challenges of unstable reasoning, hallucination, limited biological grounding, reproducibility, and biosafety.[12][13] In the clinical-genetics setting, the evidence base is dominated by feasibility and acceptability studies, and some large real-world deployments were reported by teams with commercial interests in genetic testing.[4][18] Implementation studies note practical barriers to adoption in routine care.[20]

See also

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Notes

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  1. ^ Chorus is cited here as one example among conversational-genomics interpretation tools; as of 2026 it lacked an independent peer-reviewed reference and is described only in passing. The Model Context Protocol approach to bioinformatics tooling that it employs is discussed in Flotho et al. (2026).

References

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  1. ^ a b Adedeji, Ayo (2025). "Building Conversational Genomics". Google Cloud Blog.
  2. ^ a b Richard, Guillaume; de Almeida, Bernardo P.; et al. (2025). "A multimodal conversational agent for DNA, RNA and protein tasks". Nature Machine Intelligence. 7 (6): 928–941. doi:10.1038/s42256-025-01047-1.
  3. ^ a b Perera, Akila R.; Warrier, Vinay; Sundararaman, Shwetha; Hsiao, Yi; Ghosh, Soumita; Kularatnarajah, Linganesan; Pitt, Jason J. (2024). "Melvin is a conversational voice interface for cancer genomics data". Communications Biology. 7 (1) 30. doi:10.1038/s42003-023-05688-z. PMC 10770357. PMID 38182884.
  4. ^ a b c d e f Webster, Emily M.; Ahsan, Muhammad Danyal; Perez, Luiza; et al. (2023). "Chatbot Artificial Intelligence for Genetic Cancer Risk Assessment and Counseling: A Systematic Review and Meta-Analysis". JCO Clinical Cancer Informatics. 7 (7) e2300123. doi:10.1200/CCI.23.00123. PMC 10730073. PMID 37934933.
  5. ^ a b c McGrath, Scott P.; Kozel, Beth A.; Gracefo, Sara; Sutherland, Nykole; Danford, Christopher J.; Walton, Nephi (2024). "A comparative evaluation of ChatGPT 3.5 and ChatGPT 4 in responses to selected genetics questions". Journal of the American Medical Informatics Association. 31 (10): 2271–2283. doi:10.1093/jamia/ocae128. PMC 11413464. PMID 38872284.
  6. ^ a b Flaharty, Kendall A.; Hu, Ping; Ledgister Hanchard, Suzanna; Ripper, Molly E.; Duong, Dat; Waikel, Rebekah L.; Solomon, Benjamin D. (2024). "Evaluating large language models on medical, lay-language, and self-reported descriptions of genetic conditions". American Journal of Human Genetics. 111 (9): 1819–1833. doi:10.1016/j.ajhg.2024.07.011. PMC 11393706. PMID 39146935.
  7. ^ Avsec, Žiga; Agarwal, Vikram; Visentin, Daniel; et al. (2021). "Effective gene expression prediction from sequence by integrating long-range interactions". Nature Methods. 18 (10): 1196–1203. Bibcode:2021NaMet..18.1196A. doi:10.1038/s41592-021-01252-x. PMC 8490152. PMID 34608324.
  8. ^ Linder, Johannes; Srivastava, Divyanshi; Yuan, Han; et al. (2025). "Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation". Nature Genetics. 57 (4): 949–961. doi:10.1038/s41588-024-02053-6. PMC 11985352. PMID 39779956.
  9. ^ Avsec, Žiga; et al. (2026). "Advancing regulatory variant effect prediction with AlphaGenome". Nature. 649 (8099): 1206–1218. Bibcode:2026Natur.649.1206A. doi:10.1038/s41586-025-10014-0. PMC 12851941. PMID 41606153.
  10. ^ Balakrishnan, P.; Anny Leema, A.; et al. (2025). "Gene-LLMs: a comprehensive survey of transformer-based genomic language models for regulatory and clinical genomics". Frontiers in Genetics. 16 1634882. doi:10.3389/fgene.2025.1634882. PMC 12558637. PMID 41158510.
  11. ^ Shu, Liyuan; Tang, Jiao; Guan, Xiaoyu; Zhang, Daoqiang (2026). "A comprehensive survey of genome language models in bioinformatics". Briefings in Bioinformatics. 27 (1) bbaf724. doi:10.1093/bib/bbaf724. PMC 12805252. PMID 41537311.
  12. ^ a b Dip, Sajib Acharjee; Mallick, Dipanwita; Shuvo, Uddip Acharjee; et al. (2026). "Large language model agents for biological intelligence across genomics, proteomics, spatial biology, and biomedicine". Briefings in Bioinformatics. 27 (2) bbag110. doi:10.1093/bib/bbag110. PMC 13017847. PMID 41883029.
  13. ^ a b Zhou, Juexiao; Jiang, Jindong; Han, Zhongyi; Wang, Zijian; Gao, Xin (2025). "Streamline automated biomedical discoveries with agentic bioinformatics". Briefings in Bioinformatics. 26 (5) bbaf505. doi:10.1093/bib/bbaf505. PMC 12476841. PMID 41016012.
  14. ^ Flotho, Matthias; Diks, Ian Ferenc; Flotho, Philipp; Molano, Leidy-Alejandra G.; Hirsch, Pascal; Keller, Andreas (2026). "MCPmed: a call for Model Context Protocol-enabled bioinformatics web services for LLM-driven discovery". Briefings in Bioinformatics. 27 (1) bbag076. doi:10.1093/bib/bbag076. PMC 12927880. PMID 41729821.
  15. ^ a b Kaphingst, Kimberly A.; Kohlmann, Wendy K.; Chambers, Rachelle Lorenz; et al. (2024). "Uptake of Cancer Genetic Services for Chatbot vs Standard-of-Care Delivery Models: The BRIDGE Randomized Clinical Trial". JAMA Network Open. 7 (9): e2432143. doi:10.1001/jamanetworkopen.2024.32143. PMC 11385050. PMID 39250153.
  16. ^ Chavez-Yenter, Daniel; Kimball, Kadyn E.; Kohlmann, Wendy; et al. (2021). "Patient Interactions With an Automated Conversational Agent Delivering Pretest Genetics Education: Descriptive Study". Journal of Medical Internet Research. 23 (11) e29447. doi:10.2196/29447. PMC 8663668. PMID 34792472.
  17. ^ Al-Hilli, Zahraa; Noss, Ryan; Dickard, Jennifer; et al. (2023). "A Randomized Trial Comparing the Effectiveness of Pre-test Genetic Counseling Using an Artificial Intelligence Automated Chatbot and Traditional In-person Genetic Counseling in Women Newly Diagnosed with Breast Cancer". Annals of Surgical Oncology. 30 (10): 5990–5996. doi:10.1245/s10434-023-13888-4. PMID 37567976.
  18. ^ a b Nazareth, Sunny; Hayward, Lindsay; Simmons, Emily; et al. (2021). "Hereditary Cancer Risk Using a Genetic Chatbot Before Routine Care Visits". Obstetrics & Gynecology. 138 (6): 860–870. doi:10.1097/AOG.0000000000004596. PMC 8594498. PMID 34735417.
  19. ^ Sato, Ann; Haneda, Eri; Hiroshima, Yukihiko; Narimatsu, Hiroto (2024). "Preliminary Screening for Hereditary Breast and Ovarian Cancer Using an AI Chatbot as a Genetic Counselor: Clinical Study". Journal of Medical Internet Research. 26 e48914. doi:10.2196/48914. PMC 11635313. PMID 39602801.
  20. ^ a b Wollney, Easton N.; Madani Sims, Shireen; Ricks-Santi, Luisel J.; et al. (2025). "Implementing a chatbot to promote hereditary breast & ovarian cancer genetic screening in women's health: identifying barriers and facilitators to screening adoption". BMC Public Health. 25 (1) 2516. doi:10.1186/s12889-025-23488-4. PMC 12276685. PMID 40684177.
  21. ^ De Paoli, Federica; Berardelli, Silvia; Limongelli, Ivan; Rizzo, Ettore; Zucca, Susanna (2024). "VarChat: the generative AI assistant for the interpretation of human genomic variations". Bioinformatics. 40 (4) btae183. doi:10.1093/bioinformatics/btae183. PMC 11055464. PMID 38579245.
  22. ^ Ma, Wei; Fong, Grace; Lai, Joe; Wu, Heidi; Hue, Shirley Pik Ying; Ying, Dingge; Chen, Lijuan; Tang, Wenshu; Preusch, Christopher; Chu, Annie Tsz Wai; Chung, Brian Hon Yin (2026). "AI-CURA, an automated LLM workflow for high-accuracy genetic variant classification". Science Translational Medicine. 18 (855) eadz4172. doi:10.1126/scitranslmed.adz4172. PMID 42341082.
  23. ^ Li, Shumin; Wang, Yiding; Liu, Chi-Man; Huang, Yuanhua; Lam, Tak-Wah; Luo, Ruibang (2025). "AutoPM3: enhancing variant interpretation via LLM-driven PM3 evidence extraction from scientific literature". Bioinformatics. 41 (7) btaf382. doi:10.1093/bioinformatics/btaf382. PMC 12263107. PMID 40586923.
  24. ^ a b Patel, Jharna M.; Hermann, Catherine E.; Growdon, Whitfield B.; Aviki, Emeline; Stasenko, Marina (2024). "ChatGPT accurately performs genetic counseling for gynecologic cancers". Gynecologic Oncology. 183: 115–119. doi:10.1016/j.ygyno.2024.04.006. PMID 38676973.
  25. ^ Changalidis, Anton; Barbitoff, Yury; Nasykhova, Yulia; Glotov, Andrey (2026). "A systematic review on the generative AI applications in human medical genetics". Frontiers in Genetics. 16 1694070. doi:10.3389/fgene.2025.1694070. PMC 12863965. PMID 41635571.
  26. ^ Ali, Shahid; Qadri, Yazdan Ahmad; Ahmad, Khurshid; et al. (2025). "Large Language Models in Genomics—A Perspective on Personalized Medicine". Bioengineering. 12 (5): 440. doi:10.3390/bioengineering12050440. PMC 12108693. PMID 40428059.

Category:Artificial intelligence Category:Genomics Category:Bioinformatics Category:Genetic counseling Category:Chatbots Category:Large language models

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