ARTIFICIAL INTELLIGENCE
28 Aiforia. Aiforia secures IVDR certification, paving the way for expanded clinical portfolio in Europe. (Aiforia, 2025)
www.aiforia.com/press-releases/ivdr- certification-and-new-ceivd-marked- products
29 Du X, Hao S, Olsson H, et al. Effectiveness and Cost-effectiveness of Artificial Intelligence-assisted Pathology for Prostate Cancer Diagnosis in Sweden: A Microsimulation Study. Eur Urol Oncol. 2025;8(1):80-86. doi:10.1016/j. euo.2024.05.004
Fig 4. Illustration depicting the Aiosyn Mitosis Breast AI-powered solution. Cells marked in green denote instances of mitotic figures.
compbiomed.2023.106856
14 Kim T, Chang H, Kim B, et al. Deep learning-based diagnosis of lung cancer using a nationwide respiratory cytology image set: improving accuracy and inter- observer variability. Am J Cancer Res. 2023 Nov 15;13(11):5493-5503. eCollection 2023.
15 Eloy C, Marques A, Pinto J, et al. Artificial intelligence-assisted cancer diagnosis improves the efficiency of pathologists in prostatic biopsies. Virchows Arch. 2023;482(3):595-604. doi:10.1007/s00428- 023-03518-5
16 Xue P, Xu HM, Tang HP, et al. Improving the Accuracy and Efficiency of Abnormal Cervical Squamous Cell Detection With Cytologist-in-the-Loop Artificial Intelligence. Mod Pathol. 2023;36(8):100186. doi:10.1016/j. modpat.2023.100186
17 Gross DJ, Robboy SJ, Cohen MB, et al. Strong Job Market for Pathologists: Results From the 2021 College of American Pathologists Practice Leader Survey. Arch Pathol Lab Med. 2023;147(4):434-441. doi:10.5858/arpa.2022-0023-CP
18 Walsh E, Orsi NM. The current troubled state of the global pathology workforce: a concise review. Diagn Pathol. 2024 Dec 21;19(1):163.. doi:10.1186/s13000-024- 01590-2
19 Metter DM, Colgan TJ, Leung ST, Timmons CF, Park JY. Trends in the US and Canadian Pathologist Workforces From 2007 to 2017. JAMA Netw Open. 2019 May 3;2(5):e194337. doi:10.1001/ jamanetworkopen.2019.4337
20 Kanan C, Sue J, Grady L, et al. Independent validation of Paige Prostate: Assessing clinical benefit of an artificial intelligence tool within a digital diagnostic pathology laboratory workflow. J Clin Oncol. 2020;38(15):e14076-e14076 doi:10.1200/JCO.2020.38.15_suppl. e14076
21 Sharma A, Lövgren SK, Eriksson KL,
et al. Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images. Breast Cancer Res. 2024 Aug 14;26(1):123. doi:10.1186/s13058-024-01879-6
22 Lami K, Tachibana Y, Grinwald M, Ziv R, Sandbank J, Vecsler M. Triage and Diagnosis of Primary Gastric Carcinoma Using an AI Solution: A Real-World Pilot Study in Japan. Laboratory Investigation. 2025;105(3):102858. https://www.
laboratoryinvestigation.org/article/S0023- 6837(24)02537-6/fulltext
23 Matthews GA, McGenity C, Bansal D, Treanor D. Public evidence on AI products for digital pathology. NPJ Digit Med. 2024;7(1):300. doi:10.1038/s41746-024- 01294-3
24 Grobholz R, Janowczyk A, Zlobec I. Update on digital pathology and tissue-based AI algorithms in Switzerland. Medinfo Aerzteverlag. 2025;15(2-3). https://
www.medinfo-verlag.ch/infooncosuisse/ update-on-digital-pathology-and- tissue-based-ai-algorithms-in-
switzerland/#:~:text=Digitisation%20of%20 the%20pathology%20lab,Artificial%20 Intelligence%20(AI)%20solutions.
25 Nakagawa K, Moukheiber L, Celi LA, et al. AI in Pathology: What could possibly go wrong?. Semin Diagn Pathol. 2023;40(2):100-108. doi:10.1053/j. semdp.2023.02.006
26 Lujan G, Quigley JC, Hartman D, et al. Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association. J Pathol Inform. 2021;12:17. doi:10.4103/
jpi.jpi_67_20
27 Correas A. Aiosyn Mitosis Breast becomes the first AI-powered mitosis detection solution to achieve CE mark certification under IVDR. (Aiosyn, 2025) https://www.
aiosyn.com/news/aiosyn-mitosis-breast- becomes-the-first-ai-powered-mitosis- detection-solution-to-achieve-ce-mark- certification-under-ivdr/
WWW.PATHOLOGYINPRACTICE.COM OCTOBER 2025
30 van Dooijeweert C, Flach RN, Ter Hoeve ND, et al. Clinical implementation of artificial-intelligence-assisted detection of breast cancer metastases in sentinel lymph nodes: the CONFIDENT-B single- center, non-randomized clinical trial. Nat Cancer. 2024;5(8):1195-1205. doi:10.1038/ s43018-024-00788-z
31 Postma EL, Verkooijen HM, van Diest PJ, Willems SM, van den Bosch MA, van Hillegersberg R. Discrepancy between routine and expert pathologists’ assessment of non-palpable breast cancer and its impact on locoregional and systemic treatment. Eur J Pharmacol. 2013;717(1-3):31-35. doi:10.1016/j. ejphar.2012.12.033
Anna Correas Anna earned her BSc in Biotechnology from the University of Barcelona and her MSc in Science and Business Management from Utrecht University. Before joining Aiosyn in 2023, she worked as a marketing associate in the biotechnology and medical devices sector, where she had the opportunity to develop scientific marketing content tailored to the varied needs of clinical laboratories.
Dr David Tellez David has a background in computer science and telecommunications engineering and earned his PhD in Computational Pathology from Radboud University, focusing on the development of deep learning-based algorithms. As co-founder and Chief Technology Officer of Aiosyn since 2021, he is responsible for the technology that powers Aiosyn’s AI solutions, coordinating efforts between the software engineering and the AI science teams.
Dr Diana Rosentul Diana earned her PhD in Medical Sciences from Radboud University, followed by a postdoctoral position at Tel Aviv University. She also holds a Regulatory Affairs Specialist (Medical Devices) certificate from RA People Academy in Amsterdam. In her role as a Regulatory Affairs Specialist, Diana guides Aiosyn through the evolving regulatory landscape for AI-powered pathology solutions.
contact@aiosyn.com
www.aiosyn.com
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