The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based
Open accessAbstract
Objective To examine the role of explainability in machine learning for healthcare (MLHC), and its necessity and significance with respect to effective and ethical MLHC application. Study design and setting This commentary engages with the growing and dynamic corpus of literature on the use of MLHC and artificial intelligence (AI) in medicine, which provide the context for a focused narrative review of arguments presented in favour of and opposition to explainability in MLHC. Results We find that concerns regarding explainability are not limited to MLHC, but rather extend to numerous well-validated treatment interventions as well as to human clinical judgment itself. We examine the role of evidence-based medicine in evaluating inexplicable treatments and technologies, and highlight the analogy between the concept of explainability in MLHC and the related concept of mechanistic reasoning in evidence-based medicine. Conclusion Ultimately, we conclude that the value of explainability in MLHC is not intrinsic, but is instead instrumental to achieving greater imperatives such as performance and trust. We caution against the uncompromising pursuit of explainability, and advocate instead for the development of robust empirical methods to successfully evaluate increasingly inexplicable algorithmic systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Technically speaking, similar to all other AI models, generative AI models in health care, particularly those based on deep learning, are often seen as “black boxes” [78].”
From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [78]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
Evidence based medicine: what it is and what it isn't
Sackett, D. L., Rosenberg, W. M. C., Gray, J. A. M., Haynes, R. B., & Richardson, W. S. (1996) · BMJ · doi:10.1136/bmj.312.7023.71 · Cited by 15,769
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
The environment and disease: association or causation?
Hill, A. B. (2015) · Journal of the Royal Society of Medicine · doi:10.1177/0141076814562718 · Cited by 7,804
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · cited 1×
“In-deed, prominent case studies have demonstrated precisely how explainable (and not intentionally biased) models can nonetheless deliver biased results that are only apparent upon empirical analysis [ 57 , 58 ].”
BackgroundThe Environment and Disease: Association or Causation?
Hill, A. B. (1965) · Proceedings of the Royal Society of Medicine · doi:10.1177/003591576505800503 · Cited by 6,974
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01228-7 · Cited by 6,909
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220
Lipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427
Yeh, M. (2014) · Journal of Nursing Research · doi:10.1097/jnr.0000000000000065 · Cited by 3,836
Research works printed but not linked: 2
- Holzinger A, Biemann C, Pattichis CS, Kell DB. What do we need to build explainable AI systems for the medical domain?arXiv:171209923 [cs, stat]. Published online December 28, 2017. Accessed November 24, 2020. http://arxiv.org/abs/1712.09923 · arXiv
- Lipton, 2020, The mythos of model interpretability, arXiv:160603490 [cs, stat]
Cited by
Sorted by citations
Generative AI in medical practice: in-depth exploration of privacy and security challenges
Chen, Y., & Esmaeilzadeh, P. (2024) · Journal of Medical Internet Research · doi:10.2196/53008 · Cited by 353
“Technically speaking, similar to all other AI models, generative AI models in health care, particularly those based on deep learning, are often seen as “black boxes” [78].”
Chen, H., Gomez, C., Huang, C.-M., & Unberath, M. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00699-2 · Cited by 300
Medical deep learning—A systematic meta-review
Egger, J., Gsaxner, C., Pepe, A., Pomykala, K. L., Jonske, F., Kurz, M., Li, J., & Kleesiek, J. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106874 · Cited by 289
Rahman, A., Debnath, T., Kundu, D., Khan, M. S. I., Aishi, A. A., Sazzad, S., Sayduzzaman, M., & Band, S. S. (2024) · AIMS Public Health · doi:10.3934/publichealth.2024004 · Cited by 260
Allgaier, J., Mulansky, L., Draelos, R. L., & Pryss, R. C. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102616 · Cited by 201
Hogg, H. D. J., Al-Zubaidy, M., Technology Enhanced Macular Services Study Reference Group, Talks, J., Denniston, A. K., Kelly, C. J., Malawana, J., Papoutsi, C., Teare, M. D., Keane, P. A., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/39742 · Cited by 163
Wang, H., Wu, W., Dou, Z., He, L., & Yang, L. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105173 · Cited by 163
REFORMS: Consensus-based Recommendations for Machine-learning-based Science
Kapoor, S., Cantrell, E. M., Peng, K., Pham, T. H., Bail, C. A., Gundersen, O. E., Hofman, J. M., Hullman, J. R., Lones, M. A., Malik, M. M., et al. (2024) · Science Advances · doi:10.1126/sciadv.adk3452 · Cited by 255
A Comparative Analysis of LIME and SHAP Interpreters With Explainable ML-Based Diabetes Predictions
Ahmed, S., Kaiser, M. S., Hossain, M. S., & Andersson, K. (2024) · IEEE Access · doi:10.1109/access.2024.3422319 · Cited by 173
Review of interpretable machine learning for process industries
Carter, A., Imtiaz, S. A., & Naterer, G. F. (2022) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2022.12.018 · Cited by 122
Javaid, A., Zghyer, F., Kim, C., Spaulding, E. M., Isakadze, N., Ding, J., Kargilis, D., Gao, Y., Rahman, F., Brown, D. E., et al. (2022) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2022.100379 · Cited by 119
Evidence, ethics and the promise of artificial intelligence in psychiatry
McCradden, M., Hui, K., & Buchman, D. Z. (2023) · Journal of Medical Ethics · doi:10.1136/jme-2022-108447 · Cited by 97
Artificial intelligence applications in histopathology
Bahadir, C. D., Omar, M., Rosenthal, J., Marchionni, L., Liechty, B., Pisapia, D. J., & Sabuncu, M. R. (2024) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-023-00012-7 · Cited by 93
Five critical quality criteria for artificial intelligence-based prediction models
van Royen, F. S.-A., Asselbergs, F. W., Alfonso, F., Vardas, P. E., & van Smeden, M. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad727 · Cited by 77
Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review
Ose, B. M., Sattar, Z., Gupta, A., Gahona, C. C. T., Harvey, C., & Noheria, A. (2024) · Current Cardiology Reports · doi:10.1007/s11886-024-02062-1 · Cited by 73
The ethical requirement of explainability for AI-DSS in healthcare: a systematic review of reasons
Freyer, N., Groß, D., & Lipprandt, M. (2024) · BMC Medical Ethics · doi:10.1186/s12910-024-01103-2 · Cited by 67
Tree-based local explanations of machine learning model predictions, AraucanaXAI
Parimbelli, E., Buonocore, T. M., Nicora, G., Michalowski, W., Wilk, S., & Bellazzi, R. (2021) · Artif. Intell. Medicine · doi:10.1016/j.artmed.2022.102471 · Cited by 46
Patino, G. A., Amiel, J. M., Brown, M., Lypson, M. L., & Chan, T. M. (2024) · Academic Medicine · doi:10.1097/acm.0000000000005636 · Cited by 43
Clinical deployment environments: Five pillars of translational machine learning for health
Harris, S. K., Bonnici, T., Keen, T., Lilaonitkul, W., White, M. J., & Swanepoel, N. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.939292 · Cited by 46
A flexible symbolic regression method for constructing interpretable clinical prediction models
Cava, W. L., Lee, P. C., Ajmal, I., Ding, X., Solanki, P., Cohen, J. B., Moore, J. H., & Herman, D. S. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00833-8 · Cited by 50
On the interpretability of quantum neural networks
Pira, L., & Ferrie, C. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00191-y · Cited by 57
McCoy, L. G., Ng, F. Y. C., Sauer, C. M., Legaspi, K. E. Y., Jain, B., Gallifant, J., McClurkin, M., Hammond, A., Goode, D., Gichoya, J. W., et al. (2024) · BMC Medical Education · doi:10.1186/s12909-024-06048-z · Cited by 52
Self-citationGonzález-Alday, R., García-Cuesta, E., Maojo, & Kulikowski, C. (2023) · Applied Sciences · doi:10.3390/app131910778 · Cited by 44
Li, L. T., Haley, L. C., Boyd, A. K., & Bernstam, E. V. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104531 · Cited by 49
Blackman, J., & Veerapen, R. J. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02891-2 · Cited by 38
Carreras, J., Nakamura, N., & Hamoudi, R. (2022) · Healthcare · doi:10.3390/healthcare10010155 · Cited by 36
A Framework for Interpretability in Machine Learning for Medical Imaging
Wang, A. Q., Karaman, B. K., Kim, H., Rosenthal, J., Saluja, R., Young, S. I., & Sabuncu, M. R. (2024) · IEEE Access · doi:10.1109/access.2024.3387702 · Cited by 41
Oberste, L., & Heinzl, A. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3227225 · Cited by 35
Why we do need explainable AI for healthcare
Cinà, G., Röber, T. E., Goedhart, R., & Birbil, Ş. İ. (2025) · Diagnostic and Prognostic Research · doi:10.1186/s41512-025-00209-4 · Cited by 41
DeepFixCX : Explainable privacy‐preserving image compression for medical image analysis
Gaudio, A., Smailagic, A., Faloutsos, C., Mohan, S., Johnson, E., Liu, Y., Costa, P., & Campilho, A. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1495 · Cited by 34
Recommendations for using artificial intelligence in clinical flow cytometry
Ng, D. P., Simonson, P. D., Tárnok, A., Lucas, F. M., Kern, W., Rolf, N., Bogdanoski, G., Green, C., Brinkman, R. R., & Czechowska, K. (2024) · Cytometry Part B Clinical Cytometry · doi:10.1002/cyto.b.22166 · Cited by 31
Prognosticating the outcome of intensive care in older patients—a narrative review
Beil, M., Moreno, R. P., Fronczek, J., Kogan, Y., Moreno, R. P., Flaatten, H. K., Guidet, B., de Lange, D. W., Leaver, S. K., Nachshon, A., et al. (2024) · Annals of Intensive Care · doi:10.1186/s13613-024-01330-1 · Cited by 29
Against explainability requirements for ethical artificial intelligence in health care
Kawamleh, S. (2022) · AI and Ethics · doi:10.1007/s43681-022-00212-1 · Cited by 27
Ursin, F., Lindner, F., Ropinski, T., Salloch, S., & Timmermann, C. (2023) · Ethik in der Medizin · doi:10.1007/s00481-023-00761-x · Cited by 29
Gashi, M., Vuković, M., Jekic, N., Thalmann, S., Holzinger, A., Jean-Quartier, C., & Jeanquartier, F. (2022) · BioMedInformatics · doi:10.3390/biomedinformatics2010009 · Cited by 26
Fahmi, A., Wong, D., Walker, L. E., Buchan, I. E., Pirmohamed, M., Sharma, A. K., Cant, H. E. O., Ashcroft, D. M., & van Staa, T. P. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281466 · Cited by 24
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 24
Jennings, A. M., & Cox, D. J. (2023) · Behavior Analysis in Practice · doi:10.1007/s40617-023-00868-z · Cited by 19
Lans, A., Pierik, R., Bales, J. R., Fourman, M. S., Shin, D., Kanbier, L. N., Rifkin, J., DiGiovanni, W., Chopra, R. R., Moeinzad, R., et al. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102396 · Cited by 20
Machine Learning for Precision Agriculture and Crop Yield Optimization
Roy, P., Padhiary, M., Hoque, A., Sahu, B., Roy, D., & Kumar, K. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9208-9.ch007 · Cited by 18
In humble defense of unexplainable black box prediction models in healthcare
van Royen, F. S.-A., Weerts, H., de Hond, A., Geersing, G., Rutten, F. H., Moons, K. G. M., & van Smeden, M. (2025) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2025.112013 · Cited by 18
The emerging role of artificial intelligence enabled electrocardiograms in healthcare
Sau, A., & Ng, F. S. (2023) · BMJ Medicine · doi:10.1136/bmjmed-2022-000193 · Cited by 19
Sun, Z., Yuan, Y., Farrahi, V., Herold, F., Xia, Z., Xiong, X., Qiao, Z., Shi, Y., Yang, Y., Qi, K., et al. (2024) · BMC Public Health · doi:10.1186/s12889-024-20510-z · Cited by 16
Li, F., Li, J., Lu, Q., You, Y., Xu, Z., Liu, L., Fu, L., Peng, G., Yi, J., & Li, C. (2025) · Materials & Design · doi:10.1016/j.matdes.2025.113948 · Cited by 15
Prediction and explainability in AI: Striking a new balance?
Raz, A. E., Heinrichs, B., Avnoon, N., Eyal, G., & Inbar, Y. (2024) · Big Data & Society · doi:10.1177/20539517241235871 · Cited by 15
Graph Neural Networks for Parkinson’s Disease Monitoring and Alerting
Zafeiropoulos, N., Bitilis, P., Tsekouras, G. E., & Kotis, K. (2023) · Sensors · doi:10.3390/s23218936 · Cited by 14
A prediction framework for pharmaceutical drug consumption using short time-series
Bertolotti, F., Schettini, F., Ferrario, L. B., Bellavia, D., & Foglia, E. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124265 · Cited by 14
McCradden, M. D., Thai, K., Assadi, A., Tonekaboni, S., Stedman, I., Joshi, S. D., Zhang, M., Chevalier, F., & Goldenberg, A. (2025) · BMJ Evidence-Based Medicine · doi:10.1136/bmjebm-2024-112919 · Cited by 18
Machine Learning in Computer Aided Engineering
Montáns, F. J., Cueto, E., & Bathe, K. (2023) · Computational methods in engineering & the sciences · doi:10.1007/978-3-031-36644-4_1 · Cited by 13
Early Warning Systems for Critical Illness Outside the Intensive Care Unit
Henry, K. E., & Giannini, H. M. (2024) · Critical Care Clinics · doi:10.1016/j.ccc.2024.03.007 · Cited by 13
Prinzi, F., Militello, C., Zarcaro, C., Bartolotta, T. V., Gaglio, S., & Vitabile, S. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108576 · Cited by 11
Mohanraj, P., Raman, V., & Ramanathan, S. (2024) · Diagnostics · doi:10.3390/diagnostics14192181 · Cited by 10
Phillips, V. J. (2025) · AI & Society · doi:10.1007/s00146-025-02337-3 · Cited by 11
Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study
Crocamo, C., Cioni, R. M., Canestro, A., Nasti, C., Palpella, D., Piacenti, S., Bartoccetti, A., Re, M., Simonetti, V., Pietro, C. B. D. S., et al. (2025) · JMIR Formative Research · doi:10.2196/65555 · Cited by 12
Applications of Machine Learning in Healthcare: An Overview
Samarpita, S., & Satpathy, R. (2022) · IEEE International Conference on Industrial Electronics: Developments & Applications (ICIDeA) · doi:10.1109/icidea53933.2022.9970177 · Cited by 10
Alarcon, G. M., & Willis, S. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.134 · Cited by 10
A time series algorithm to predict surgery in neonatal necrotizing enterocolitis
Cui, C., Qiu, L., Li, L., Chen, F., Liu, X., Sun, H. W., Liu, X.-C., Bao, L., & Li, L.-Q. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02695-w · Cited by 9
Ratkevičiūtė, K., & Aliukonis, V. (2025) · Healthcare · doi:10.3390/healthcare13121429 · Cited by 11
Nowrozy, R., Ahmed, K. E. U., & Wang, H. (2025) · PLoS ONE · doi:10.1371/journal.pone.0310553 · Cited by 12
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
A review of applications of artificial intelligence in cardiorespiratory rehabilitation
Raja, M. A., Loughran, R., & McCaffery, F. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101327 · Cited by 8
Automatic Assessment of Speech Intelligibility using Consonant Similarity for Head and Neck Cancer
Quintas, S., Mauclair, J., Woisard, V., & Pinquier, J. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-182 · Cited by 8
Gallardo‐Pizarro, A., Peyrony, O., Chumbita, M., Monzó, P., Aiello, T. F., Teijón‐Lumbreras, C., Gras, E., Mensa, J., Soriano, À., & García‐Vidal, C. (2024) · Expert Review of Anti-infective Therapy · doi:10.1080/14787210.2024.2322445 · Cited by 8
Artificial intelligence analysis of the single-lead ECG predicts long-term clinical outcomes
Alrumayh, A., Bächtiger, P., Sau, A., Mansell, J., Almonte, M. T., Chhatwal, K., Ng, F. S., Kelshiker, M. A., & Peters, N. S. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztaf057 · Cited by 7
Cross-platform multi-cancer histopathology classification using local-window vision transformers
Nayeem, M. D., Nisita, N. J., Islam, M. M., Rahman, M. S., & Ali, A. B. M. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-24791-1 · Cited by 7
ChatGPT and beyond with artificial intelligence (AI) in health: Lessons to be learned
Thiébaut, R., Hejblum, B. P., Fleur Mougin, A., Tzourio, C., & Richert, L. E. (2023) · Joint Bone Spine · doi:10.1016/j.jbspin.2023.105607 · Cited by 7
Rahman, M. M., Calhoun, V. D., & Plis, S. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1129 · Cited by 16
Arends, B. K. O., McCormick, J. M., van der Harst, P., Heus, P., & van Es, R. (2025) · European Journal of Clinical Investigation · doi:10.1111/eci.14387 · Cited by 5
Great debate: artificial intelligence will replace much of what cardiologists do
Averbuch, T., Asselbergs, F. W., Vardas, P. E., & Spall, H. G. C. V. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf305 · Cited by 5
Moral Values in Medical AI: A Scoping ReviewPreprint
Victor, G., Barbu, A., & Bélisle‐Pipon, J. (2024) · Research Square · doi:10.21203/rs.3.rs-4391239/v1 · Cited by 8
Hlede, V., Valanci, S., D’Antuono, G., Dow, H., Wiggins, R. H., Dorman, T., & O’Beirne, R. (2026) · JMIR Medical Education · doi:10.2196/69156 · Cited by 4
Using AI explainable models and handwriting/drawing tasks for psychological well-being
Prinzi, F., Barbiero, P., Greco, C., Amorese, T., Cordasco, G., Lió, P., Vitabile, S., & Esposito, A. A. (2024) · Information Systems · doi:10.1016/j.is.2024.102465 · Cited by 5
Ardahanlı, İ., Aslan, R., Akdoğan, M., Akgün, O., & Ardahanlı, A. (2025) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2025.153926 · Cited by 4
Patiño, G. A., & Roberts, L. W. (2024) · Academic Medicine · doi:10.1097/acm.0000000000005793 · Cited by 4
Imperfect physics-guided neural networks
Carter, A., Imtiaz, S. A., & Naterer, G. F. (2024) · Chemical Engineering Science · doi:10.1016/j.ces.2024.121153 · Cited by 3
Zhao, Y., Ren, B., Yu, W., Zhang, H., Zhao, D., Lv, J., Xie, Z., Jiang, K., Shang, L., Yao, H., et al. (2022) · Neurology and Therapy · doi:10.1007/s40120-022-00355-7 · Cited by 3
New Era’s of Artificial Intelligence in Pharmaceutical Industries
Dubey, A., & Yadav, A. P. (2024) · Asian Journal of Pharmaceutical Research and Development · doi:10.22270/ajprd.v12i2.1362 · Cited by 5
State-space-guided neural networks for fault detection
Carter, A., Rezaei, A., Imtiaz, S. A., & Naterer, G. F. (2025) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2025.109260 · Cited by 2
Kassem, A., Folkes, J., Mukherjee, S., & Rosengarten, J. A. (2025) · The Egyptian Heart Journal · doi:10.1186/s43044-025-00689-1 · Cited by 2
Wu, W., Mei, B., Wu, Y., Liu, L., Wang, Y., Lombardo, M., & Yang, H. (2026) · Food Research International · doi:10.1016/j.foodres.2026.118499 · Cited by 2
Hajikarimloo, B., Mohammadzadeh, I., Tos, S. M., Mortezaei, A., & Habibi, M. A. (2026) · Acta Neurochirurgica · doi:10.1007/s00701-026-06775-w · Cited by 2
Artificial Intelligence in Healthcare
Choudhary, R., Kaushik, A., & Igulu, K. T. (2022) · Advances in business information systems and analytics book series · doi:10.4018/978-1-6684-4950-9.ch001 · Cited by 2
ChatGPT et au-delà avec l’intelligence artificielle en santé : des leçons à tirer
Thiébaut, R., Hejblum, B. P., Fleur Mougin, A., Tzourio, C., & Richert, L. E. (2023) · Revue du Rhumatisme · doi:10.1016/j.rhum.2023.07.007 · Cited by 2
Inter-Pol: An Interpretable Machine Learning Framework for Solvent Polarity Prediction
Ugurlu, S. Y. (2025) · Journal of Solution Chemistry · doi:10.1007/s10953-025-01508-6 · Cited by 1
Sleptsova, E. E., Vershinina, O., Ivanchenko, M., & Turubanova, V. D. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27020667 · Cited by 1
Dave, A., Dave, A., & Moussa, I. D. (2026) · Bioengineering · doi:10.3390/bioengineering13020167 · Cited by 1
Machine Learning aplicado à atenção domiciliar para predição de condição de óbito
Silva, D. H. C., & Timo, E. M. D. N. (2022) · Research Society and Development · doi:10.33448/rsd-v11i14.36078 · Cited by 1
Cui, C., Chen, F., & Li, L.-Q. (2023) · PubMed Central · doi:10.7499/j.issn.1008-8830.2302165 · Cited by 1
The fundamentals of AI ethics in medical imaging
Amann, J., Bürger, V. K., Livne, M., Bui, C. K. T., & Madai, V. I. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-323761-4.00010-9 · Cited by 3
Algorithmic Transparency and Algorithmic Accountability in Organizations
Mastrogiorgio, A., Nauta, P. D., & Martinez, M. (2025) · Lecture notes in information systems and organisation · doi:10.1007/978-3-032-01396-5_8 · Cited by 1
KI in der Medizin: Ethische Aspekte
Wágner, R. (2023) · Zeitschrift für medizinische Ethik · doi:10.30965/29498570-20230010 · Cited by 1
Prosperi, M., Bian, J., Harle, C. A., Wang, M., Lee, G., Li, Y., Štiglic, G., Kleesiek, J., Pongtriang, P., & Veltri, P. (2023) · ACM SIGBioinformatics Record · doi:10.1145/3615362.3615363
HealSmart Homes: Integrating Technology for Enhanced Convalescent Care
Kanimozhi, T. (2024) · Journal of Electrical Systems · doi:10.52783/jes.3537
Santos, F., Suárez, F., & Sallán, J. G. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3305-7_15
Machine learning in predicting postoperative complications in Crohn’s disease
Zhang, L., Chen, L.-X., Yang, W.-J., & Hu, B. (2024) · World Journal of Gastrointestinal Surgery · doi:10.4240/wjgs.v16.i8.2745
Hlede, V., Valanci, S., D’Antuono, G., Dow, H., Wiggins, R. H., Dorman, T., & O'Beirne, R. (2024) · doi:10.2196/preprints.69156
Patiño, G. A., Amiel, J. M., Brown, M. E. L., Lypson, M. L., & Chan, T. M. (2024) · doi:10.32920/27926490.v1
Patiño, G. A., Amiel, J. M., Brown, M. E. L., Lypson, M. L., & Chan, T. M. (2024) · doi:10.32920/27926490
Validation of a Novel Data-Driven Algorithm to Detect Atypical Prescriptions in Radiation Therapy
Thropp, C., Hepel, J. T., Leech, T., Klein, E., & Li, Q. (2025) · Advances in Radiation Oncology · doi:10.1016/j.adro.2025.101804
Pervaiz, K., & Bawa, S. S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-93601-2_6 · Cited by 2
Metadata
- Authors
- McCoy, L. G., Brenna, C. T. A., Chen, S. S., Vold, K., & Das, S.
- Affiliations
- University of Toronto (CA)
- Corresponding author
- McCoy, Liam G.
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2022-02
- Volume
- 142
- Pages
- 252-257
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W3213880547
- Cited by
- 118
- References
- 64
- Citations by year
- 2026: 17 · 2025: 28 · 2024: 31 · 2023: 23 · 2022: 19
- FWCI
- 1.32
- Type
- journal article
- Language
- en
- Open access
- green
- philpapers.org
- Licence
- Locations
- Journal of Clinical Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- Funders
- Canadian Institutes of Health Research
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- explainability, machine learning in healthcare, artificial intelligence in healthcare, evidence-based medicine, mechanistic reasoning, algorithm evaluation, trust in algorithms, clinical judgment, ethical use of AI, black-box models
- MeSH terms
- Machine Learning, Artificial Intelligence, Delivery of Health Care, Humans, Technology, Trust
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
