{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T13:25:36Z","timestamp":1787232336504,"version":"build-2736575974"},"reference-count":47,"publisher":"Bioscientifica","issue":"8","license":[{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":4,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["journals.bioscientifica.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) is rapidly moving from conceptual innovation to high-performing algorithms across the pituitary patient pathway, promising benefits to patients, endocrinologists and surgeons alike. Pre-operatively, machine learning applied to facial imaging and natural language processing of electronic health records show potential for earlier identification of pituitary adenomas and timelier referral from primary providers to a specialist endocrinologist. Intraoperatively, computer vision systems have already augmented surgical training but have the potential to become integrated real-time decision support systems. Post-operatively, predictive models may help to forecast complications and longer-term remission, endocrinological outcomes or recurrence. Across these domains, the emergence of large, multimodal datasets, which integrate clinical text, endocrinological investigations, radiological imaging, and intraoperative video, promises further AI improvements, yet the impact of AI on real-world clinical decision-making is less clear and depends as much on implementation processes and clinicians themselves. This review provides an overview of AI technologies for pituitary patients and explores some of the challenges translating them to clinical practice.<\/jats:p>","DOI":"10.1530\/erc-26-0104","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T14:35:46Z","timestamp":1785940546000},"update-policy":"https:\/\/doi.org\/10.1530\/crossmarkpolicy-8","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in pituitary surgery: the path to clinical solutions"],"prefix":"10.1530","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2506-6965","authenticated-orcid":true,"given":"George","family":"Hudson","sequence":"first","affiliation":[{"name":"Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery 1 , ,","place":["London, UK"]},{"name":"UCL Queen Square Institute of Neurology 2 , ,","place":["London, UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danyal Z","family":"Khan","sequence":"additional","affiliation":[{"name":"Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery 1 , ,","place":["London, UK"]},{"name":"UCL Queen Square Institute of Neurology 2 , ,","place":["London, UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephanie E","family":"Baldeweg","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, UCLH 3 , ,","place":["London, UK"]},{"name":"Department of Medicine, UCL 4 , ,","place":["London, UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hani J","family":"Marcus","sequence":"additional","affiliation":[{"name":"Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery 1 , ,","place":["London, UK"]},{"name":"UCL Queen Square Institute of Neurology 2 , ,","place":["London, UK"]},{"name":"Panda Surgical Ltd 5 , ,","place":["London, UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"416","published-online":{"date-parts":[[2026,8,5]]},"reference":[{"key":"2026082008365946400_bib1","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1001\/jama.2016.19699","article-title":"Diagnosis and treatment of pituitary adenomas: a review","volume":"317","author":"Molitch","year":"2017","journal-title":"JAMA"},{"key":"2026082008365946400_bib2","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s10143-023-02136-8","article-title":"The learning curve for endoscopic endonasal pituitary surgery: a systematic review","volume":"46","author":"Candy","year":"2023","journal-title":"Neurosurg Rev"},{"key":"2026082008365946400_bib3","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1210\/endrev\/bnad014","article-title":"Current and future advances in surgical therapy for pituitary adenoma","volume":"44","author":"Khan","year":"2023","journal-title":"Endocr Rev"},{"key":"2026082008365946400_bib4","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1007\/s11102-025-01502-7","article-title":"Identifying research priorities for pituitary adenoma surgery: an international Delphi consensus statement","volume":"28","author":"Newall","year":"2025","journal-title":"Pituitary"},{"key":"2026082008365946400_bib5","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2024-097804","article-title":"Estimating diagnostic delay in patients with pituitary adenomas in Sweden: a cross-sectional study","volume":"15","author":"Forsgren","year":"2025","journal-title":"BMJ Open"},{"key":"2026082008365946400_bib6","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1007\/s11102-024-01495-9","article-title":"Reappraising prediction of surgical complexity of non-functioning pituitary adenomas after transsphenoidal surgery: the modified TRANSSPHER grade","volume":"28","author":"Fiore","year":"2025","journal-title":"Pituitary"},{"key":"2026082008365946400_bib7","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1159\/000502211","article-title":"Deep-learning approach to automatic identification of facial anomalies in endocrine disorders","volume":"110","author":"Wei","year":"2020","journal-title":"Neuroendocrinology"},{"key":"2026082008365946400_bib8","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1093\/ejendo\/lvad005","article-title":"Real-time detection of acromegaly from facial images with artificial intelligence","volume":"188","author":"Kizilgul","year":"2023","journal-title":"Eur J Endocrinol"},{"key":"2026082008365946400_bib9","doi-asserted-by":"crossref","DOI":"10.1210\/jendso\/bvaf203","article-title":"Detection of acromegaly from facial images using machine learning: a comparison with clinical experts","volume":"10","author":"Vouzouneraki","year":"2026","journal-title":"J Endocr Soc"},{"key":"2026082008365946400_bib10","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1007\/s11102-025-01515-2","article-title":"Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace system","volume":"28","author":"Rashwan","year":"2025","journal-title":"Pituitary"},{"key":"2026082008365946400_bib11","article-title":"Facial analysis in acromegaly using machine learning: toward earlier diagnosis","volume":"111","author":"Kocaman","year":"2025","journal-title":"J Clin Endocrinol Metab"},{"key":"2026082008365946400_bib12","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1186\/s12911-018-0623-9","article-title":"CogStack \u2013 experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust Hospital","volume":"18","author":"Jackson","year":"2018","journal-title":"BMC Med Inf Decis Making"},{"key":"2026082008365946400_bib13","article-title":"Diagnostic performance of GPT-4o and claude 3 opus in determining causes of death from medical histories and postmortem CT findings","volume":"16","author":"Ishida","year":"2024","journal-title":"Cureus"},{"key":"2026082008365946400_bib14","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1038\/s41746-025-01486-5","article-title":"Multiple large language models versus experienced physicians in diagnosing challenging cases with gastrointestinal symptoms","volume":"8","author":"Yang","year":"2025","journal-title":"NPJ Digit Med"},{"key":"2026082008365946400_bib15","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1007\/s11547-025-02040-9","article-title":"Automated MRI protocoling in neuroradiology in the era of large language models","volume":"130","author":"Reiner","year":"2025","journal-title":"Radiol Med"},{"key":"2026082008365946400_bib16","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.243774","article-title":"Comparison of a specialized large language model with GPT-4o for CT and MRI radiology report summarization","volume":"316","author":"Zheng","year":"2025","journal-title":"Radiology"},{"key":"2026082008365946400_bib17","doi-asserted-by":"crossref","first-page":"2731","DOI":"10.1007\/s00330-018-5876-2","article-title":"Predicting response to somatostatin analogues in acromegaly: machine learning-based high-dimensional quantitative texture analysis on T2-weighted MRI","volume":"29","author":"Kocak","year":"2019","journal-title":"Eur Radiol"},{"key":"2026082008365946400_bib18","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1016\/j.acra.2024.09.023","article-title":"Noninvasive radiomics approach predicts dopamine agonists treatment response in patients with prolactinoma: a multicenter study","volume":"32","author":"Fan","year":"2025","journal-title":"Acad Radiol"},{"key":"2026082008365946400_bib19","article-title":"Evaluation of ChatGPT as a supplementary tool for pituitary adenomas: an observational study based on simulated consultations","volume":"104","author":"Chen","year":"2025","journal-title":"Medicine"},{"key":"2026082008365946400_bib20","doi-asserted-by":"crossref","first-page":"6179","DOI":"10.1038\/s41598-025-89996-w","article-title":"Analyzing patient perspectives with large language models: a cross-sectional study of sentiment and thematic classification on exception from informed consent","volume":"15","author":"Kornblith","year":"2025","journal-title":"Sci Rep"},{"key":"2026082008365946400_bib21","article-title":"Real-world deployment and evaluation of PErioperative AI CHatbot (PEACH) \u2013 a large language model chatbot for perioperative medicine","volume-title":"arXiv","author":"Ke","year":"2024"},{"key":"2026082008365946400_bib22","article-title":"Ethics and governance of artificial intelligence for health. Guidance on large multi-modal models","year":"2024"},{"key":"2026082008365946400_bib23","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s11102-021-01162-3","article-title":"Pituitary society expert delphi consensus: operative workflow in endoscopic transsphenoidal pituitary adenoma resection","volume":"24","author":"Marcus","year":"2021","journal-title":"Pituitary"},{"key":"2026082008365946400_bib24","doi-asserted-by":"crossref","first-page":"51","DOI":"10.3171\/2021.6.JNS21923","article-title":"Automated operative workflow analysis of endoscopic pituitary surgery using machine learning: development and preclinical evaluation (IDEAL stage 0)","volume":"137","author":"Khan","year":"2022","journal-title":"J Neurosurg"},{"key":"2026082008365946400_bib25","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1049\/htl2.12099","article-title":"PitRSDNet: predicting intra-operative remaining surgery duration in endoscopic pituitary surgery","volume":"11","author":"Wijekoon","year":"2024","journal-title":"Healthc Technol Lett"},{"key":"2026082008365946400_bib26","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1049\/htl2.12101","article-title":"Automated surgical skill assessment in endoscopic pituitary surgery using real-time instrument tracking on a high-fidelity bench-top phantom","volume":"11","author":"Das","year":"2024","journal-title":"Healthc Technol Lett"},{"key":"2026082008365946400_bib27","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1007\/s11548-024-03094-2","article-title":"PitSurgRT: real-time localization of critical anatomical structures in endoscopic pituitary surgery","volume":"19","author":"Mao","year":"2024","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2026082008365946400_bib28","doi-asserted-by":"crossref","first-page":"e797","DOI":"10.1016\/j.wneu.2024.07.219","article-title":"Video-based performance analysis in pituitary surgery \u2013 part 2: artificial intelligence assisted surgical coaching","volume":"190","author":"Khan","year":"2024","journal-title":"World Neurosurg"},{"key":"2026082008365946400_bib29","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1038\/s41746-024-01273-8","article-title":"Artificial intelligence assisted operative anatomy recognition in endoscopic pituitary surgery","volume":"7","author":"Khan","year":"2024","journal-title":"NPJ Digit Med"},{"key":"2026082008365946400_bib30","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-72089-5_46","article-title":"PitVQA: image-grounded text embedding LLM for visual question answering in pituitary surgery [internet]","volume-title":"arXiv","author":"He","year":"2024"},{"key":"2026082008365946400_bib31","article-title":"SurgicalVLM-agent: towards an interactive AI co-pilot for pituitary surgery","volume-title":"arXiv","author":"Huang","year":"2025"},{"key":"2026082008365946400_bib32","doi-asserted-by":"crossref","DOI":"10.64898\/2026.06.11.26355205","article-title":"Computer vision for real-time anatomical navigation in neurosurgery: first-in-human clinical evaluation and iterative development (IDEAL Stage 1)","author":"Khan","year":"2026"},{"key":"2026082008365946400_bib33","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2022-070904","article-title":"Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: decide-AI","volume":"377","author":"Vasey","year":"2022","journal-title":"BMJ"},{"key":"2026082008365946400_bib34","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1007\/s11102-020-01056-w","article-title":"Feasibility of machine learning based predictive modelling of postoperative hyponatremia after pituitary surgery","volume":"23","author":"Voglis","year":"2020","journal-title":"Pituitary"},{"key":"2026082008365946400_bib35","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1038\/s41598-024-83319-1","article-title":"Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma","volume":"15","author":"Lin","year":"2025","journal-title":"Sci Rep"},{"key":"2026082008365946400_bib36","doi-asserted-by":"crossref","first-page":"102","DOI":"10.4103\/neurol-india.Neurol-India-D-24-00350","article-title":"Machine learning: a novel approach for predicting visual outcomes and factors affecting it in patients with pituitary adenomas","volume":"73","author":"Shah","year":"2025","journal-title":"Neurol India"},{"key":"2026082008365946400_bib37","article-title":"Machine learning driven prediction of cerebrospinal fluid rhinorrhoea following endonasal skull base surgery: a multicentre prospective observational study","volume":"13","author":"Das","year":"2023","journal-title":"Front Oncol"},{"key":"2026082008365946400_bib38","article-title":"Artificial intelligence models as predictors of poor outcomes in transsphenoidal pituitary tumor surgeries: a diagnostic test accuracy review","volume":"205","author":"Bacelar Ferreira","year":"2025","journal-title":"World Neurosurg"},{"key":"2026082008365946400_bib39","first-page":"332","article-title":"Concept recognition and characterization of patients undergoing resection of vestibular schwannoma using natural language processing","volume":"86","author":"Williams","year":"2024","journal-title":"J Neurol Surg B Skull Base"},{"key":"2026082008365946400_bib40","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1038\/s41746-025-01952-0","article-title":"AI assisted prediction of unplanned intensive care admissions using natural language processing in elective neurosurgery","volume":"8","author":"Ive","year":"2025","journal-title":"NPJ Digit Med"},{"key":"2026082008365946400_bib41","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1148\/radiol.2020200723","article-title":"Thin-slice pituitary MRI with deep learning-based reconstruction: diagnostic performance in a postoperative setting","volume":"298","author":"Kim","year":"2021","journal-title":"Radiology"},{"key":"2026082008365946400_bib42","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s11102-020-01086-4","article-title":"Machine learning in predicting early remission in patients after surgical treatment of acromegaly: a multicenter study","volume":"24","author":"Qiao","year":"2021","journal-title":"Pituitary"},{"key":"2026082008365946400_bib43","doi-asserted-by":"crossref","first-page":"e217","DOI":"10.1210\/clinem\/dgaa698","article-title":"Development of machine learning models for predicting postoperative delayed remission in patients with Cushing\u2019s disease","volume":"106","author":"Fan","year":"2021","journal-title":"J Clin Endocrinol Metab"},{"key":"2026082008365946400_bib44","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.3390\/cancers16061199","article-title":"Machine learning analysis of post-operative tumour progression in non-functioning pituitary neuroendocrine tumours: a pilot study","volume":"16","author":"Hussein","year":"2024","journal-title":"Cancers"},{"key":"2026082008365946400_bib45","doi-asserted-by":"crossref","first-page":"2771","DOI":"10.3390\/cancers15102771","article-title":"Post-operative medium- and long-term endocrine outcomes in patients with non-functioning pituitary adenomas-machine learning analysis","volume":"15","author":"Hussein","year":"2023","journal-title":"Cancers"},{"key":"2026082008365946400_bib46","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1186\/s12902-025-01955-8","article-title":"Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis","volume":"25","author":"Mohammadzadeh","year":"2025","journal-title":"BMC Endocr Disord"},{"key":"2026082008365946400_bib47","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s12020-025-04351-3","article-title":"Using machine learning to predict remission after surgery for pituitary adenoma: a systematic review and meta-analysis","volume":"90","author":"Mohammadzadeh","year":"2025","journal-title":"Endocrine"}],"container-title":["Endocrine-Related Cancer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.bioscientifica.com\/erc\/article-pdf\/doi\/10.1530\/ERC-26-0104\/523143\/erc-26-0104.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/journals.bioscientifica.com\/erc\/article-pdf\/doi\/10.1530\/ERC-26-0104\/523143\/erc-26-0104.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T12:37:09Z","timestamp":1787229429000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.bioscientifica.com\/erc\/article\/33\/8\/e260104\/77942\/Artificial-intelligence-in-pituitary-surgery-the"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,1]]},"references-count":47,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,8,1]]}},"URL":"https:\/\/doi.org\/10.1530\/erc-26-0104","relation":{},"ISSN":["1351-0088","1479-6821"],"issn-type":[{"value":"1351-0088","type":"print"},{"value":"1479-6821","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,1]]},"article-number":"e260104"}}