Cited by 274Machine Learning in HealthcareArtificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

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How to develop machine learning models for healthcare

Chen, Po-Hsuan Cameron, Liu, Yun, Peng, Lily·2019·Nature Materials

DOI 10.1038/s41563-019-0345-0

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Noel, C. W., Sutradhar, R., Conn, L. G., Forner, D. W. A., Chan, W. C., Fu, R., Hallet, J. I., Coburn, N. G., & Eskander, A. E. (2022) · JAMA Otolaryngology–Head & Neck Surgery · doi:10.1001/jamaoto.2022.1629 · Cited by 18
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“…it is critical to develop robust techniques that can not only yield good performance on given tasks but also provide efﬁciency, reliability, and explainability (Szolovits & Pauker, 1978; Szolovits, 1982), to improve the likelihood of their practical clinical deployment ( Chen et al., 2019b).”
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“Machine learning (ML) is an expanding ﬁeld that has immense potential to transform the approach we take toward tackling complex problems across various industries, such as ﬁnance [21,22], healthcare [23,24], weather [25,26], and others.”
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Artificial intelligence for precision medicine
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Artificial Intelligence for Understanding Imaging, Text, and Data in Gastroenterology.
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Machine Learning in Pituitary Surgery
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Privacy-Preserving and Verifiable Cloud-Aided Disease Diagnosis and Prediction With Hyperplane Decision-Based Classifier
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XGBoost machine learning algorithm for predicting unplanned readmission in elderly patients with coronary heart disease
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Artificial intelligence and machine learning for the healthcare sector
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“The adoption of structured processes and validated methods guarantees an efficient development process and more reliable models (59, 60).”
Method
Design of Efficient Quadrature Scheme in Finite Element Using Deep Learning
Chinchkar, R., Nath, D., & Gautam, S. S. (2023) · Lecture notes in mechanical engineering · doi:10.1007/978-981-99-3033-3_3 · Cited by 6
Big data research is everyone's research—Making epilepsy data science accessible to the global community: Report of the ILAE big data commission
Josephson, C. B., Aronica, E., Beniczky, S., Boyce, D. M., Cavalleri, G. L., Denaxas, S., French, J. A., Jehi, L. E., Koh, H. Y., Kwan, P., et al. (2024) · Epileptic Disorders · doi:10.1002/epd2.20288 · Cited by 5
Sex estimation with parameters of the facial canal by computed tomography using machine learning algorithms and artificial neural networks
Seçgin, Y., Kaya, S., Harmandaoğlu, O., Öztürk, O., Şenol, D., Önbaş, Ö., & Yılmaz, N. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01834-7 · Cited by 5
“The achievement of fast, reliable, and accurate results, as well as the usability of digitized health data, has led to an increase in studies evaluating the application areas and effectiveness of ML algorithms [26].”
Should we synthesize more than we need: impact of synthetic data generation for high-dimensional cross-sectional medical data
Pilgram, L., Kababji, S. E., Liu, D., & Emam, K. E. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf169 · Cited by 5
“20,21 For each of them, we specified a binary classification task (see online Appendix A) and defined the variables that would be required to perform this task as core variables (ie, the predictors and the outcome variables).”
Advancements in Fire Alarm Detection using Computer Vision and Machine Learning: A Literature Review
Ridhani, M. F., & Mahmudy, W. F. (2023) · Journal of Information Technology and Computer Science · doi:10.25126/jitecs.202382554 · Cited by 5
“It is very important to choose input features from the relevant data because it will affect the results [18] and using a method that fits the data will also have a significant effect on the model that is built [57].”
Machine learning for spelling acquisition: How accurate is the prediction of specific spelling errors in German primary school students?
Böhme, R., Coors, S., Oster, P., Munser-Kiefer, M., & Hilbert, S. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100233 · Cited by 13
“Even though ML as an analysis tool in research has been embraced by many fields of behavioural sciences – such as psychology (Stachl et al., 2020) or the health sciences (Chen, Liu, & Peng, 2019) – the use of ML models in the educational sciences has to date been much sparser.”
Decentralized Learning with Virtual Patients for Medical Diagnosis of Diabetes
Takahashi, Y., Chang, H., Nakai, A., Kagawa, R., Ando, H., Imakura, A., Okada, Y., Tsurushima, H., Suzuki, K., & Sakurai, T. (2021) · SN Computer Science · doi:10.1007/s42979-021-00564-1 · Cited by 5
“While machine learning is one of the critical techniques for analyzing medical data [5, 6, 34, 40, 43, 45], patients’ privacy must be protected in the learning process.”
Background
Applications of neural networks in liver transplantation
Meng, J., Liu, Z., & Xu, X. (2022) · iLiver · doi:10.1016/j.iliver.2022.07.002 · Cited by 5
Artificial Intelligence and the Future of Computer-Assisted Medical Research and Writing
Dutton, J. J. (2023) · Ophthalmic Plastic and Reconstructive Surgery · doi:10.1097/iop.0000000000002420 · Cited by 5
Highly efficient secure linear algebra for private machine learning classifications over malicious clients in the post-quantum world
Kjamilji, A., & Güney, O. B. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101718 · Cited by 5
“Namely, different hospitals/clinics own already trained ML models and use/offer them to their patients/clients for accurate diagnosis of many diseases (Kjamilji et al., 2021b; Liu et al., 2017; Kjamilji et al., 2020; Chen et al., 2019).”
Framework for Personalized Chronic Pain Management: Harnessing AI and Personality Insights for Effective Care
Kumar, A., Seewal, R., Jain, D., & Kaur, R. (2024) · Journal of Artificial Intelligence and Technology · doi:10.37965/jait.2024.0457 · Cited by 6
“Model training and validation are pivotal stages in the development of machine learning models for multimodal personality assessment [31].”
Background
Predicting Dosage of Immunosuppressant Drugs After Kidney Transplantation Using Machine Learning
Panda, K., & Mazumder, A. (2023) · IEEE MIT Undergraduate Research Technology Conference (URTC) · doi:10.1109/urtc60662.2023.10534958 · Cited by 5
“In this initial study, a limited set of patient variables such as age, gender, height, weight, kidney type, and donor type were utilized [22].”
Method
Cross-Domain Co-Author Recommendation Based on Knowledge Graph Clustering
Munna, M. T. A., & Delhibabu, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-73280-6_62 · Cited by 5
ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE
Dudin, O., Mintser, O. P., & Sulaieva, O. (2021) · Proceedings of the Shevchenko Scientific Society Medical Sciences · doi:10.25040/ntsh2021.02.07 · Cited by 6
Thoracic Disease Identification and Localization using Distance Learning and Region Verification
Zhang, C., Chen, F., & Chen, Y. (2020) · British Machine Vision Conference (BMVC) · doi:10.5244/c.34.192 · Cited by 5
“Several datasets have been released for disease classification from chest X-ray images, including [3, 12, 14, 29], and the use of deep learning models in combination with the datasets has resulted in much progress [4, 5, 16, 23, 29, 30].”
Method
Automated diagnosis of chronic obstructive pulmonary disease using deep learning applied to electrocardiograms
Vaid, A., Sharma, J., Jiang, J. J., Lampert, J. M., Sawant, A. S., Argulian, E., Lerakis, S., Tandon, P., Kovatch, P., Powell, C. A., et al. (2026) · EBioMedicine · doi:10.1016/j.ebiom.2025.106066 · Cited by 6
“28 At an appropriately low threshold (with resulting high sensitivity), deep learning models may be utilised as “screening” tools.”
A Systematic Review of Medical Expert Systems for Cardiac Arrest Prediction
Kaur, I., Ahmad, T., & Doja, M. N. (2023) · Current Bioinformatics · doi:10.2174/0115748936251658231002043812 · Cited by 4
Machine learning applied to inverse systems design
de Moura, U. C., Ros, F. D., Zibar, D., Brusin, A. M. R., & Carena, A. (2022) · International Conference on Optical Network Design and Modeling (ONDM) · doi:10.23919/ondm54585.2022.9782836 · Cited by 4
“These ML models have been successfully applied to address some of society’s biggest and most complex problems in different ﬁelds such as business [1], healthcare [2], and astronomy [3].”
Method
Artificial intelligence-enabled Bioprinting 5.0
Bai, L., Zhang, Y., Wang, S., Liu, J., Liu, Y., & Su, J. (2025) · Bio-Design and Manufacturing · doi:10.1631/bdm.2400354 · Cited by 4
Self-citation
Non-destructive classification of type 2 diabetic teeth using LIBS-derived elemental biomarkers and machine learning
Dastageer, M. M., Siraj, K., Senesi, N., Israr, M. A., & Haq, S. U. (2026) · Analytica Chimica Acta · doi:10.1016/j.aca.2026.345615 · Cited by 3
Tutorial: guidance on the use of large language models for medical research
Jin, Q., Wan, N., Leaman, R., Tian, S., Wang, Z., Yang, Y., Wang, Z., Xiong, G., Lai, P.-T., Zhu, Q. L., et al. (2026) · Nature Protocols · doi:10.1038/s41596-026-01408-z · Cited by 15
The Benefits of Continuous Health Data Monitoring in Cardiovascular Diseases and Dementia
Christogianni, A. (2023) · Advances in information quality and management · doi:10.4018/978-1-6684-7366-5.ch014 · Cited by 4
The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review
Handra, J., James, H., Mbilinyi, A., Moller‐Hansen, A., O'Riley, C., Andrade, J. G., Deyell, M. W., Hague, C. J., Hawkins, N. M., Ho, K., et al. (2024) · JMIR Cardio · doi:10.2196/60697 · Cited by 5
Pioneering Healthcare Innovations with the Convergence of Blockchain, AI, and the Internet of Medical Things (IoMT)
Goel, A., & Neduncheliyan, S. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-75861-4_17 · Cited by 3
Comparing the performance of ChatGPT-3.5-Turbo, ChatGPT-4, and Google Bard with Iranian students in pre-internship comprehensive exams
Zare, S., Vafaeian, S., Amini, M., Farhadi, K., Vali, M., & Golestani, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-79335-w · Cited by 3
Advances in Machine Learning Models for Healthcare Applications: A Precise and Patient-Centric Approach
Parashar, B., Sridhar, S. B., Kalpana, K., Malviya, R., Prajapati, B. G., & Uniyal, P. (2025) · Current Pharmaceutical Design · doi:10.2174/0113816128353371250119121315 · Cited by 4
Optimised denoising-based deep learning classification for evaluating concrete surface cracks
Matarneh, S. T., Elghaish, F., Rahimian, F. P., Abdellatef, E., Tezel, A., Mahamadu, A., & Abdelmegid, M. (2025) · Journal of Information Technology in Construction · doi:10.36680/j.itcon.2025.064 · Cited by 3
A Study of COVID ‐19 Information Behaviors among Black Americans
Stewart, B. J., Ju, B., & Walker, J. J. (2023) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.797 · Cited by 3
“Based on this data, public health campaigns can employ tailored messaging and communication platforms to tackle their concerns and enhance vaccination adoption (Chen, Liu, & Peng, 2019).”
Background
Machine learning for predictive analytics
Kashyap, S., Corey, K., Kansal, A., & Sendak, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820273-9.00003-8 · Cited by 3
Modeling Drivers of Machine Learning in Health care Using Interpretive Structural Modeling Approach
Gupta, P., & Mehra, R. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-15-9829-6_35 · Cited by 3
Serverless on FHIR: Deploying machine learning models for healthcare on the cloudPreprint
Eapen, B. R., Sartipi, K., & Archer, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.04748 · Cited by 10
“Many of the sophisticated and expensive ML models end up as theoretical artifacts in academic publications with minimal practical utility [2].”
Background
A physician-in-the-loop approach by means of machine learning for the diagnosis of lymphocytosis in the clinical laboratory
Bigorra, L. (2022) · Repositori digital de la UPF (Universitat Pompeu Fabra) · Cited by 3
Applicability Area: A novel utility-based approach for evaluating predictive models, beyond discriminationPreprint
Liu, S., Wei, S., & Lehmann, H. P. (2023) · medRxiv · doi:10.1101/2023.07.06.23292124 · Cited by 3
“Beyond calls for evaluation, in general 3 , leaders in machine learning (ML) in medicine call for considering “clinically acceptable performance characteristics for the targeted application” 4 .”
Background
ATCON: Attention Consistency for Vision Models
Mirzazadeh, A., Dubost, F., Pike, M., Maniar, K., Zuo, M., Lee‐Messer, C., & Rubin, D. L. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00192 · Cited by 3
“In many real-world problems such as healthcare, labeled training data can be scarce [19, 4], which can drive models to learn partially-incorrect representations and overfit to their training set [32, 3].”
Background
A Pathway for Integrating Artificial Intelligence for Neonatal Neuromonitoring: From Code to Crib Side
Kułakowski, P., Alarcón, A., Škorić, T., Seoni, S., O’Sullivan, M., & O’Toole, J. M. (2025) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2025.114708 · Cited by 2
Magnitude and Impact of Hallucinations in Tabular Synthetic Health Data on Prognostic Machine Learning Models: Validation Study
Pilgram, L., Kababji, S. E., Liu, D., & Emam, K. E. (2025) · Journal of Medical Internet Research · doi:10.2196/77893 · Cited by 4
“These datasets cover a wide range of typical characteristics (eg, class imbalance, missing values, and noisy variables) that are encountered when working with real-world health data [38,39].”
Evaluation of model performance in predicting sepsis after intestinal obstruction surgery: a multicenter retrospective study
Chen, C., Yang, J., Zhang, L., Huang, P., Li, D., Xu, J., Yang, D., Shu, H., Zhou, S., & Hei, Z. (2025) · Annals of Medicine · doi:10.1080/07853890.2025.2578429 · Cited by 2
Artificial Intelligence in Pediatric Ultrasound: An Update and Future Applications
Kuang, C., Jiang, Z., Wang, Y., Fang, A., Zhang, H., Liu, H., & Ye, Z. (2025) · BIO Integration · doi:10.15212/bioi-2025-0130 · Cited by 3
“Furthermore, performance metrics, such as sensitivity, specificity, and accuracy, are widely used to evaluate AI models; these metrics depend on specific threshold settings [31].”

Authors: Chen, P.-H. C., Liu, Y., & Peng, L.
Affiliations: Google (United States) (US)
Corresponding author: Chen, Po-Hsuan Cameron
ORCID: Chen, Po-Hsuan Cameron
Liu, Yun
Peng, Lily
Venue: Nature Materials
Publisher: Springer Science and Business Media LLC
Published: 2019-04-18
Volume: 18
Issue: 5
Pages: 410-414
ISSN: 1476-1122 (ISSN-L)
1476-4660
DOI: 10.1038/s41563-019-0345-0
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
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
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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