Evaluation of artificial intelligence on a reference standard based on subjective interpretation

STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration

Cohen, J. F., Korevaar, D. A., Altman, D. G., Bruns, D. E., Gatsonis, C. A., Hooft, L., Irwig, L., Levine, D., Reitsma, J. B., de Vet, H. C. W., et al. (2016) · BMJ Open · doi:10.1136/bmjopen-2016-012799 · Cited by 2,570 · see citing works

Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study

Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J., Hulsbergen-van de Kaa, C., & Litjens, G. (2020) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30739-9 · Cited by 705 · see citing works

Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study

Ström, P., Kartasalo, K., Olsson, H., Solorzano, L., Delahunt, B., Berney, D. M., Bostwick, D. G., Evans, A. J., Grignon, D. J., Humphrey, P. A., et al. (2020) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30738-7 · Cited by 620 · see citing works

How to Read Articles That Use Machine Learning

Liu, Y., Chen, P.-H. C., Krause, J., & Peng, L. H. (2019) · JAMA · doi:10.1001/jama.2019.16489 · Cited by 530 · see citing works

An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study

Pantanowitz, L., Quiroga‐Garza, G. M., Bien, L., Heled, R., Laifenfeld, D., Linhart, C., Sandbank, J., Shach, A. A., Shalev, V., Vecsler, M., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30159-x · Cited by 361 · see citing works

Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens

Nagpal, K., Foote, D., Tan, F. E., Liu, Y., Chen, P.-H. C., Steiner, D. F., Manoj, N. S., Olson, N. H., Smith, J. L., Mohtashamian, A., et al. (2020) · JAMA Oncology · doi:10.1001/jamaoncol.2020.2485 · Cited by 223 · see citing works

Use of Expert Panels to Define the Reference Standard in Diagnostic Research: A Systematic Review of Published Methods and Reporting

Bertens, L. C. M., Broekhuizen, B. D., Naaktgeboren, C. A., Rutten, F. H., Hoes, A. W., van Mourik, Y., Moons, K. G. M., & Reitsma, J. B. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001531 · Cited by 158 · see citing works

Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic studyPreprint

Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J. A. W. M., van de Kaa, C. A. H. ‐., & Litjens, G. J. S. (2020) · arXiv (Cornell University) · Cited by 106 · see citing works

Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading

Wulczyn, E., Nagpal, K., Symonds, M., Moran, M., Plass, M., Reihs, R., Nader, F., Tan, F., Cai, Y., Brown, T., et al. (2021) · Communications Medicine · doi:10.1038/s43856-021-00005-3 · Cited by 51 · see citing works

Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects

Koçak, B., Ponsiglione, A., Stanzione, A., Bluethgen, C., Santinha, J., Ugga, L., Huisman, M., Klontzas, M. E., Cannella, R., & Cuocolo, R. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242854 · Cited by 168 · see citing works

Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making

Wysocki, O., Davies, J. K., Vigo, M., Armstrong, A. C., Landers, D., Lee, R., & Freitas, A. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2022.103839 · Cited by 161 · see citing works

APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support

Kwong, J. C. C., Khondker, A., Lajkosz, K., McDermott, M. B. A., Borrat, X., McCradden, M. D., Mamdani, M., Kulkarni, G. S., & Johnson, A. E. W. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.35377 · Cited by 151 · see citing works

Deep learning: A primer for dentists and dental researchers

Mohammad‐Rahimi, H., Rokhshad, R., Bencharit, S., Krois, J., & Schwendicke, F. (2023) · Journal of Dentistry · doi:10.1016/j.jdent.2023.104430 · Cited by 97 · see citing works

Deep learning models for histologic grading of breast cancer and association with disease prognosis

Jaroensri, R., Wulczyn, E., Hegde, N., Brown, T., Flament-Auvigne, I., Tan, F. E., Cai, Y., Nagpal, K., Rakha, E. A., Dabbs, D. J., et al. (2022) · npj Breast Cancer · doi:10.1038/s41523-022-00478-y · Cited by 84 · see citing works

Artificial Intelligence Enables Quantitative Assessment of Ulcerative Colitis Histology

Najdawi, F., Sucipto, K., Mistry, P., Hennek, S. R., Jayson, C., Lin, M., Fahy, D., Kinsey, S., Wapinski, I. N., Beck, A. H., et al. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100124 · Cited by 68 · see citing works

Artificial intelligence in endodontics: Data preparation, clinical applications, ethical considerations, limitations, and future directions

Mohammad‐Rahimi, H., Sohrabniya, F., Ourang, S. A., Dianat, O., Aminoshariae, A., Nagendrababu, V., Dummer, P. M. H., Duncan, H. F., & Nosrat, A. (2024) · International Endodontic Journal · doi:10.1111/iej.14128 · Cited by 70 · see citing works

Effect of Human-AI Interaction on Detection of Malignant Lung Nodules on Chest Radiographs

Lee, J. H., Hong, H., Nam, G., Hwang, E. J., & Park, C. M. (2023) · Radiology · doi:10.1148/radiol.222976 · Cited by 52 · see citing works

An interactive dashboard to track themes, development maturity, and global equity in clinical artificial intelligence research

Zhang, J., Whebell, S. F., Gallifant, J., Budhdeo, S., Mattie, H., Lertvittayakumjorn, P., Lopez, M. D. P. A., Tiangco, B. J., Gichoya, J. W., Ashrafian, H., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00032-2 · Cited by 52 · see citing works

Is Artificial Intelligence Replacing Our Radiology Stars? Not Yet!

Cacciamani, G. E., Sanford, D. I., Chu, T. N., Kaneko, M., Abreu, A. L., Duddalwar, V. A., & Gill, I. S. (2022) · European Urology Open Science · doi:10.1016/j.euros.2022.09.024 · Cited by 43 · see citing works

Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and Researchers

Montezuma, D., Oliveira, S. P., Neto, P. C., Oliveira, D., Monteiro, A., Cardoso, J. S., & Macedo-Pinto, I. (2023) · Modern Pathology · doi:10.1016/j.modpat.2022.100086 · Cited by 40 · see citing works

Artificial Intelligence in Infection Management in the ICU

Corte, T. D., Hoecke, S. V., & Waele, J. J. D. (2022) · Critical Care · doi:10.1186/s13054-022-03916-2 · Cited by 38 · see citing works

A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America

Ong, A. Y., Taribagil, P., Sevgi, M., Kale, A. U., Dow, E. R., Macdonald, T., Kras, A., Maniatopoulos, G., Liu, X., Keane, P. A., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01726-8 · Cited by 39 · see citing works

Artificial Intelligence for Detection of External Cervical Resorption Using Label-Efficient Self-Supervised Learning Method

Mohammad‐Rahimi, H., Dianat, O., Abbasi, R., Zahedrozegar, S., Ashkan, A., Motamedian, S. R., Rohban, M. H., & Nosrat, A. (2023) · Journal of Endodontics · doi:10.1016/j.joen.2023.11.004 · Cited by 38 · see citing works

Detecting acute respiratory diseases in the pediatric population using cough sound features and machine learning: A systematic review

Sharan, R. V., & Rahimi‐Ardabili, H. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105093 · Cited by 32 · see citing works

Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study

Wagner, S. K., Liefers, B. J., Radia, M., Zhang, G., Struyven, R., Faes, L., Than, J., Balal, S., Hennings, C., Kilduff, C. L. S., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00050-x · Cited by 29 · see citing works

Accuracy and safety of an autonomous artificial intelligence clinical assistant conducting telemedicine follow-up assessment for cataract surgery

Meinert, E., Milne‐Ives, M., Lim, E., Higham, A., Boege, S., de Pennington, N., Bajre, M., Mole, G., Normando, E. M., & Xue, K. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102692 · Cited by 27 · see citing works

A checklist for reporting, reading and evaluating Artificial Intelligence Technology Enhanced Learning (AITEL) research in medical education

Masters, K., & Salcedo, D. (2024) · Medical Teacher · doi:10.1080/0142159x.2023.2298756 · Cited by 26 · see citing works

Automated Cough Sound Analysis for Detecting Childhood Pneumonia

Sharan, R. V., Qian, K., & Yamamoto, Y. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3327292 · Cited by 25 · see citing works

Managing a patient with uveitis in the era of artificial intelligence: Current approaches, emerging trends, and future perspectives

Rojas-Carabali, W. A., Cifuentes‐González, C., Gutiérrez-Sinisterra, L., Heng, L. Y., Tsui, E. Y. K., Gangaputra, S., Sadda, S. R., Nguyen, Q. D., Kempen, J. H., Pavesio, C. E., et al. (2024) · Asia-Pacific Journal of Ophthalmology · doi:10.1016/j.apjo.2024.100082 · Cited by 21 · see citing works

Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities

González, R., Saha, A., Campbell, C. J. V., Nejat, P., Lokker, C., & Norgan, A. P. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100347 · Cited by 21 · see citing works

Use of artificial intelligence in forecasting glaucoma progression

Thakur, S., Dinh, L. L., Lavanya, R., Quek, T. C., Liu, Y., & Cheng, C. (2023) · Taiwan Journal of Ophthalmology · doi:10.4103/tjo.tjo-d-23-00022 · Cited by 14 · see citing works

Effects of Expert-Determined Reference Standards in Evaluating the Diagnostic Performance of a Deep Learning Model: A Malignant Lung Nodule Detection Task on Chest Radiographs

Huh, J. E., Lee, J. H., Hwang, E. J., & Park, C. M. (2023) · Korean Journal of Radiology · doi:10.3348/kjr.2022.0548 · Cited by 13 · see citing works

Annotation Practices in Computational Pathology: A European Society of Digital and Integrative Pathology (ESDIP) Survey Study

Montezuma, D., Oliveira, S. P., Tolkach, Y., Boor, P., Haragan, A., Carvalho, R., Mea, V. D., Kiehl, T., Leh, S. M., Yousif, M. Q., et al. (2024) · Laboratory Investigation · doi:10.1016/j.labinv.2024.102203 · Cited by 12 · see citing works

Comparison of Pathologist and Artificial Intelligence–based Grading for Prediction of Metastatic Outcomes After Radical Prostatectomy

Oliveira, L. D., Lu, J., Erak, E., Mendes, A. A. D. A., Dairo, O., Ertunç, O., Kulaç, İ., Valle, J. A. B.-D., Jones, T., Hicks, J. L., et al. (2024) · European Urology Oncology · doi:10.1016/j.euo.2024.08.004 · Cited by 12 · see citing works

Diagnostic accuracy of code-free deep learning for detection and evaluation of posterior capsule opacification

Huemer, J. C., Kronschläger, M., Ruiß, M., Sim, D. A., Keane, P. A., Findl, O., & Wagner, S. K. (2022) · BMJ Open Ophthalmology · doi:10.1136/bmjophth-2022-000992 · Cited by 11 · see citing works

Advancements in computer vision and pathology: Unraveling the potential of artificial intelligence for precision diagnosis and beyond

Chang, J., & Hatfield, B. S. (2024) · Advances in cancer research · doi:10.1016/bs.acr.2024.05.006 · Cited by 11 · see citing works

Predicting intraoperative blood loss risk in severe lumbar disc herniation patients undergoing PLIF: a multicenter cohort study using ensemble learning

Shen, N., Zhang, Y., Ding, Z., Li, R., Quan, X., Liu, X., Zhang, Y., Xiang, T., Zhang, Y., Yin, C., et al. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000002730 · Cited by 12 · see citing works

The Enemies of Reliable and Useful Clinical Prediction Models: A Review of Statistical and Scientific Challenges

CALSTER, B. V., van Smeden, M., van Amsterdam, W. A. C., Coemans, M., Wynants, L., & Steyerberg, E. W. (2025) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-042324-123749 · Cited by 12 · see citing works

Deep learning methods for diagnosis of graves’ ophthalmopathy using magnetic resonance imaging

Ma, Z.-C., Lin, J., Li, S.-K., Liu, H.-J., & Zhang, Y. (2024) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-24-80 · Cited by 9 · see citing works

A first explainable-AI-based workflow integrating forward-forward and backpropagation-trained networks of label-free multiphoton microscopy images to assess human biopsies of rare neuromuscular disease

Scodellaro, R., Zschüntzsch, J., Hell, A. K., & Alves, F. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108733 · Cited by 9 · see citing works

Unveiling variability: A systematic review of reproducibility in visual EEG analysis, with focus on seizures

Aanestad, E., Beniczky, S., Olberg, H. K., & Brøgger, J. C. (2024) · Epileptic Disorders · doi:10.1002/epd2.20291 · Cited by 8 · see citing works

Development and external validation of a transfer learning-based system for the pathological diagnosis of colorectal cancer: a large emulated prospective study

Yuan, L., Zhou, H., Xiao, X., Zhang, X., Chen, F., Liu, L., Liu, J., Bao, S., & Tao, K. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1365364 · Cited by 6 · see citing works

Cochrane corner: artificial intelligence for diagnosing exudative age-related macular degeneration

Ong, A. Y., Hogg, H. D. J., & Keane, P. A. (2025) · Eye · doi:10.1038/s41433-025-03599-3 · Cited by 6 · see citing works

Reference standard methodology in the clinical evaluation of AI chest X-ray algorithms for lung cancer detection: A systematic review

Duncan, S. F., Kidd, A. C., Perdomo-Lampignano, J. A., Cannon, P., Hall, M., Stobo, D., Maclay, J. D., Blyth, K. G., & Lowe, D. J. (2025) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.112409 · Cited by 7 · see citing works

Clinical ground truth in machine learning for early sepsis diagnosis

Lindner, H. A., Thiel, M., & Schneider‐Lindner, V. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00070-5 · Cited by 5 · see citing works

A machine learning algorithm improves the diagnostic accuracy of the histologic component of antibody mediated rejection (AMR-H) in cardiac transplant endomyocardial biopsies

Glass, M., Ji, Z., Davis, R., Pavlisko, E. N., DiBernardo, L. R., Carney, J., Fishbein, G. A., Luthringer, D., Miller, D. V., Mitchell, R. N., et al. (2024) · Cardiovascular Pathology · doi:10.1016/j.carpath.2024.107646 · Cited by 4 · see citing works

Wet and dry cough classification using cough sound characteristics and machine learning: A systematic review

Sharan, R. V., & Xiong, H. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105912 · Cited by 4 · see citing works

Evaluating a clinically available artificial intelligence model for intracranial aneurysm detection: a multi-reader study and algorithmic audit

Hu, B., He, H., Shi, Z., Wang, L., Liu, Q., Sun, Z., & Zhang, L. J. (2025) · Neuroradiology · doi:10.1007/s00234-024-03536-3 · Cited by 4 · see citing works

Conformity towards humans versus AI in different task domains: the type of task matters

Thorp, S. O., Slupphaug, K. S., Rimol, L. M., Lervik, S., Hoel, S. K. B., & Grassini, S. (2025) · Journal of Psychology and AI · doi:10.1080/29974100.2025.2540762 · Cited by 3 · see citing works

Digital pathology‐based artificial intelligence algorithms in prostate cancer: inside the ‘black box’

de la Calle, C. M., Baras, A. S., & Lotan, T. L. (2026) · British Journal of Urology · doi:10.1111/bju.70164 · Cited by 3 · see citing works

Medical multimodal large language models: A survey

Xiao, H., Hui, N., Xu, Y., Li, Z., & Peng, J. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104386 · Cited by 2 · see citing works

Artificial Intelligence in Infection Management in the ICU

Corte, T. D., Hoecke, S. V., & Waele, J. J. D. (2022) · Annual update in intensive care and emergency medicine · doi:10.1007/978-3-030-93433-0_28 · Cited by 2 · see citing works

TRIAGE: Trustworthy Reporting and Assessment for Clinical Gain and Effectiveness of AI Models

Fazilati, F., Rajabi, M. T., Alihosseini, N., Farsani, M. E., Sandid, S. H., Zamani, S., Farahani, M. A., Biriaei, F., Sadeghipour, F., Mirshamsi, M. T., et al. (2026) · Diagnostics · doi:10.3390/diagnostics16050666 · Cited by 5 · see citing works

Cross-institutional validation of a polar map-free 3D deep learning model for obstructive coronary artery disease prediction using myocardial perfusion imaging: insights into generalizability and bias

Shih, Y.-C., Ko, C., Wang, S.-Y., Chang, C.-Y., Lin, S.-S., Huang, C.-W., Cheng, M., Chen, C., & Wu, Y. (2025) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-025-07243-w · Cited by 1 · see citing works

Exploring reference standards for the measurement of respiratory rate in children under 5 years of age: a scoping review

Tzirita, S., King, C., McCollum, E. D., Baker, K. S., López, M. B., & Schedwin, M. (2026) · BMJ Paediatrics Open · doi:10.1136/bmjpo-2025-003506 · Cited by 1 · see citing works

Artificial intelligence for screening drug resistance in tuberculosis

Mohanvel, S. K., Radhakrishnan, R., Balraj, P., Singh, N., Danisetty, S., Vats, H., Chaudhury, A., Choudhary, H., Rajendran, P., Madhumathi, J., et al. (2026) · The Indian Journal of Medical Research · doi:10.25259/ijmr_1546_2025 · Cited by 1 · see citing works

AI‐Assisted Fungal Detection in Dermatopathology: A Study on Accuracy, Efficiency, and Usability

Schmidle, P., Lang, T., Springenberg, S., Calvopina, D. A., Köhler, T., Getova, V., Gebing, A.-M., Hetzer, S., Pierrone, J., Aebischer, V., et al. (2026) · JDDG Journal der Deutschen Dermatologischen Gesellschaft · doi:10.1111/ddg.70242 · Cited by 1 · see citing works

Uveal Melanoma Ground Truth Labeling in Machine Learning

Kao, E., Ganesh, S., Chadwick, W. F., Alahmadi, R., Yao, X., & Heiferman, M. J. (2026) · Cancers · doi:10.3390/cancers18091357 · Cited by 1 · see citing works

Data annotation and its evaluation in artificial intelligence-based anatomy recognition for ultrasound-guided regional anesthesia: a clinical perspective

Delvaux, B. V., Elmaleh, Y. D., Chuan, A., Sia, A. T., GUPTA, R. K., Schroeder, K. M., Guessous, K., & Bowness, J. S. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1823745 · Cited by 1 · see citing works

Artificial intelligence for dental caries diagnosis: translating algorithms to clinical practice

Wang, Z., Zhu, Z., Jiang, T., & Gu, Y. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1879708 · Cited by 1 · see citing works

Managing the Infodemic: Leveraging Deep Learning to Evaluate the Maturity Level of AI-Based COVID-19 Publications for Knowledge Surveillance and Decision SupportPreprint

Awasthi, R., Nagori, A., Mishra, S. V., Mathur, A., Mathur, P., & Nasri, B. (2023) · medRxiv · doi:10.1101/2023.06.20.23291653 · Cited by 1 · see citing works

Navigating the Frontier of artificial intelligence implementation in radiology – part 1: Performance assessment

Farhat, M., Dagher, S. A., Ozkara, B. B., Ozcan, B. B., Karabacak, M., Chen, M., Yedavalli, V. S., & Wintermark, M. (2025) · The Neuroradiology Journal · doi:10.1177/19714009251407175

The application of Artificial Intelligence in the ultrasound-based diagnosis of liver disease: current status, hurdles, and prospects

Hashim, A., Giamperoli, A., Indre, M. G., & Piscaglia, F. (2026) · Ultraschall in der Medizin - European Journal of Ultrasound · doi:10.1055/a-2809-9211

Artificial Intelligence (Pattern Recognition) in Musculoskeletal Imaging: The Future or Hype?

Tordjman, M., Regnard, N.-E., Bouvier, F. M., Rizk, B., & Guermazi, A. (2026) · Seminars in Musculoskeletal Radiology · doi:10.1055/a-2818-3827

A call for methodological guidance to support initiatives to develop diagnostic criteria for conditions without reference standard diagnostic tests

White, S. J., Barker, T. H., Merlin, T., & Munn, Z. (2026) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2026.112333

Methodological Quality and Clinical Translation of Deep Learning in Traditional Chinese Medicine Disease Diagnosis: A Systematic Review and Validation Gap Analysis

Lu, L., Li, H., Liu, Y.-M., Du, H.-X., Qin, J., Yeung, W.-F., Zhong, C. C., Xiong, L., & Chen, S.-C. (2026) · Information · doi:10.3390/info17060554

Artificial Intelligence in Orthodontics: Part 2—Data Preparation and Performance Evaluation

Naveed, K., Sohrabniya, F., Mohammad‐Rahimi, H., Woolley, J., Pauwels, R., & Stoustrup, P. B. (2026) · Orthodontics and Craniofacial Research · doi:10.1111/ocr.70171

Clinical scale of artificial intelligence-based nerve segmentation on ultrasound: a pilot validation study

Delvaux, B. V., Elmaleh, Y. D., Guessous, K., Garnier, T., Morel, B., Yao, K., Giral, T., Bowness, J. S., & Herbelet, S. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1879747

Modular multi-task deep learning framework for prediction of treatment initiation in neovascular age-related macular degeneration modular AI for NVAMD treatment initiation study-MANTIS

Gao, Q., Kuo, D. C., Amason, J., Lee, T., Rathinavelu, J. K., Pajić, M., & Hadziahmetovic, M. (2026) · British Journal of Ophthalmology · doi:10.1136/bjo-2025-328327

Artificial Intelligence in Healthcare Systems: Evolution of Clinical Analytics Architectures and Governance Structures

Kumar, R., Sharma, N., Deshmukh, A., Nair, A., & Pillai, M. (2022) · Journal of Artificial Intelligence for Healthcare Systems · doi:10.68159/l244649356

On the Behavior of Calibration Error Under Rater Disagreement

Kumar, H., Vu, Y. N. T., Su, J., & Matthews, T. P. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38470-6_21

Automatic Posterior Capsule Opacification detection with a custom deep learning model

Lisy, M., Peer, L., Schartmüller, D., Leydolt, C., & Saffer, Z. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100503

The benchmark illusion: on the curious underestimation of AI and the uncertainty of its creators’ gold standards

Pohlkamp, C., & Haferlach, T. (2026) · EClinicalMedicine · doi:10.1016/j.eclinm.2026.104184

Reference-standard agreement versus clinical appropriateness: rethinking large language model evaluation in infectious diseases and clinical microbiology

Pato, I., Carrasco, J., Grobusch, M. P., & Hänscheid, T. (2026) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2026.07.054

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define PerformancePreprint

Fytas, P., Selby, I., Karner, C., Babar, J. L., Baker, S., Beckford, J., Sadler, T. J., Shahipasand, S., Thavakumar, A., Chen, J. L., et al. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2607.26333

The Use of Artificial Intelligence in Diagnostic Medical Imaging: Systematic Literature Review

Hafizovic, L., Čaušević, A., Deumić, A., Spahić, L., Pokvić, L. G., & Badnjević, A. (2021) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe52308.2021.9635307 · Cited by 43 · see citing works

Artificial Intelligence for Diagnosis in Medical Imaging and Pathology: A Systematic Review of Reported Discriminative Performance with an Exploratory Diagnostic Test Accuracy Meta-Analysis

Maltsev, D., Fedoryshyn, H., Мисловська, С., Якименко, В., & Александрук, О. (2026) · Journal of Pioneering Medical Science · doi:10.47310/jpms2026150938