Big data and black-box medical algorithms

Dermatologist-level classification of skin cancer with deep neural networks

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362

Predicting the future - big data, machine learning, and clinical medicine

Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652

How the machine “thinks”: Understanding opacity in machine learning algorithms

Burrell, J. (2016) · Big Data & Society · doi:10.1177/2053951715622512 · Cited by 3,196 · cited 1×

External validity of randomised controlled trials: “To whom do the results of this trial apply?”

Rothwell, P. M. (2005) · The Lancet · doi:10.1016/s0140-6736(04)17670-8 · Cited by 2,544

Real-World Evidence — What Is It and What Can It Tell Us?

Sherman, R. E., Anderson, S. A., Pan, G. J. D., Gray, G. W., Gross, T. P., Hunter, N. L., LaVange, L. M., Marinac‐Dabic, D., Marks, P. W., Robb, M. A., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmsb1609216 · Cited by 2,192

New FDA Breakthrough-Drug Category — Implications for Patients

Darrow, J. J., Avorn, J., & Kesselheim, A. S. (2014) · New England Journal of Medicine · doi:10.1056/nejmhle1311493 · Cited by 119

Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients

Liu, N. T., Holcomb, J. B., Wade, C. E., Batchinsky, A. I., Cancio, L. C., Darrah, M. I., & Salinas, J. (2013) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-013-1130-x · Cited by 67

Regulating Black-Box Medicine

Price, W. (2017) · Michigan Law Review · doi:10.36644/mlr.116.3.regulating · Cited by 64

Next generation phenotyping in Emanuel and Pallister‐Killian syndrome using computer‐aided facial dysmorphology analysis of 2D photos

Liehr, T., Acquarola, N., Pyle, K., St‐Pierre, S., Rinholm, M., Bar, O., Wilhelm, K., & Schreyer, I. (2017) · Clinical Genetics · doi:10.1111/cge.13087 · Cited by 48

Black-Box Medicine

Price, & Nicholson, W. T. (2015) · Harvard journal of law & technology · Cited by 25

Theories of Change in Reality

Koleros, A., Adrien, M.-H., & Tyrrell, T. (2024) · Routledge eBooks · doi:10.4324/9781032669618 · Cited by 17

Artificial Intelligence in Cancer Care: Legal and Regulatory Dimensions

Gowda, V., Kwaramba, T., Hanemann, C. W., Garcia, J. A., & Barata, P. C. (2021) · The Oncologist · doi:10.1002/onco.13862 · Cited by 13

Fast-developing machine learning support complex system research in environmental chemistry

Duan, Q., & Lee, J. (2019) · New Journal of Chemistry · doi:10.1039/c9nj05717j · Cited by 13

La inteligencia artificial en la educación médica y la predicción en salud

Joison, A. N., Barcudi, R. J., Majul, E. A., Ruffino, S., J, D. M. R. J., Joison, A. M., & Baiardi, G. (2021) · Methodo Investigación Aplicada a las Ciencias Biológicas · doi:10.22529/me.2021.6(1)07 · Cited by 13

Automated classification of total knee replacement prosthesis on plain film radiograph using a deep convolutional neural network

Belete, S. C., Batta, V., & Kunz, H. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100669 · Cited by 13

“Just the Facts Ma’am”: Moral and Ethical Considerations for Artificial Intelligence in Medicine and its Potential to Impact Patient Autonomy and Hope

Love, C. S. (2023) · The Linacre Quarterly · doi:10.1177/00243639231162431 · Cited by 15

Artificial Intelligence Reporting Guidelines’ Adherence in Nephrology for Improved Research and Clinical Outcomes

Salybekov, A. A., Wolfien, M., Hahn, W., Hidaka, S., & Kobayashi, S. (2024) · Biomedicines · doi:10.3390/biomedicines12030606 · Cited by 16

Application of artificial intelligence to pancreatic adenocarcinoma

Chen, X., Fu, R., Shao, Q., Chen, Y., Ye, Q., Li, S., He, X., & Zhu, J. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.960056 · Cited by 11

Predicting periprosthetic joint infection in primary total knee arthroplasty: a machine learning model integrating preoperative and perioperative risk factors

Chong, Y. Y., Lau, L. C., Jiang, T., Wen, C., Zhang, J., Cheung, A., Luk, M. H., Leung, K. C. T., Cheung, M. H., Fu, H., et al. (2025) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-025-08296-6 · Cited by 11

Artificial Intelligence: Has Its Time Come for Inclusion in Medical School Education? Maybe…Maybe Not [Version 2]

Ngo, B., Nguyen, D., & vanSonnenberg, E. (2021) · MedEdPublish · doi:10.15694/mep.2021.000131.2 · Cited by 12

Improving risk prediction for target subpopulations: Predicting suicidal behaviors among multiple sclerosis patients

Barak‐Corren, Y., Castro, V. M., Javitt, S., Nock, M. K., Smoller, J. W., & Reis, B. Y. (2023) · PLoS ONE · doi:10.1371/journal.pone.0277483 · Cited by 13

Considerations for the application of artificial intelligence in vascular surgical education

Rigberg, D. A., & Jim, J. (2023) · Seminars in Vascular Surgery · doi:10.1053/j.semvascsurg.2023.07.004 · Cited by 12

From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care

Griffin, A. C., Topaloĝlu, Ü., Davis, S. R., & Chung, A. E. (2020) · Yearbook of Medical Informatics · doi:10.1055/s-0040-1701983 · Cited by 13

Development and Validation of a Natural Language Processing Model to Identify Low-Risk Pulmonary Embolism in Real Time to Facilitate Safe Outpatient Management

Amin, K. D., Weissler, E. H., Ratliff, W., Sullivan, A. E., Holder, T. A., Bury, C., Francis, S., Theiling, B. J., Hintze, B. J., Gao, M. C., et al. (2024) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2024.01.036 · Cited by 10

The prediction of cancer-specific mortality in T1 non-muscle-invasive bladder cancer: comparison of logistic regression and artificial neural network: a SEER population-based study

Ślusarczyk, A., Zapała, P., Olszewska-Ślusarczyk, Z., & Radziszewski, P. (2023) · International Urology and Nephrology · doi:10.1007/s11255-023-03655-5 · Cited by 12

Triage 4.0: On Death Algorithms and Technological Selection. Is Today’s Data- Driven Medical System Still Compatible with the Constitution?

Helbing, D., Beschorner, T., Frey, B. S., Diekmann, A., Hagendorff, T., Seele, P., Spiekermann, S., van den Hoven, J., & Zwitter, A. (2021) · Journal of European CME · doi:10.1080/21614083.2021.1989243 · Cited by 10

Conflicting roles for humans in learning health systems and AI‐enabled healthcare

Kasperbauer, T. J. (2020) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.13510 · Cited by 8

Promise and Frustration: Machine Learning in Cardiology

Fornwalt, B. K., & Pfeifer, J. M. (2021) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.121.012838 · Cited by 9

Letter: Machine Learning and Artificial Intelligence in Neurosurgery: Status, Prospects, and Challenges

Lim, M. J. R. (2021) · Neurosurgery · doi:10.1093/neuros/nyab337 · Cited by 9

Interpretable machine-learning model for Predicting the Convalescent COVID-19 patients with pulmonary diffusing capacity impairment

Ma, F.-Q., He, C., Yang, H.-R., Hu, Z., Mao, H.-R., Fan, C.-Y., Qi, Y., Zhang, J., & Xu, B. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02192-6 · Cited by 8

Overcome Medical Algorithm Aversion: Conditional Joint Effect of Direct and Indirect Information

Zhao, Y., & Xiao, C. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2344146 · Cited by 10

Rule ensemble method with adaptive group lasso for heterogeneous treatment effect estimation

Wan, K., Tanioka, K., & Shimokawa, T. (2023) · Statistics in Medicine · doi:10.1002/sim.9812 · Cited by 8

The performance of VCS(volume, conductivity, light scatter) parameters in distinguishing latent tuberculosis and active tuberculosis by using machine learning algorithm

Chen, L., Yuan, L., Sun, T., Liu, R., Huang, Q. Y., & Deng, S. (2023) · BMC Infectious Diseases · doi:10.1186/s12879-023-08531-2 · Cited by 8

The Black Box of Technological Outcome Measures: An Example in Duchenne Muscular Dystrophy

Naarding, K. J., Janssen, M. M. H. P., Boon, R. D., Bank, P. J. M., Matthew, R. P., Kurillo, G., Han, J. J., Verschuuren, J. J. G. M., de Groot, I. J. M., van der Holst, M., et al. (2022) · Journal of Neuromuscular Diseases · doi:10.3233/jnd-210767 · Cited by 8

Microvascular invasion risk scores affect the estimation of early recurrence after resection in patients with hepatocellular carcinoma: a retrospective study

Wang, S., Zheng, W., Zhang, Z., Zhang, G., & Huang, D. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00940-0 · Cited by 8

Deep learning based on multiparametric MRI predicts early recurrence in hepatocellular carcinoma patients with solitary tumors ≤5 cm

Mu, T., Zheng, X., Song, D., Chen, J., Yue, X., Wang, W., & Rao, S. (2024) · European Journal of Radiology Open · doi:10.1016/j.ejro.2024.100610 · Cited by 7

Developing and Applying the BE-FAIR Equity Framework to a Population Health Predictive Model: A Retrospective Observational Cohort Study

Gupta, R., Sasaki, M., Taylor, S. L., Fan, S., Hoch, J. S., Zhang, Y., Crase, M., Tancredi, D. J., Adams, J. Y., & Ton, H. (2025) · Journal of General Internal Medicine · doi:10.1007/s11606-025-09462-1 · Cited by 13

Applications of machine learning and deep learning in musculoskeletal medicine: a narrative review

Feierabend, M., Wolfgart, J. M., Praster, M., Danalache, M., Migliorini, F., & Hofmann, U. K. (2025) · European journal of medical research · doi:10.1186/s40001-025-02511-9 · Cited by 12

Robust prediction of nonhome discharge following elective anterior cervical discectomy and fusion using explainable machine learning

Geng, E. A., Gal, J. S., Kim, J., Martini, M. L., Markowitz, J. S., Neifert, S. N., Tang, J. E., Shah, K. C., White, C. A., Dominy, C. L., et al. (2023) · European Spine Journal · doi:10.1007/s00586-023-07621-8 · Cited by 7

Sex as a Biological Variable in Early-Phase Oncology Clinical Trials: Enhancing the Path to Personalised Medicine

Sutherland, L., & Carter, L. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e32597 · Cited by 8

Development of Explainable Machine Learning Models to Identify Patients at Risk for 1-Year Mortality and New Distant Metastases Postendoprosthetic Reconstruction for Lower Extremity Bone Tumors

Deng, J., Moskalyk, M., Nayan, M., Aoude, A., Ghert, M. A., Bhatnagar, S., & Bozzo, A. (2025) · JBJS Open Access · doi:10.2106/jbjs.oa.24.00213 · Cited by 6

Preoperative prediction of cytokeratin-19 expression for hepatocellular carcinoma using T1 mapping on gadoxetic acid-enhanced MRI combined with diffusion-weighted imaging and clinical indicators

Zhao, Y., Tan, X., Chen, J., Tan, H., Huang, H., Luo, P., Liang, Y., & Jiang, X. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2022.1068231 · Cited by 6

ADVANCEMENTS IN BILIOPANCREATIC ENDOSCOPY: A COMPREHENSIVE REVIEW OF ARTIFICIAL INTELLIGENCE IN EUS AND ERCP.

Agudo, B., Mascarenhas, M., Martins, M., Mendes, F., de la Iglesia, D., da Costa, A. M. P., Fernández-Zarza, C. E., & González-Haba, M. (2024) · Revista Española de Enfermedades Digestivas · doi:10.17235/reed.2024.10456/2024 · Cited by 8

Analysis of the medical residency matching algorithm to validate and improve equity

Mascarenhas, B., & Puranam, K. S. (2023) · PLoS ONE · doi:10.1371/journal.pone.0284153 · Cited by 6

Expert Knowledge–Guided Bayesian Belief Networks for Predicting Bridge Pile Capacity

Assaad, R. H., Hu, X., & Hussein, M. (2023) · Journal of Bridge Engineering · doi:10.1061/jbenf2.beeng-6096 · Cited by 6

Introducing Attribute Association Graphs to Facilitate Medical Data Exploration: Development and Evaluation Using Epidemiological Study Data

Bellmann, L., Wiederhold, A. J., Trübe, L., Twerenbold, R., Ückert, F., & Gottfried, K. (2024) · JMIR Medical Informatics · doi:10.2196/49865 · Cited by 6

Discrimination analysis of intelligent voice assistants

Lima, L., Furtado, V., Furtado, E., Almeida, V. A. F., & da Silva, T. H. O. (2019) · Simpósio Brasileiro de Fatores Humanos em Sistemas Computacionais · doi:10.1145/3357155.3358483 · Cited by 6

A Generic Neural Network Approach to Infer Segmenting Classifiers for Disease-Associated Regions in Medical ImagesPreprint

Schuhmacher, D., Gerwert, K. B., & Mosig, A. (2020) · medRxiv · doi:10.1101/2020.02.27.20028845 · Cited by 6

Explainable machine learning model for predicting the transarterial chemoembolization response and subtypes of hepatocellular carcinoma patients

Zhang, Y., Tong, S., Yang, J., Lin, J., Kong, Y., Lu, D., Chen, Y., Li, Y., Xu, L., Kong, X., et al. (2025) · BMC Gastroenterology · doi:10.1186/s12876-025-04105-5 · Cited by 5

Development and validation of a machine learning model for predicting venous thromboembolism complications following colorectal cancer surgery

Sun, Z., Hao, D., Yang, M., Wu, W., Jing, H., Yang, Z., Sun, A., Xie, W., Zheng, L., Wang, X., et al. (2025) · Visual Computing for Industry Biomedicine and Art · doi:10.1186/s42492-025-00204-y · Cited by 5

Personalized Treatment of Patients with Coronary Artery Disease: The Value and Limitations of Predictive Models

Greco, A., & Capodanno, D. (2025) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd12090344 · Cited by 6

Flexibility of a large blindly synthetized avatar database for occupational research: Example from the CONSTANCES cohort for stroke and knee pain

Fadel, M., Petot, J., Gourraud, P., & Descatha, A. (2024) · PLoS ONE · doi:10.1371/journal.pone.0308063 · Cited by 5

Towards Effective Patient Simulators

Liventsev, V., Härmä, A., & Petković, M. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.798659 · Cited by 7

Survival causal rule ensemble method considering the main effect for estimating heterogeneous treatment effects

Wan, K., Tanioka, K., & Shimokawa, T. (2024) · Statistics in Medicine · doi:10.1002/sim.10180 · Cited by 5

Image learning to accurately identify complex mixture components

Duan, Q., Lee, J., Chen, J., Feng, Y., Luo, R., Wang, C., Bi, S., Liu, F., Wang, W., Huang, Y., et al. (2021) · The Analyst · doi:10.1039/d1an01288f · Cited by 5

AI-driven ECG diagnostics: A game-changer for hypertrophic cardiomyopathy. A systematic review and diagnostic test accuracy meta-analysis

Łajczak, P. M., Righetto, B. B., Obi, O. S., Wijaya, P., Sahin, O. K., Eltawansy, S., Salha, I., Łajczak, A., & Schincariol, M. (2025) · Heart & Lung · doi:10.1016/j.hrtlng.2025.09.012 · Cited by 5

Identification of key factors and explainability analysis for surgical decision-making in hepatic alveolar echinococcosis assisted by machine learning

Zhu, D., Tulahong, A., Liu, C., Aierken, A., Tan, W., Ruze, R., Yuan, Z., Yin, L., Jiang, T., Lin, R., et al. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i37.111038 · Cited by 4

Multi-modal Artificial Intelligence of Embryo Grading and Pregnancy Prediction in Assisted Reproductive Technology: A Review

Ouyang, X., & Wei, J. (2025) · Annals of Biomedical Engineering · doi:10.1007/s10439-025-03865-1 · Cited by 16

A Paradoxical Role for Somatic Chromosomal Mosaicism and Chromosome Instability in Cancer: Theoretical and Technological Aspects

Iourov, I. Y., Vorsanova, S. G., & Yurov, Y. B. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3946-7_3 · Cited by 4

Artificial intelligence interpretation of touch print smear cytology of testicular specimen from patients with azoospermia

Hsu, C.-H., Yeh, C.-F., Huang, I.-S., Chen, W.-J., Peng, Y., Tsai, C.-H., Ko, M.-C., Su, C.-P., Chen, H.-C., Wu, W., et al. (2024) · Journal of Assisted Reproduction and Genetics · doi:10.1007/s10815-024-03215-5 · Cited by 4

Regulating Generative AI in Radiology Practice: A Trilaminar Approach to Balancing Risk with Innovation

Gowda, V., Bizzo, B. C., & Dreyer, K. J. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.05.032 · Cited by 4

The Need for Greater Transparency in Journal Submissions That Report Novel Machine Learning Models in Health Professions Education

Patiño, G. A., & Roberts, L. W. (2024) · Academic Medicine · doi:10.1097/acm.0000000000005793 · Cited by 4

A survey on privacy of health data lifecycle: a taxonomy, review, and future directions

Bose, S., & Marijan, D. (2026) · International Journal of Information Security · doi:10.1007/s10207-025-01169-y · Cited by 7

A Comparison Between Wavelet Scattering Transform and Transfer Learning for Elevated Blood Pressure Detection

Martinez-Ríos, E. A., Montesinos, L., & Alfaro-Ponce, M. (2022) · Biomedical Engineering International Conference · doi:10.1109/bmeicon56653.2022.10012088 · Cited by 4

Morphometric differences between basal cell carcinomas & melanomas of the head & neck versus other sites and their influence on neural networks

Hue, J., Ekanayake, J., Dehmeshki, J., & Dhanda, J. (2024) · EJC Skin Cancer · doi:10.1016/j.ejcskn.2024.100024 · Cited by 4

Machine Learning to Decode the Electroencephalography for Post Cardiac Arrest Neuroprognostication*

Stevens, R. D. (2019) · Critical Care Medicine · doi:10.1097/ccm.0000000000003932 · Cited by 4

The use of machine learning based models to predict the severity of community acquired pneumonia in hospitalised patients: A systematic review

Lythgoe, C., Hamilton, D. O., Johnston, B. W., Ortega‐Martorell, S., Olier, I., & Welters, I. D. (2025) · Journal of the Intensive Care Society · doi:10.1177/17511437251315319 · Cited by 6

Work-Life Conditions as the Primary Determinant of Seafarer Mental Health: An Explainable Machine Learning Analysis

Jiang, Y., Tang, Z., Liu, H., Cao, W., Zhao, Z., Uğurlu, Ö., & Wang, X. (2026) · INQUIRY The Journal of Health Care Organization Provision and Financing · doi:10.1177/00469580261438708 · Cited by 3

The role of AI in oncology: present applications and future horizons

Weitzner, A. S., Singla, N., & Rai, A. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01408-y · Cited by 4

Integrating generative AI Into clinical reasoning education for medical students: Mixed methods study

Liu, C.-H., Chen, Y.-T., Hsu, C., Chen, H.-L., & Yang, C.-W. (2026) · JMIR Medical Education · doi:10.2196/94951 · Cited by 3

The Use of Artificial Intelligence in Health Applications and its Ethical Discussion: Traditional Compilation

SAĞIROĞLU, E., & Tosun, H. (2023) · Turkiye Klinikleri Journal of Medical Ethics-Law and History · doi:10.5336/mdethic.2022-92760 · Cited by 4

Detection of Hepatocellular Carcinoma Using Optimized miRNA Combinations and Interpretable Machine Learning Models

Long, Z., & Zhang, L. (2025) · IEEE Access · doi:10.1109/access.2025.3559105 · Cited by 3

AAPM Task Group Report 332: Verification of vendor‐provided data, tools, and test procedures in radiotherapy

Smith, K. S., Blasi, O. C., Casey, D., Chan, M. F., Dieterich, S., Dresser, S. A., Kennedy, C. W., Kielar, K., Lowenstein, J. R., Pawlicki, T. A., et al. (2025) · Medical Physics · doi:10.1002/mp.17879 · Cited by 3

On the classification of simple and complex biological images using Krawtchouk moments and Generalized pseudo-Zernike moments: a case study with fly wing images and breast cancer mammograms

Goh, J. Y., & Khang, T. F. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.698 · Cited by 3

Public Health Informatics in the Larger Context of Biomedical and Health Informatics

Hersh, W. (2020) · Health informatics · doi:10.1007/978-3-030-41215-9_3 · Cited by 3

Leveraging Deep Learning of Chest Radiograph Images to Identify Individuals at High Risk for Chronic Obstructive Pulmonary DiseasePreprint

Jorshery, S. D., Chandra, J., Walia, A. S., Stumiolo, A., Corey, K., Zekavat, S. M., Zinzuwadia, A. N., Patel, K., Short, S., Mega, J. L., et al. (2024) · medRxiv · doi:10.1101/2024.11.14.24317055 · Cited by 3

Endoscopic ultrasound-based artificial intelligence for gastrointestinal subepithelial lesions

Jiang, H., Ye, L., Yuan, X., Luo, Q., Zhou, N., & Hu, B. (2025) · EngMedicine · doi:10.1016/j.engmed.2025.100073 · Cited by 4

From T-cell sensitization to molecular–intelligent stratification: a roadmap for precision diagnosis of latent tuberculosis infection

Ni, R., Liu, Y., Armanni, A., Ghisleni, G., Fumagalli, S., An, Y., Li, Y., Zhuang, L., Ling, Y., Li, L., et al. (2026) · Clinical Microbiology Reviews · doi:10.1128/cmr.00258-25 · Cited by 3

Jak być świadomym użytkownikiem algorytmów? O potrzebie rozwijania kompetencji algorytmicznych

Iwasiński, Ł., & Furman, W. (2023) · Zagadnienia Informacji Naukowej - Studia Informacyjne · doi:10.36702/zin.910 · Cited by 2

PAPEL DA INTELIGÊNCIA ARTIFICIAL NA PREDIÇÃO DE EVENTOS CARDÍACOS

Lima, M. A. N., Ferreira, A. F., de Lima, M. E. C. A. G., Retto, Y. C. S., Nogueira, A. C. A., Bernardino, S. B., Aurélio, S. M., Sá, C. A., Motta, M. C., Norões, S. D. V., et al. (2024) · Brazilian Journal of Implantology and Health Sciences · doi:10.36557/2674-8169.2024v6n2p2213-2229 · Cited by 4

The Human-Centered AI Paradigm: Augmenting Clinical Expertise with Accountable Intelligence

(2024) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets50865 · Cited by 4

Beyond the root: Geometric characterization for the diagnosis of syndromic heritable thoracic aortic diseases

Romero, P., Lozano, M., Dux‐Santoy, L., Guala, A., Teixidó‐Turà, G., Sebastián, R., & García‐Fernández, I. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109176 · Cited by 2

Guided Decision Tree: A Tool to Interactively Create Decision Trees Through Visualization of Subsequent LDA Diagrams

Mohedano-Muñoz, M. A., Raya, L., & Sánchez, A. (2024) · Applied Sciences · doi:10.3390/app142210497 · Cited by 2

Using a Composite Summary of Daily Sex Hormones to Gauge Time Until Menopause: A Focus on Pregnanediol Glucuronide (PDG)

Winkles, J. F., Colvin, A. B., Khoudary, S. R. E., Santoro, N. F., Sammel, M. D., & Crawford, S. L. (2025) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgae895 · Cited by 2

Data computation platform (DCP): empowering pharma 4.0 innovation through GxP-compliant and scalable software platform enabling advanced data analytics and real-time process monitoring in regulated environments

Ladner, T., Weh, C., Dhillon, A., Giffard, M., & Iacovelli, D. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02574-9 · Cited by 2

Unveiling the myth of economies of scale in local government: empirical evidence from interpretable machine learning

Wang, K., & Zhao, J. (2025) · Economics of Governance · doi:10.1007/s10101-025-00331-5 · Cited by 2

15. Ethical usage of data in long-term care: how do we proceed?

Aarts, S. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_15 · Cited by 2

A Symbolic Regression Approach to Hepatocellular Carcinoma Diagnosis Using Hypermethylated CpG Islands in Circulating Cell-Free DNA

Goyal, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25191-7_23 · Cited by 2

Decision trees for COVID-19 prognosis learned from patient data: Desaturating the ER with Artificial IntelligencePreprint

Bernaola, N., de Lima, G., Riaño, M., Llanos, L., Heili-Frades, S. B., Sanchez, O., Lara, A., Plaza, G., Carballo, C., Gallego, P., et al. (2022) · medRxiv · doi:10.1101/2022.05.09.22274832 · Cited by 2

GraphXplore: Visual exploration and accessible preprocessing of medical data

Bellmann, L., Gottfried, K., Breitfeld, P., & Ückert, F. (2024) · SoftwareX · doi:10.1016/j.softx.2024.101978 · Cited by 2

Will artificial intelligence reach any limit in gastroenterology?

Jaoude, J. B., Bacha, R. A., & Abboud, B. (2024) · Artificial Intelligence in Gastroenterology · doi:10.35712/aig.v5.i2.91336 · Cited by 1

Externalist XAI ?

Søgaard, A. (2024) · Theoria · doi:10.1111/theo.12581 · Cited by 1

A Trusted Friend in the Middle of the Night: End-User Perspectives on Artificial Intelligence Informed Software Systems as a Decision-Making Aid for Patients and Clinicians Navigating Uncertainty in Kidney Transplant

Wingfield, L. R., Wainwright, K., Knight, S. R., & Webb, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-80889-0_14 · Cited by 1

Overcoming Neuroanatomical Mapping and Computational Barriers in Human Brain Synaptic Architecture

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