How to develop machine learning models for healthcare
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If samples used for manual performance evaluation are not representative of the overall patient population, there is an increased risk that reported model performance is not representative of true model performance.
“However, if these samples are not representative of the overall patient population, there is an increased risk of the reported model performance not being representative of the true model performance 37 .”
From Empirical data drift detection experiments on real-world medical imaging data (2024) · cites this paper as [37]
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BackgroundAI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring
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MethodPerformance of ChatGPT and GPT-4 on Neurosurgery Written Board Examinations
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BackgroundXAI Framework for Cardiovascular Disease Prediction Using Classification Techniques
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“20 In our review, we include studies using ML for the purpose of predictive modeling (such as decision trees, support vector machines [SVM], k-means clustering) and exclude studies us-ing statistical methods for cohort summarization and hypothesis testing (such as odds ratio, Chi-square…”
MethodEmpirical data drift detection experiments on real-world medical imaging data
Kore, A., Abbasi Bavil, E., Subasri, V., Abdalla, M., Fine, B., Dolatabadi, E., & Abdalla, M. (2024) · Nature Communications · doi:10.1038/s41467-024-46142-w · Cited by 124 · see citing works
“However, if these samples are not representative of the overall patient population, there is an increased risk of the reported model performance not being representative of the true model performance 37 .”
Hierarchical Machine Learning for High-Fidelity 3D Printed Biopolymers
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“Several guidelines for developing ML models with healthcare data have recently been published 24 – 28 .”
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“72 Consequently, fair evaluation and comparisons can only be made through standardized benchmark testing with fixed data sets.”
BackgroundAI in Rehabilitation Medicine: Opportunities and Challenges
Lanotte, F., O’Brien, M. K., & Jayaraman, A. (2023) · Annals of Rehabilitation Medicine · doi:10.5535/arm.23131 · Cited by 98 · see citing works
“Various methods are available to separate the training, testing, and validation datasets during AI development, including a ratio-based train-test-validation split, leave-one-out, k-fold, or Monte Carlo sampling [60].”
BackgroundMethodInfluentialS E RS‐Based Biosensors Combined with Machine Learning for Medical Application**
Ding, Y., Sun, Y., Liu, C., Jiang, Q., Chen, F., & Cao, Y. Z. (2023) · ChemistryOpen · doi:10.1002/open.202200192 · Cited by 97 · see citing works
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“Since such clinical data from patients is usually low in volume and could hence result in overfitting when training the model [14], we choose to use a Bayesian regularized neural network, which has good capability of generalization that avoids overfitting [57, 68].”
MethodMotwani, A., Shukla, P. K., & Pawar, M. K. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102431 · Cited by 97 · see citing works
“For example image analysis using DL algorithm requires large amount of valid data and its availability is a challenge [101].”
Requirements and reliability of AI in the medical context
Balagurunathan, Y., Mitchell, R., & Naqa, I. E. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.024 · Cited by 91 · see citing works
“It may be necessary that ML methods follow a series of internal and external validation for ML related clinical applications to improve chances of being useful for routine patient care with some extent of prospective (or live) training [56, 57].”
MethodClosing the translation gap: AI applications in digital pathology
Steiner, D. F., Chen, P.-H. C., & Mermel, C. H. (2020) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2020.188452 · Cited by 90 · see citing works
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Introduction to Artificial Intelligence and Machine Learning for Pathology
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“The general elements required for machine learning model development 66,119 are similar to those for statistical predictive models.”
BackgroundMethodVaradharajan, C., Appling, A., Arora, B., Christianson, D., Hendrix, V., Kumar, V., Junior, A. R. L., Müller, J., Oliver, S. K., Ombadi, M., et al. (2022) · Hydrological Processes · doi:10.1002/hyp.14565 · Cited by 82 · see citing works
“…data analysis or modelling, and the use of ML for making optimal decisions has been proposed for numerous applications such as business intelligence, healthcare, network optimization, and precision agriculture (Chen, Liu, & Peng, 2019; Liakos et al., 2018; Ma et al., 2020; Provost & Fawcett, 2013).”
MethodWang, J., Wu, L., Wang, H., Choo, K. R., & He, D. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3004231 · Cited by 71 · see citing works
“The recent trend in ML (and artificial intelligence) has resulted in a number of advances and challenges for healthcare analytics [21]–[23].”
BackgroundX-MIR: EXplainable Medical Image Retrieval
Hu, B., Vasu, B., & Hoogs, A. J. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00161 · Cited by 68 · see citing works
“Recent techniques include prototypical part networks [10] and concept bottleneck networks [32], where models are made more interpretable via specialized loss functions.”
BackgroundMethodNishida, N., Yamakawa, M., Shiina, T., Mekada, Y., Nishida, M., Sakamoto, N., Nishimura, T., Iijima, H., Hirai, T., Takahashi, K., et al. (2022) · Journal of Gastroenterology · doi:10.1007/s00535-022-01849-9 · Cited by 67 · see citing works
“Compared with the previous study, much larger size of the training cohort with enough variation from 11 different institutes should give rise to the reliable performance of our AI models even in the independent test cohort, which should not be affected by overfitting [5].”
BackgroundMethodResultHigh-performance exclusion of schizophrenia using a novel machine learning method on EEG data
Buettner, R., Hirschmiller, M., Schlosser, K., Rössle, M., Fernandes, M., & Timm, I. J. (2019) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom46333.2019.9009437 · Cited by 66 · see citing works
“Artificial Intelligence [25] consisting of different Machine Learning techniques [26] such as Deep Learning [6], or supervised classifiers like the Random Forest method [27], have already become established in healthcare.”
MethodInsights from teaching artificial intelligence to medical students in Canada
Hu, R., Fan, K. Y., Pandey, P., Hu, Z., Yau, O., Teng, M., Wang, P., Li, T., Ashraf, M., & Singla, R. (2022) · Communications Medicine · doi:10.1038/s43856-022-00125-4 · Cited by 58 · see citing works
Pharmacometrics and Machine Learning Partner to Advance Clinical Data Analysis
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Explainable machine learning practices: opening another black box for reliable medical AI
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“For data including simple or engineered features such as clinical characteristics, then linear/logistic regression, support vector machines, and decision tree are considered accurate, while for images convolutional neural networks seems to be the norm [5].”
BackgroundMethodInfluentialFederated Learning-Aware Multi-Objective Modeling and blockchain-enable system for IIoT applications
Lakhan, A. R., Mohammed, M. A., Kadry, S., AlQahtani, S. A., Maashi, M. S., & Abdulkareem, K. H. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107839 · Cited by 59 · see citing works
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“Moreover, there are no clear guidelines on how to develop AI approaches for medical studies, even though a few recommendations have been summarized by Foster et al [24] and Chen et al [25].”
Valik, J. K., Ward, L., Tanushi, H., Johansson, A. F., Färnert, A., Mogensen, M. L., Pickering, B. W., Herasevich, V., Dalianis, H., Henriksson, A., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-38858-4 · Cited by 52 · see citing works
Assessment of ChatGPT’s performance on neurology written board examination questions
Chen, T. C., Multala, E., Kearns, P., Delashaw, J. B., Dumont, A. S., Maraganore, D. M., & Wang, A. (2023) · BMJ Neurology Open · doi:10.1136/bmjno-2023-000530 · Cited by 51 · see citing works
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“ML focuses on developing computer systems that can learn from big data (or data of such volume, collection velocity, or complexity that it is difficult or impossible to process using traditional methods) [14], identifying patterns, and making decisions with minimal human intervention [15].”
BackgroundHanna, M. G., Olson, N. H., Zarella, M. D., Dash, R. C., Herrmann, M. D., Furtado, L. V., Stram, M. N., Raciti, P. M., Hassell, L. A., Mays, A., et al. (2023) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2023-0042-cp · Cited by 51 · see citing works
Machine Learning Models for Inpatient Glucose Prediction
Zale, A. D., & Mathioudakis, N. N. (2022) · Current Diabetes Reports · doi:10.1007/s11892-022-01477-w · Cited by 50 · see citing works
“These steps include problem selection, data collection, machine learning development, model validation, assessment of impact, and deployment and monitoring [24].”
BackgroundA normative framework for artificial intelligence as a sociotechnical system in healthcare
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Discriminating BTX Molecules by the Nonselective Metal Oxide Sensor-Based Smart Sensing System
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GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows
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Machine-Learning Implementation in Clinical Anesthesia: Opportunities and Challenges
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de Hond, A., Kant, I. M. J., Honkoop, P., Smith, A. D., Steyerberg, E. W., & Sont, J. K. (2022) · Scientific Reports · doi:10.1038/s41598-022-24909-9 · Cited by 44 · see citing works
“All the other days are normal asthma control days 9,11 .”
BackgroundAmiri, M., Raimondo, F., Fisher, P. M., Cacic Hribljan, M., Sidaros, A., Othman, M. H., Zibrandtsen, I., Bergdal, O., Fabritius, M. L., Hansen, A. E., et al. (2024) · Neurocritical Care · doi:10.1007/s12028-023-01816-z · Cited by 44 · see citing works
“While the quality of the data that underly machine learning models is crucial, data quantity is also important because datasets with many variables but limited number of samples introduce high level of variance, rendering the models imbalanced.(42) Despite our relatively large population of acute DoC patients, our res…”
ResultThe promise of graphene-based transistors for democratizing multiomics studies
Lu, H., Kane, A. A., Parkinson, J., Gao, Y., Hajian, R., Heltzen, M., Goldsmith, B. R., & Aran, K. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2021.113605 · Cited by 44 · see citing works
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“This approach is useful to measure the prediction performance of the final production model or compare predictions with reference to held-out samples [54].”
ResultMachine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer
Bostancı, E., Koçak, E., Ünal, M., Güzel, M. S., Açıcı, K., & Aşuroğlu, T. (2023) · Sensors · doi:10.3390/s23063080 · Cited by 39 · see citing works
“It has been used in many fields of healthcare [1–3].”
MethodLeveraging Data Science for Global Health
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Federated learning enables intelligent reflecting surface in fog-cloud enabled cellular network
Lakhan, A. R., Mohammed, M. A., Kadry, S., Abdulkareem, K. H., AL‐Dhief, F. T., & Hsu, C. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.758 · Cited by 38 · see citing works
Neonatal mortality prediction with routinely collected data: a machine learning approach
Batista, A. F. D. M., Diniz, C. S. G., Bonilha, E. D. A., Kawachi, I. I., & Filho, A. D. P. C. (2021) · BMC Pediatrics · doi:10.1186/s12887-021-02788-9 · Cited by 38 · see citing works
Artificial intelligence in oncology: From bench to clinic
Elkhader, J. A., & Elemento, O. (2021) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2021.04.013 · Cited by 39 · see citing works
Juang, W., Hsu, M.-H., Cai, Z.-X., & Chen, C.-M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0276501 · Cited by 36 · see citing works
Artificial Intelligence in a Structurally Unjust Society
Lin, T.-A., & Chen, P.-H. C. (2022) · Feminist Philosophy Quarterly · doi:10.5206/fpq/2022.3/4.14191 · Cited by 36 · see citing works
“Chen, Liu, and Peng 2019).”
MethodSelf-citationNishida, N., & Kudo, M. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.594580 · Cited by 36 · see citing works
“The process of selecting a model ’ s architecture and training essentially involves a balance between model under fi tting and over fi tting (17).”
BackgroundInfluentialLi, M., Chang, J., Deng, Z., Mi, L., Kumar, M., Wang, S., He, Y. J., & Meng, G. (2022) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2022.133115 · Cited by 35 · see citing works
Rubrics to Prompts: Assessing Medical Student Post-Encounter Notes with AI
Jamieson, A. R., Holcomb, M. J., Dalton, T. O., Campbell, K. K., Vedovato, S., Shakur, A. H., Kang, S.-Y., Hein, D., Lawson, J., Danuser, G. M., et al. (2024) · NEJM AI · doi:10.1056/aics2400631 · Cited by 37 · see citing works
Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database
Olsavszky, V., Dosius, M., Vlădescu, C., & Benecke, J. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17144979 · Cited by 35 · see citing works
Jafari, M., Marquez, G., Dechiraju, H., Gomez, M. M., & Rolandi, M. (2023) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2023.101535 · Cited by 34 · see citing works
Cohen, R. Y., & Kovacheva, V. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3259395 · Cited by 33 · see citing works
“Yet, most sophisticated AI models exist only in high-profile publications, and only a few are implemented in clinical practice [4], [5].”
BackgroundChen, B., Sun, H., Gao, J., Chen, Z., & Qiu, X. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110490 · Cited by 33 · see citing works
Mandal, P., Maiti, A., Paul, S., Bhattacharya, S., & Paul, S. (2022) · Tropical Cyclone Research and Review · doi:10.1016/j.tcrr.2023.03.001 · Cited by 33 · see citing works
Artificial Intelligence to Improve Antibiotic Prescribing: A Systematic Review
Amin, D., Garzόn-Orjuela, N., Garcia Pereira, A., Parveen, S., Vornhagen, H., & Vellinga, A. (2023) · Antibiotics · doi:10.3390/antibiotics12081293 · Cited by 34 · see citing works
“, train/test splits) are generated [59].”
BackgroundInfluentialFaria, A. L., Almeida, Y., Branco, D., Câmara, J., Cameirão, M. S., Ferreira, L., Moreira, A., Paulino, T., Rodrigues, P., Spínola, M., et al. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2023.1258323 · Cited by 35 · see citing works
“The application of Machine Learning (ML)-based methods in healthcare is also rapidly evolving with practical implications in the prevention, diagnosis, treatment, and prognosis of specific clinical conditions (17, 18).”
MethodMa, W., Wu, H., Chen, Y., Xu, H., Jiang, J., Du, B., Wan, M., Ma, X., Chen, X., Lin, L., et al. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae028 · Cited by 29 · see citing works
“Though promising, developing models that integrate prior biological knowledge (e.g. known gene regulatory pathways for specific types of tumors) has not been extensively explored due to the limited availability of such prior knowledge [110].”
BackgroundInfluentialMachine Learning for Clinical Predictive Analytics
Weng, W. (2020) · Springer eBooks · doi:10.1007/978-3-030-47994-7_12 · Cited by 30 · see citing works
“…free texts (Pivovarov et al., 2015; Weng & Szolovits, 2018; Weng et al., 2019b), images (Gulshan et al., 2016; Esteva et al., 2017; Bejnordi et al., 2017; Chen et al., 2019a), physi-a ological signals (Lehman et al., 2018), and even cross-modal information (Hsu et al., 2018; Liu et al., 2019).”
BackgroundRonzio, L., Cabitza, F., Barbaro, A., & Banfi, G. (2021) · Diagnostics · doi:10.3390/diagnostics11020372 · Cited by 29 · see citing works
“However, the lack of prospective studies can result in a lack of evaluations of the impact of ML in real-world practice; in turn, this can reduce the benefits of using ML [64].”
BackgroundReshi, A. A., Ashraf, I., Rustam, F., Shahzad, H. F., Mehmood, A., & Choi, G. S. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.547 · Cited by 29 · see citing works
Combining Clinical Symptoms and Patient Features for Malaria Diagnosis: Machine Learning Approach
Mariki, M., Mkoba, E. S., & Mduma, N. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2022.2031826 · Cited by 31 · see citing works
Zhou, Y., Yao, X.-Y., Han, W., Wang, Y., Li, Z., & Li, Y. (2022) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.5827 · Cited by 26 · see citing works
“Nowadays, machine learning has made breakthroughs in diagnosing dementia and mental health.(14,23) However, MCI patients have several underlying comorbidities that can mask dementia.”
BackgroundTranslational biomarkers in the era of precision medicine
Bravo-Merodio, L., Acharjee, A., Russ, D., Bisht, V., Williams, J. A., Tsaprouni, L., & Gkoutos, G. V. (2020) · Advances in clinical chemistry · doi:10.1016/bs.acc.2020.08.002 · Cited by 28 · see citing works
Bülow, R. D., Dimitrov, D., Boor, P., & Sáez-Rodríguez, J. (2021) · Seminars in Immunopathology · doi:10.1007/s00281-021-00847-y · Cited by 27 · see citing works
“In general, deep learning performs better with increasing amounts of data [69], although this amount can vary substantially for different approaches.”
BackgroundIssitt, R. W., Cortina‐Borja, M., Bryant, W. A., Bowyer, S. A., Taylor, A. M., & Sebire, N. J. (2022) · Cureus · doi:10.7759/cureus.22443 · Cited by 27 · see citing works
“There is increasing interest in the potential role that data science and machine learning can play in healthcare [1-5].”
BackgroundExpert Discussions Improve Comprehension of Difficult Cases in Medical Image Assessment
Schaekermann, M., Cai, C. J., Huang, A. E., & Sayres, R. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376290 · Cited by 28 · see citing works
Coombs, L., Orlando, A., Wang, X., Shaw, P., Rich, A. S., Lakhtakia, S., Titchener, K., Adamson, B., Miksad, R. A., & Mooney, K. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00660-3 · Cited by 27 · see citing works
“Prostate 95 (15) 8 (13) 87 (15) 83 (14) 6 (14) 77 (14)”
InfluentialAjibade, S.-S. M., Alhassan, G. N., Zaidi, A. T., Oki, O. A., Awotunde, J. B., Ogbuju, E., & Akintoye, K. A. (2024) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2024.200441 · Cited by 27 · see citing works
Ramakrishnaiah, Y., Maćešić, N., Webb, G. I., Peleg, A. Y., & Tyagi, S. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105816 · Cited by 31 · see citing works
Alatrany, A. S., Hussain, A. J., Mustafina, J., & Al‐Jumeily, D. (2022) · IEEE Access · doi:10.1109/access.2022.3182543 · Cited by 26 · see citing works
“estimations, as well as numerous regularisation algorithms, for example, early stopping [67].”
BackgroundTakahashi, M., Noda, K., Yoshida, K., Tsuchida, K., Yui, R., Nakazawa, T., Kurihara, S., Baba, A., Motegi, M., Yamamoto, K., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0273915 · Cited by 26 · see citing works
Liu, C., Li, Y., Fang, M., & Liu, F. (2023) · Service Business · doi:10.1007/s11628-023-00535-x · Cited by 26 · see citing works
“Prior literature only focused on free or fee consultation services (Li et al. 2019b; Liu et al. 2019; Mezemir 2014) instead of comparing them.”
BackgroundInfluentialDanilatou, V., Dimopoulos, D., Κωστούλας, Θ., & Douketis, J. D. (2024) · Thrombosis and Haemostasis · doi:10.1055/a-2299-4758 · Cited by 25 · see citing works
Ground Truth Or Dare: Factors Affecting The Creation Of Medical Datasets For Training AI
Zając, H. D., Avlona, N.-R., Kensing, F., Andersen, T. O., & Shklovski, I. (2023) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3600211.3604766 · Cited by 25 · see citing works
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“Various studies have found that the CNN is becoming a more widely used and effective technique for detecting cracks (Gopalakrishnan et al ., 2017; Mandal et al ., 2018; Liu et al ., 2019; Huyan et al ., 2020; Majidifard et al ., 2020; Liu and Wang, 2022).”
MethodInfluentialValidation Methods to Promote Real-world Applicability of Machine Learning in Medicine
Rafiq, R. B., Modave, F. P., Guha, S., & Albert, M. V. (2020) · International Conference on Digital Medicine and Image Processing (DMIP) · doi:10.1145/3441369.3441372 · Cited by 23 · see citing works
“Therefore, it is critical to teach the proper strategies for developing models for health care [8].”
BackgroundPredicting the length-of-stay of pediatric patients using machine learning algorithms
Medeiros, N. B., Fogliatto, F. S., ROCHA, M. K., & Tortorella, G. L. (2023) · International Journal of Production Research · doi:10.1080/00207543.2023.2235029 · Cited by 22 · see citing works
“The increasing availability of data related to health services contributed to the dissemination of ML in healthcare, presenting several opportunities to optimise processes and flows and improve the lives of patients and their experiences in hospitals (Khare et al. 2017; Chen, Liu, and Peng 2019).”
Boussi Rahmouni, H., Hassine, N. B. E. H., Chouchen, M., Ceylan, H. İ., Muntean, R. I., Bragazzi, N. L., & Dergaa, I. (2025) · Healthcare · doi:10.3390/healthcare13202553 · Cited by 25 · see citing works
“The Learn phase establishes intelligent clinical decision support foundations through comprehensive data integration and sophisticated pattern recognition [51].”
Prediction of Diabetes Risk based on Machine Learning Techniques
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“Using machine learning applications in healthcare is contributing a benefit to clinicians and patients for better decision making in less time and cost [4].”
BackgroundMethodInfluentialLazzarini, N., Filippoupolitis, A., Manzione, P., & Eleftherohorinou, H. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271227 · Cited by 20 · see citing works
“This approach was adopted to avoid introducing bias in our metrics by having a test set with a distribution of patient types significantly different to that of real-world populations [28].”
MethodStrasser, L. M., Anschuetz, W., Dennstädt, F., & Hastings, J. (2026) · JMIR Medical Education · doi:10.2196/81399 · Cited by 20 · see citing works
“When adequately implemented, LLMs exhibit substantial potential for supporting clinical practice [4,5].”
- Authors
- Chen, P.-H. C., Liu, Y., & Peng, L.
- Affiliations
- Google (United States) (US)
- Corresponding author
- Chen, Po-Hsuan Cameron
- Venue
- Nature Materials
- Published
- 2019-04-18
- Volume
- 18
- Issue
- 5
- Pages
- 410-414
- ISSN
- 1476-1122 (ISSN-L)
- 1476-4660
- OpenAlex
- W2936086693
- Semantic Scholar
- e13c152b9f9945578e55885bb4ea6a079769cec3
- Cited by
- 274
- References
- 25
- Citations by year
- 2026: 16 · 2025: 32 · 2024: 41 · 2023: 47 · 2022: 56 · 2021: 43 · 2020: 35 · 2019: 4
- FWCI
- 15.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Materials (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- machine learning models, health care, clinical decision support, model validation, model implementation, patient care
- MeSH terms
- Machine Learning, Algorithms, Cardiovascular Diseases, Diabetic Retinopathy, Diagnosis, Computer-Assisted, Diagnostic Errors, Echocardiography, Humans, Image Processing, Computer-Assisted, Medical Informatics, Models, Theoretical, Predictive Value of Tests, Prognosis, Radiotherapy Planning, Computer-Assisted, Reproducibility of Results, Drug Discovery
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