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Bulletin references April 2026

You can read the April 2026 Bulletin here.

AI in pathology services

AI-assisted diagnosis of rare ocular cancers

  1. Angi M, Kalirai H, Taktak A, Hussain R, Groenewald C, Damato BE et al. Prognostic biopsy of choroidal melanoma: an optimised surgical and laboratory approach. Br J Ophthalmol 2017;101(8):1143–1146.
  2. Lane AM, Kim IK, Gragoudas ES. Survival rates in patients after treatment for metastasis from uveal melanoma. JAMA Ophthalmol 2018;136(9):981–986.
  3. Woodman SE. Metastatic uveal melanoma: Biology and emerging treatments. Cancer J (Sudbury, Mass.) 2012;18(2):148–152.
  4. Singh AD, Kalyani P, Topham A. Estimating the risk of malignant transformation of a choroidal nevus. Ophthalmol 2005;112(10):1784–1789.
  5. Zhou Y, Chia MA, Wagner SK, Ayhan MS, Williamson DJ, Struyven RR et al. A foundation model for generalizable disease detection from retinal images. Nature 2023;622(7981):156–163.
  6. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016:770–778.
  7. Tan M, Le QV. EfficientNet: Rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning 2019;97:6105–6114.
  8. Huang G, Liu Z, van der Maaten L, Weinberger KQ. Densely connected convolutional networks. Proceedings – 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017; 2261–2269.
  9. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. MobileNetV2: Inverted residuals and linear bottlenecks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2018;4510–4520.
  10. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations, 2015.
  11. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: Visual explanations from deep networks via gradient-based localization. 2017 IEEE International Conference on Computer Vision (ICCV) 2017; 618–626.
  12. Zhang H, Kalirai H, Acha-Sagredo A, Yang X, Zheng Y, Coupland SE. Piloting a deep learning model for predicting nuclear BAP1 immunohistochemical expression of uveal melanoma from hematoxylin-and-eosin sections. Transl Vis Sci Tech 2020;9(2):50.

Establishing an open register of AI products for digital pathology

  1. Matthews G, McGenity C, Bansal D, Treanor D. Public evidence on AI products for digital pathology. NPJ Digital Medicine 2024;7:300.
  2. Matthews G, Godson L, McGenity C, Bansal D, Treanor D. Artificial intelligence devices for image analysis in digital pathology (preprint). medRxiv 2026.03.23.26349089.
  3. The Health Foundation. Priorities for an AI in health care strategy. Published June 2024. Available at: www.health.org.uk/reports-and-analysis/briefings/priorities-for-an-ai-in-health-care-strategy
  4. NHS England Transformation Directorate. Surveying public perceptions of AI. Accessed February 2026. Available at: webarchive.nationalarchives.gov.uk/ukgwa/20240501051957/https:/transform.england.nhs.uk/ai-lab/ai-lab-programmes/the-national-strategy-for-ai-in-health-and-social-care/surveying-public-perceptions-of-ai/
  5. Regulatory Horizons Council. Regulatory Horizons Council: The regulation of Artificial Intelligence as a Medical Device. Published 30 November 2022. Available at: www.gov.uk/government/publications/regulatory-horizons-council-the-regulation-of-artificial-intelligence-as-a-medical-device
  6. UK Government. 10 Year Health Plan for England: fit for the future. Published 3 July 2025. Available at: www.gov.uk/government/publications/10-year-health-plan-for-england-fit-for-the-future

College news

The patient’s perspective, from a College Lay Advisor

  1. The Health Foundation. Inequalities in life expectancy and healthy life expectancy. Available at: www.health.org.uk/evidence-hub/health-inequalities/inequalities-in-life-expectancy-and-healthy-life-expectancy

2026 Scottish and Welsh elections: how we're preparing

  1. Royal College of Pathologists. Scotland Regional Council. Available at: www.rcpath.org/our-uk-regions/example-devolved-nation-page-scotland/scotland-regional-council.html        
  2. Royal College of Pathologists. Wales Regional Council. Available at: www.rcpath.org/our-uk-regions/wales/wales-regional-council.html  
  3. Royal College of Pathologists. Scotland election priorities, 2026. Available at: www.rcpath.org/static/713a16eb-8ba8-475a-a981dc4d12448463/RCPath-Scotland-election-priorities-Feb-2026.pdf
  4. Royal College of Pathologists. Workforce census spotlight 1: response rate, retirements and working patterns, 2025. Available at: www.rcpath.org/static/1563bf43-60cf-4128-984197aaf06c5dd2/RCPath-workforce-census-spotlight-1.pdf   
  5. Royal College of Pathologists. Wales election priorities, 2026. Available at: www.rcpath.org/static/98e25b76-04ba-46dd-94fc2157a7a943e3/147c1712-d079-42db-a252d52756174c94/RCPath-election-priorities-for-Wales-2026.pdf
  6. Royal College of Pathologists. Workforce census spotlight 1: response rate, retirements and working patterns, 2025. Available at: www.rcpath.org/discover-pathology/news/workforce-census-2025-response-rate-retirements-working-patterns-and-sustainability-of-pathology-services.html     
  7. Welsh Government. NHS Wales performance and productivity: independent review, 2025. Available at: www.gov.wales/nhs-wales-performance-and-productivity-independent-review

Sharing our subject

Reimagining histopathology: a laboratory built for the future

  1. Cambridge University Hospitals NHS Trust. Building for the future. Available at: www.cuh.nhs.uk/about-us/our-hospitals/our-strategy/building-for-the-future/
  2. Cambridge University Hospitals NHS Trust. CUH Histopathology- Move to 1000 Discovery Drive. Available at: www.youtube.com/watch?v=mhQUJL5cb_w
  3. Hoare Lea. Cambridge University Hospitals Histopathology. Available at:  https://hoarelea.com/project-story/cambridge-university-hospital-trusts-histopathology-hub/
  4. Axlab. AS-410M – Auto Slide Preparation System. Available at: https://axlab.co.uk/produkt/as-410m-auto-slide-preparation-system/

Training

The Sampson equation for LDL-cholesterol estimation: Clinical biochemistry impact and rationale for adoption: an introductory guide for trainees

  1. Sampson M, Ling C, Sun Q, Harb R, Ashmaih M, Warnick R et al. A new equation for calculation of low-density lipoprotein cholesterol in patients with normolipidemia and/or hypertriglyceridemia. JAMA Cardiol 2020;5:540–548.
  2. Kenkre JS, Mazaheri T, Neely RDG, Datta D, Penson P, Downie P et al. Standardising lipid testing and reporting in the United Kingdom; a joint statement by HEART UK and The Association for Laboratory Medicine. Ann Clin Biochem 2025;62:257–286.
  3. Sampson M, Zubiran R, Wolska A, Meeusen JW, Donato LJ, Jaffe AS et al. A modified Sampson–NIH equation with improved accuracy for estimating low levels of low-density lipoprotein-cholesterol. Clin Chem 2025;71:1125–1137.