Recommendation Statement for the Validation,Implementation,and Clinical Application of Artificial Intelligence Within a Clinical Laboratory from the Digital Pathology Association
Restricted accessReview articleFirst published online 2026
Recommendation Statement for the Validation,Implementation,and Clinical Application of Artificial Intelligence Within a Clinical Laboratory from the Digital Pathology Association
The rapid adoption of digital pathology and artificial intelligence (AI) in oncology creates multiple opportunities for precision diagnostics but also creates an urgent need for evidence-based standards to ensure safe and effective implementation.
Goal:
This article, developed by the Digital Pathology Association, presents recommendations for digital pathology as well as the validation and clinical utility of AI-enabled digital pathology tools in clinical practice. This guidance addresses analytical and clinical validation, algorithm reliability, and criteria for establishing clinical utility attributed to test use. Key recommendations emphasize separate validation of scanning processes and AI algorithms, concordance studies to support interscanner generalizability, rigorous assessment of accuracy and reliability in real-world settings, and clear description of algorithmic use limitations. These recommendations further provide frameworks for when AI may replace or augment existing diagnostic approaches, such as biomarker scoring, cancer diagnosis, and prognostic risk assessment.
Conclusions:
By considering payer, regulatory, and clinical perspectives, the recommendations promote transparency, trust, and reproducibility in digital pathology while encouraging value-based care delivery. We support responsible innovation in computational pathology, ensuring that AI applications achieve not only technical performance goals but also deliver measurable clinical benefit to patients.
BultenW, BalkenholM, BelingaJ-JA, et al.; ISUP Pathology Imagebase Expert Panel. Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists. Mod Pathol, 2021; 34(3):660–671.
2.
SakamotoT, FurukawaT, PhamHHN, et al.A collaborative workflow between pathologists and deep learning for the evaluation of tumour cellularity in lung adenocarcinoma. Histopathology, 2022; 81(6):758–769.
3.
DyA, NguyenN-NJ, MeyerJ, et al.AI improves accuracy, agreement and efficiency of pathologists for Ki67 assessments in breast cancer. Sci Rep, 2024; 14(1):1283.
4.
RacitiP, SueJ, RetameroJA, et al.Clinical validation of artificial intelligence–augmented pathology diagnosis demonstrates significant gains in diagnostic accuracy in prostate cancer detection. Arch Pathol Lab Med, 2023; 147(10):1178–1185.
5.
Marron-EsquivelJM, Duran-LopezL, Linares-BarrancoA, et al.A comparative study of the inter-observer variability on Gleason grading against Deep Learning-based approaches for prostate cancer. Comput Biol Med, 2023; 159:106856.
6.
WetsteinSC, de JongVMT, StathonikosN, et al.Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images. Sci Rep, 2022; 12(1):15102.
7.
ErtosunMG, RubinDL. Automated grading of gliomas using deep learning in digital pathology images: A modular approach with ensemble of convolutional neural networks. AMIA Annu Symp Proc, 2015; 2015 pp:1899–1908.
8.
NagpalK, FooteD, LiuY, et al.Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. NPJ Digit Med, 2019; 2(1):48.
9.
SimardM, ShenZ, BräutigamK, et al.Immunocto: A massive immune cell database auto-generated for histopathology. ArXiv, 2024Prepr ArXiv240602618.
10.
GrahamS, VuQD, RazaSEA, et al.Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med Image Anal, 2019; 58:101563.
11.
NegahbaniF, SabziR, JahromiBP, et al.PathoNet: Deep learning assisted evaluation of Ki-67 and tumor infiltrating lymphocytes (TILs) as prognostic factors in breast cancer; A large dataset and baseline. ArXiv, 2020 Prepr ArXiv201004713.
12.
EstevaA, FengJ, van der WalD, et al.; NRG Prostate Cancer AI Consortium. Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials. NPJ Digit Med, 2022; 5(1):71.
13.
ZhangY, YangZ, ChenR, et al.Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer. NPJ Digit Med, 2024; 7(1):15.
14.
LoefflerCML, SainathS, JiangX, et al.545P HIBRID: Histology and ct-DNA based risk-stratification with deep learning. Ann Oncol, 2024; 35:S454.
15.
GuH, YangC, HaeriM, et al.Augmenting pathologists with NaviPath: Design and evaluation of a human-AI collaborative navigation system. In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 2023. pp. 1–19.
16.
GaffneyH, MirzaKM. Pathology in the artificial intelligence era: Guiding innovation and implementation to preserve human insight. Acad Pathol, 2025; 12(1):100166.
17.
RetameroJA, GulturkE, BozkurtA, et al.Artificial intelligence helps pathologists increase diagnostic accuracy and efficiency in the detection of breast cancer lymph node metastases. Am J Surg Pathol, 2024; 48(7):846–854.
18.
VaidyaA, ChenRJ, WilliamsonDFK, et al.Demographic bias in misdiagnosis by computational pathology models. Nat Med, 2024; 30(4):1174–1190.
19.
PantanowitzL, WileyCA, DemetrisA, et al.Experience with multimodality telepathology at the University of Pittsburgh Medical Center. J Pathol Inform, 2012; 3(1):45.
20.
MinerviniMI, YagiY, MarinoIR, et al.Development and experience with an integrated system for transplantation telepathology. Hum Pathol, 2001; 32(12):1334–1343.
21.
NassSJ, PatlakM, BaloghE. Improving cancer diagnosis and care: Patient access to oncologic imaging and pathology expertise and technologies: proceedings of a workshop. 2018.
22.
NassSJ, CohenMB, NayarR, et al.Improving cancer diagnosis and care: Patient access to high-quality oncologic pathology. Oncologist, 2019; 24(10):1287–1290.
23.
NakhlehRE, MyersJL, AllenTC, et al.Consensus statement on effective communication of urgent diagnoses and significant, unexpected diagnoses in surgical pathology and cytopathology from the College of American Pathologists and Association of Directors of Anatomic and Surgical Pathology. Arch Pathol Lab Med, 2012; 136(2):148–154.
24.
NakhlehRE, SouersR, BrownRW. Significant and unexpected, and critical diagnoses in surgical pathology: A College of American Pathologists’ survey of 1130 laboratories. Arch Pathol Lab Med, 2009; 133(9):1375–1378.
25.
RobboySJ, GrossD, ParkJY, et al.Reevaluation of the US Pathologist Workforce Size. JAMA Netw Open, 2020; 3(7):e2010648.
26.
RiendaI, ValeJ, PintoJ, et al.Using artificial intelligence to prioritize pathology samples: Report of a test drive. Virchows Arch, 2025; 487(1):203–208.
27.
WangD, JinR, ShiehC-C, et al.Real world validation of an AI-based CT hemorrhage detection tool. Front Neurol, 2023; 14:1177723.
HenleySJ, AndersonRN, ThomasCC, et al.Invasive cancer incidence, 2004–2013, and deaths, 2006–2015, in nonmetropolitan and metropolitan counties—United States. MMWR Surveill Summ, 2017; 66(14):1–13.
30.
LevitLA, ByattL, LyssAP, et al.Closing the rural cancer care gap: Three institutional approaches. JCO Oncol Pract, 2020; 16(7):422–430.
31.
UngerJM, MoseleyA, SymingtonB, et al.Geographic distribution and survival outcomes for rural patients with cancer treated in clinical trials. JAMA Netw Open, 2018; 1(4):e181235.
LavelleTA, RoseAJ, TimbieJW, et al.Utilization of health care services among Medicare beneficiaries who visit federally qualified health centers. BMC Health Serv Res, 2018; 18(1):41.
NayakL, BeaulieuCF, RubinDL, et al.A picture is worth a thousand words: Needs assessment for multimedia radiology reports in a large tertiary care medical center. Acad Radiol, 2013; 20(12):1577–1583.
36.
CabarrusM, NaegerDM, RybkinA, et al.Patients prefer results from the ordering provider and access to their radiology reports. J Am Coll Radiol, 2015; 12(6):556–562.
37.
AlarifiM, PatrickT, JabourA, et al.Full radiology report through patient web portal: A literature review. Int J Environ Res Public Health, 2020; 17(10):3673.
38.
RanotJ, NoghaniP, RothwellD, et al.If you provide them, they will come: An observational study of online pathology report access by patients at a large, academic, tertiary care hospital in Canada. J Clin Pathol, 2025; 78(9):636–640.
39.
HulterP, LangendoenW, PluutB, et al.Patients’ choices regarding online access to laboratory, radiology and pathology test results on a hospital patient portal. PLoS One, 2023; 18(2):e0280768.
CoxWAS, CavenaghP, BelloF. What are the benefits and risks of sharing patients’ diagnostic radiological images with them? A cross-sectional study of the perceptions of patients and clinicians in the UK. BMJ Open, 2020; 10(1):e033835.
42.
WienerRS, GouldMK, WoloshinS, et al.What do you mean, a spot?: A qualitative analysis of patients’ reactions to discussions with their physicians about pulmonary nodules. Chest, 2013; 143(3):672–677.
43.
CampanellaG, ChenS, SinghM, et al.A clinical benchmark of public self-supervised pathology foundation models. Nat Commun, 2025; 16(1):3640.
44.
ChenRJ, DingT, LuMY, et al.Towards a general-purpose foundation model for computational pathology. Nat Med, 2024; 30(3):850–862.
45.
VorontsovE, BozkurtA, CassonA, et al.Virchow: A million-slide digital pathology foundation model. ArXiv, 2023Prepr ArXiv230907778.
46.
AlberM, TietzS, DippelJ, et al.Atlas: A novel pathology foundation model by Mayo Clinic, Charit\backslash’e, and Aignostics. ArXiv, 2025Prepr ArXiv250105409.
47.
VorontsovE, BozkurtA, CassonA, et al.A foundation model for clinical-grade computational pathology and rare cancers detection. Nat Med, 2024; 30(10):2924–2935.
48.
LuMY, ChenB, WilliamsonDFK, et al.A visual-language foundation model for computational pathology. Nat Med, 2024; 30(3):863–874.
49.
MaJ, GuoZ, ZhouF, et al.A generalizable pathology foundation model using a unified knowledge distillation pretraining framework. Nat Biomed Eng, 2026; 10(3):545–564.
50.
DingT, WagnerSJ, SongAH, et al.A multimodal whole-slide foundation model for pathology. Nat Med, 2025:1–13.
European Parliament and Council of the European Union. Regulation (EU) 2017/746 of the European Parliament and of the Council of 5 April 2017 on in vitro diagnostic medical devices (IVDR) and repealing Directive 98/79/EC and Commission Decision 2010/227/EU [Internet]. 2017. Available from: https://eur-lex.europa.eu/eli/reg/2017/746/oj
60.
Parliament E, Union C of the E. Directive 98/79/EC of the European Parliament and of the Council on in vitro diagnostic medical devices [Internet]. 1998. Available from: https://eur-lex.europa.eu/eli/dir/1998/79/oj
61.
International Organization for Standardization. ISO 13485:2016 Medical devices—Quality management systems—Requirements for regulatory purposes [Internet]. 2016. Available from: https://www.iso.org/standard/59752.html
62.
International Organization for Standardization. ISO 15189:2022 Medical laboratories—Requirements for quality and competence [Internet]. 2022. Available from: https://www.iso.org/standard/76677.html
FrankRA, JarrinR, PritzkerJ, et al.Developing current procedural terminology codes that describe the work performed by machines. NPJ Digit Med, 2022; 5(1):177.
68.
Clinical and Laboratory Standards Institute (CLSI). Evaluation of Precision of Quantitative Measurement Procedures; Approved Guideline—Third Edition (EP05-A3) [Internet]. Wayne, PA: Clinical and Laboratory Standards Institute; 2014. Report No.: EP05-A3. Available from: https://clsi.org/standards/products/ep05/
69.
Clinical and Laboratory Standards Institute (CLSI). Evaluation of Detection Capability for Clinical Laboratory Measurement Procedures; Approved Guideline—Second Edition (EP17-A2) [Internet]. Wayne, PA: Clinical and Laboratory Standards Institute; 2012. Report No.: EP17-A2. Available from: https://clsi.org/standards/products/ep17/
70.
Clinical and Laboratory Standards Institute (CLSI). User Verification of Precision and Estimation of Bias; Approved Guideline—Third Edition (EP15-A3) [Internet]. Wayne, PA: Clinical and Laboratory Standards Institute; 2014. Report No.: EP15-A3. Available from: https://clsi.org/standards/products/ep15/
KrollMH, BiswasB, BuddJR, et al.Assessment of the diagnostic accuracy of laboratory tests using receiver operating characteristic curves; Approved Guideline—Second Edition. Clinical and Laboratory Standards Institute, 2011. p;31(23):1–45. Report No
McCormackRT, BillingsPR. Clinical utility: Informing treatment decisions by changing the paradigm. NAM Perspect, 2015.
76.
TeutschSM, BradleyLA, PalomakiGE, et al.; EGAPP Working Group. The evaluation of genomic applications in practice and prevention (EGAPP) initiative: Methods of the EGAPP working group. Genet Med, 2009; 11(1):3–14.
SamsonD, SchoellesKM. Chapter 2: Medical tests guidance (2) developing the topic and structuring systematic reviews of medical tests: Utility of PICOTS, analytic frameworks, decision trees, and other frameworks. J Gen Intern Med, 2012; 27(Suppl 1):S11–S19.
80.
Group SSSS. othersRandomised trial of cholesterol lowering in 4444 patients with coronary heart disease: The Scandinavian Simvastatin Survival Study (4S). The Lancet, 1994; 344(8934):1383–1389.
HannaMG, ArdonO, ReuterVE, et al.Integrating digital pathology into clinical practice. Mod Pathol, 2022; 35(2):152–164.
84.
BaidoshviliA, BucurA, van LeeuwenJ, et al.Evaluating the benefits of digital pathology implementation: Time savings in laboratory logistics. Histopathology, 2018; 73(5):784–794.
85.
PantanowitzL, ValensteinPN, EvansAJ, et al.Review of the current state of whole slide imaging in pathology. J Pathol Inform, 2011; 2(1):36.
86.
BaxiV, EdwardsR, MontaltoM, et al.Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol, 2022; 35(1):23–32.
87.
ChaudhariP, GuptaS, SrivastavS, et al.Digital Versus conventional teaching of surgical pathology: A comparative study. Cureus, 2023; 15(9):e45747.
88.
JahnSW, PlassM, MoinfarF. Digital pathology: Advantages, limitations and emerging perspectives. J Clin Med, 2020; 9(11):3697.
89.
MoscaluM, MoscaluR, DascăluCG, et al.Histopathological images analysis and predictive modeling implemented in digital pathology—current affairs and perspectives. Diagnostics (Basel), 2023; 13(14):2379.
90.
BaW, WangS, ShangM, et al.Assessment of deep learning assistance for the pathological diagnosis of gastric cancer. Mod Pathol, 2022; 35(9):1262–1268.
91.
LitjensG, SánchezCI, TimofeevaN, et al.Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep, 2016; 6(1):26286.
92.
SandbankJ, BataillonG, NudelmanA, et al.Validation and real-world clinical application of an artificial intelligence algorithm for breast cancer detection in biopsies. NPJ Breast Cancer, 2022; 8(1):129.
93.
BernardC, ChandrakanthSA, CornellIS, et al.; Canadian Association of Pathologists Telepathology Guidelines Committee. Guidelines from the Canadian Association of Pathologists for establishing a telepathology service for anatomic pathology using whole-slide imaging: The Canadian Association of Pathologists Telepathology Guidelines Committee. J Pathol Inform, 2014; 5(1):15.
94.
MukhopadhyayS, FeldmanMD, AbelsE, et al.Whole slide imaging versus microscopy for primary diagnosis in surgical pathology: A multicenter blinded randomized noninferiority study of 1992 cases (pivotal study). Am J Surg Pathol, 2018; 42(1):39–52.
EvansAJ, BrownRW, BuiMM, et al.Validating whole slide imaging systems for diagnostic purposes in pathology: Guideline update from the College of American Pathologists in collaboration with the American Society for Clinical Pathology and the Association for Pathology Informatics. Arch Pathol Lab Med, 2022; 146(4):440–450.
97.
AubrevilleM, BertramC, VetaM, et al.Quantifying the scanner-induced domain gap in mitosis detection. ArXiv, 2021Prepr ArXiv210316515.
98.
Swiderska-ChadajZ, de BelT, BlanchetL, et al.Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer. Sci Rep, 2020; 10(1):14398.
HannaMG, OlsonNH, ZarellaM, et al.Recommendations for performance evaluation of machine learning in pathology: A concept paper from the College of American Pathologists. Arch Pathol Lab Med, 2024; 148(10):e335–61–e361.
102.
DuenwegSR, BobholzSA, LowmanAK, et al.Whole slide imaging (WSI) scanner differences influence optical and computed properties of digitized prostate cancer histology. J Pathol Inform, 2023; 14:100321.
103.
BandiP, GeessinkO, MansonQ, et al.From detection of individual metastases to classification of lymph node status at the patient level: The camelyon17 challenge. IEEE Trans Med Imaging, 2019; 38(2):550–560.
104.
BoschmanJ, FarahaniH, DarbandsariA, et al.The utility of color normalization for AI-based diagnosis of hematoxylin and eosin-stained pathology images. J Pathol, 2022; 256(1):15–24.
105.
LinS, ZhouH, WatsonM, et al.Impact of stain variation and color normalization for prognostic predictions in pathology. Sci Rep, 2025; 15(1):2369.
106.
Us F. Principles for codevelopment of an in vitro companion diagnostic device with a therapeutic product. Silver Spring MD. US FDA. 2016;
107.
HuangW, RandhawaR, JainP, et al.Development and validation of an artificial intelligence–powered platform for prostate cancer grading and quantification. JAMA Netw Open, 2021; 4(11):e2132554.
108.
CampanellaG, HannaMG, GeneslawL, et al.Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med, 2019; 25(8):1301–1309.
109.
SteinerDF, MacDonaldR, LiuY, et al.Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer. Am J Surg Pathol, 2018; 42(12):1636–1646.
110.
CoudrayN, OcampoPS, SakellaropoulosT, et al.Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat Med, 2018; 24(10):1559–1567.
111.
Miranda RuizF, LahrmannB, BartelsL, et al.CNN stability training improves robustness to scanner and IHC-based image variability for epithelium segmentation in cervical histology. Front Med (Lausanne), 2023; 10:1173616.
112.
OchiM, KomuraD, OnoyamaT, et al.Registered multi-device/staining histology image dataset for domain-agnostic machine learning models. Sci Data, 2024; 11(1):330.
113.
PerssonJ, WilderängU, JibornT, et al.Interobserver variability in the pathological assessment of radical prostatectomy specimens: Findings of the Laparoscopic Prostatectomy Robot Open (LAPPRO) study. Scand J Urol, 2014; 48(2):160–167.
114.
VelosoSG, LimaMF, SallesPG, et al.Interobserver agreement of Gleason score and modified Gleason score in needle biopsy and in surgical specimen of prostate cancer. Int Braz J Urol, 2007; 33(5):639–651.
115.
MontironiR, Lopez-BeltranA, ChengL, et al.Central prostate pathology review: Should it be mandatory? Eur Urol, 2013; 64(2):199–201.
116.
BottkeD, GolzR, StörkelS, et al.Phase 3 study of adjuvant radiotherapy versus wait and see in pT3 prostate cancer: Impact of pathology review on analysis. Eur Urol, 2013; 64(2):193–198.
117.
OzkanTA, EruyarAT, CebeciOO, et al.Interobserver variability in Gleason histological grading of prostate cancer. Scand J Urol, 2016; 50(6):420–424.
118.
RobbinsP, PinderS, de KlerkN, et al.Histological grading of breast carcinomas: A study of interobserver agreement. Hum Pathol, 1995; 26(8):873–879.
119.
Van BockstalMR, FrançoisA, AltinayS, et al.Interobserver variability in the assessment of stromal tumor-infiltrating lymphocytes (sTILs) in triple-negative invasive breast carcinoma influences the association with pathological complete response: The IVITA study. Mod Pathol, 2021; 34(12):2130–2140.
120.
RabeK, SnirOL, BossuytV, et al.Interobserver variability in breast carcinoma grading results in prognostic stage differences. Hum Pathol, 2019; 94:51–57.
121.
GinterPS, IdressR, D’AlfonsoTM, et al.Histologic grading of breast carcinoma: A multi-institution study of interobserver variation using virtual microscopy. Mod Pathol, 2021; 34(4):701–709.
122.
DanoH, AltinayS, ArnouldL, et al.Interobserver variability in upfront dichotomous histopathological assessment of ductal carcinoma in situ of the breast: The DCISion study. Mod Pathol, 2020; 33(3):354–366.
123.
ChandlerI, HoulstonR. Interobserver agreement in grading of colorectal cancers—findings from a nationwide web-based survey of histopathologists. Histopathology, 2008; 52(4):494–499.
124.
ReisMT, MatsushitaM, SantosW, et al.Assessing the applicability and interobserver variability of tumor budding and poorly differentiated clusters in colorectal cancer. Surg Exp Pathol, 2024; 7(1):1.
125.
KomutaK, BattsK, JessurunJ, et al.Interobserver variability in the pathological assessment of malignant colorectal polyps. Br J Surg, 2004; 91(11):1479–1484.
126.
KaramchandaniDM, GonzalezRS, LeeH, et al.Interobserver agreement and practice patterns for grading of colorectal carcinoma: World Health Organization (WHO) classification of tumours 5th edition versus American Joint Committee on Cancer (AJCC) staging manual. Histopathology, 2025; 86(7):1101–1111.
127.
SmitsLJH, Vink-BörgerE, van LijnschotenG, et al.Diagnostic variability in the histopathological assessment of advanced colorectal adenomas and early colorectal cancer in a screening population. Histopathology, 2022; 80(5):790–798.
128.
PaechDC, WestonAR, PavlakisN, et al.A systematic review of the interobserver variability for histology in the differentiation between squamous and nonsquamous non-small cell lung cancer. J Thorac Oncol, 2011; 6(1):55–63.
129.
ButterR, HondelinkLM, van ElswijkL, et al.The impact of a pathologist’s personality on the interobserver variability and diagnostic accuracy of predictive PD-L1 immunohistochemistry in lung cancer. Lung Cancer, 2022; 166:143–149.
130.
TrontzasIP, HeM, WurtzA, et al.Quantitative protein expression of antibody–Drug conjugate targets in egfr mutated and wild-type non–small cell lung cancer. Clin Cancer Res, 2025; 31(13):2767–2776.
131.
WuY-L, XiaoZ, LiJ, et al.357P: Association of TROP2 quantitative continuous scoring (QCS) normalised membrane ratio (NMR) with efficacy in Chinese patients (pts) with advanced/metastatic non-small cell lung cancer (a/mNSCLC) treated with datopotamab deruxtecan (Dato-DXd) in TROPION-PanTumor02. J Thorac Oncol, 2025; 20(3):S212–S213.
132.
GarassinoMC, SandsJ, Paz-AresL, et al.PL02. 11 normalized membrane ratio of TROP2 by quantitative continuous scoring is predictive of clinical outcomes in TROPION-lung 01. J Thorac Oncol, 2024; 19(10):S2–S3.
133.
ChangSS, LaunerB, NarayanV, et al.Computational Histology Artificial Intelligence (CHAI) enhances risk stratification of high-grade Ta non–muscle-invasive bladder cancer in a multicenter cohort: Comparison to current European Association of Urology and American Urological Association stratification schemes. Eur Urol, 2025; 88(4):411–413.
134.
ShamaiG, CohenS, BinenbaumY, et al.Deep learning on histopathological images to predict breast cancer recurrence risk and chemotherapy benefit. medRxiv, 2025:2025.05.15.25327686. 2025–05.
135.
NairSS, MuhammadH, JainP, et al.A novel artificial intelligence-powered tool for precise risk stratification of prostate cancer progression in patients with clinical intermediate risk. Eur Urol, 2025; 87(6):728–729.
136.
ValierisR, MartinsL, DefelicibusA, et al.Weakly-supervised deep learning models enable HER2-low prediction from H &E stained slides. Breast Cancer Res, 2024; 26(1):124.
137.
WangJ, ZhangS, LiJ, et al.Development and clinical validation of deep learning-based immunohistochemistry prediction models for subtyping and staging of gastrointestinal cancers. BMC Gastroenterol, 2025; 25(1):494–411.
138.
LiuY, LiX, ZhengA, et al.Predict Ki-67 positive cells in H&E-stained images using deep learning independently from IHC-stained images. Front Mol Biosci, 2020; 7:183.
139.
DasA, TomitaN, SymeKJ, et al.Cross-modality learning for predicting IHC biomarkers from H&E-stained whole-slide images. Am J Pathol, 2025
140.
LevinM, AlexanianS, KallenM, et al.1195 Multi-modal spatial analysis of classic Hodgkin lymphoma microenvironments utilizing multiplex immunofluorescence and virtual staining. BMJ Specialist Journals, 2024
141.
BüttnerR, GosneyJR, SkovBG, et al.Programmed death-ligand 1 immunohistochemistry testing: A review of analytical assays and clinical implementation in non–small-cell lung cancer. J Clin Oncol, 2017; 35(34):3867–3876.
142.
RimmDL, HanG, TaubeJM, et al.A prospective, multi-institutional, pathologist-based assessment of 4 immunohistochemistry assays for PD-L1 expression in non–small cell lung cancer. JAMA Oncol, 2017; 3(8):1051–1058.
143.
ZarellaMD, BowmanD, AeffnerF, et al.A practical guide to whole slide imaging: A white paper from the digital pathology association. Arch Pathol Lab Med, 2019; 143(2):222–234.
144.
TizhooshHR, PantanowitzL. Artificial intelligence and digital pathology: Challenges and opportunities. J Pathol Inform, 2018; 9(1):38.
MeyerJ, KhademiA, TêtuB, et al.Impact of artificial intelligence on pathologists’ decisions: An experiment. J Am Med Inform Assoc, 2022; 29(10):1688–1695.
147.
WongA, OtlesE, DonnellyJP, et al.External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med, 2021; 181(8):1065–1070.
148.
MarcuLG, MarcuDC. Examining the role of AI in cancer imaging through the lens of clinical studies. Health Technol, 2025; 15(6):1065–1074.
149.
EchleA, RindtorffNT, BrinkerTJ, et al.Deep learning in cancer pathology: A new generation of clinical biomarkers. Br J Cancer, 2021; 124(4):686–696.
150.
KatherJN, PearsonAT, HalamaN, et al.Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med, 2019; 25(7):1054–1056.
BrügmannA, EldM, LelkaitisG, et al.Digital image analysis of membrane connectivity is a robust measure of HER2 immunostains. Breast Cancer Res Treat, 2012; 132(1):41–49.
156.
NiaziMKK, ParwaniAV, GurcanMN. Digital pathology and artificial intelligence. Lancet Oncol, 2019; 20(5):e253–61–e261.
157.
EloyC, MarquesA, PintoJ, et al.Artificial intelligence–assisted cancer diagnosis improves the efficiency of pathologists in prostatic biopsies. Virchows Arch, 2023; 482(3):595–604.
158.
GoldenJA. Deep learning algorithms for detection of lymph node metastases from breast cancer: Helping artificial intelligence be seen. JAMA, 2017; 318(22):2184–2186.
159.
SteinerDF, NagpalK, SayresR, et al.Evaluation of the use of combined artificial intelligence and pathologist assessment to review and grade prostate biopsies. JAMA Netw Open, 2020; 3(11):e2023267.
160.
PerincheriS, LeviAW, CelliR, et al.An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy. Mod Pathol, 2021; 34(8):1588–1595.
161.
Roche granted FDA Breakthrough Device Designation for first AI-driven companion diagnostic for non-small cell lung cancer [Internet] Available from: https://www.roche.com//media/releases/med-cor-2025-04-29 [Last accessed: Jan28, 2026].
162.
GuyattGH, OxmanAD, VistGE, et al.; GRADE Working Group. GRADE: An emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 2008; 336(7650):924–926.
163.
Ibex Prostate becomes first standalone AI-powered cancer diagnostics solution to obtain CE mark under the IVDR - IBEX [Internet]. 2023. Available from: https://ibex-ai.com/ivdr23/ [Last accessed: Jan1, 2026].
ChenC, LuMY, WilliamsonDF, et al.Fast and scalable search of whole-slide images via self-supervised deep learning. Nat Biomed Eng, 2022; 6(12):1420–1434.
166.
SprattDE, TangS, SunY, et al.Artificial intelligence predictive model for hormone therapy use in prostate cancer. NEJM Evid, 2023; 2(8):EVIDoa2300023.
167.
ChenRJ, LuMY, WangJ, et al.Pathomic fusion: An integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Trans Med Imaging, 2022; 41(4):757–770.
168.
ChunduruP, PhillipsJJ, MolinaroAM. Prognostic risk stratification of gliomas using deep learning in digital pathology images. Neurooncol Adv, 2022; 4(1):vdac111.
169.
WulczynE, SteinerDF, XuZ, et al.Deep learning-based survival prediction for multiple cancer types using histopathology images. PLoS One, 2020; 15(6):e0233678.
170.
ChoiS, KimS. Artificial intelligence in the pathology of gastric cancer. J Gastric Cancer, 2023; 23(3):410–427.
171.
OakleyGIII, Reis-FilhoJ, KlimstraD, et al.Deep learning-based assessment of HER2-low expression on breast cancer H&E digital whole slide images. In: CANCER RESEARCH. AMER ASSOC CANCER RESEARCH. 615 CHESTNUT ST, 17TH FLOOR, PHILADELPHIA, PA. 2023.
172.
JanowczykA, ZuoR, GilmoreH, et al.HistoQC: An open-source quality control tool for digital pathology slides. JCO Clin Cancer Inform, 2019; 3:1–7.
173.
WeinsteinJN, CollissonEA, MillsGB, et al.The cancer genome atlas pan-cancer analysis project. Nat Genet, 2013; 45(10):1113–1120.
174.
HaghighatM, BrowningL, SirinukunwattanaK, et al.Automated quality assessment of large digitised histology cohorts by artificial intelligence. Sci Rep, 2022; 12(1):5002.
175.
WengZ, SeperA, PryalukhinA, et al.GrandQC: A comprehensive solution to quality control problem in digital pathology. Nat Commun, 2024; 15(1):10685.
176.
JabarF, Rasmussen BusundL-T, RicciutiB, et al.Fully automatic content-aware tiling pipeline for pathology whole slide images. Intell-Based Med, 2025; 12:100318.
PetryM, LanskyC, ChodakiewitzY, et al.Decreased hospital length of stay for ICH and PE after adoption of an artificial intelligence-augmented radiological worklist triage system. Radiol Res Pract, 2022; 2022(1):2141839.
186.
PantanowitzL, Quiroga-GarzaGM, BienL, et al.An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: A blinded clinical validation and deployment study. Lancet Digit Health, 2020; 2(8):e407–16–e416.
187.
LuG, FeiB. Medical hyperspectral imaging: A review. J Biomed Opt, 2014; 19(1):10901.
188.
LiuW, WangL, LiuJ, et al.A comparative performance analysis of multispectral and rgb imaging on her2 status evaluation for the prediction of breast cancer prognosis. Transl Oncol, 2016; 9(6):521–530.
