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How to develop machine learning models for healthcare
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“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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Significance of machine learning in healthcare: Features, pillars and applications
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“As it is pretty obvious, ML applications for healthcare wearables are a multidisciplinary field that requires standards about naming conventions, evaluation metrics, ethical reporting of research results, and clinical impact as suggested in [101] as these devices may directly affect human life.”
BackgroundAI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring
Wasilewski, T., Kamysz, W., & Gębicki, J. (2024) · Biosensors · doi:10.3390/bios14070356 · Cited by 328
“The ML model must be deployed and utilized in both retrospective and prospective studies, with the clinical impact assessed [146].”
MethodPerformance of ChatGPT and GPT-4 on Neurosurgery Written Board Examinations
Ali, R., Tang, O. Y., Connolly, I. D., Sullivan, P. L. Z., Shin, J. H., Fridley, J., Asaad, W. F., Cielo, D., Oyelese, A. A., Doberstein, C. E., et al. (2023) · Neurosurgery · doi:10.1227/neu.0000000000002632 · Cited by 272
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Rahmani, A. M., Yousefpoor, E., Yousefpoor, M. S., Mehmood, Z., Haider, A., Hosseinzadeh, M., & Naqvi, R. A. (2021) · Mathematics · doi:10.3390/math9222970 · Cited by 289
“They are known as the first generation of AI-based systems [20,21].”
BackgroundArtificial intelligence in digital breast pathology: Techniques and applications
Ibrahim, A., Gamble, P., Jaroensri, R., Abdelsamea, M. M., Mermel, C. H., Chen, P.-H. C., & Rakha, E. A. (2020) · The Breast · doi:10.1016/j.breast.2019.12.007 · Cited by 202
“First, the validation should be appropriate for the anticipated use cases [5,67].”
BackgroundSelf-citationFriha, O., Ferrag, M. A., Kantarcı, B., Çakmak, B., Ozgun, A., & Ghoualmi‐Zine, N. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3456549 · Cited by 226
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Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions
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“However, computational hypotheses generated by ML models must still be confirmed by subject matter experts in order to achieve enough precision for clinical decision-making [64].”
BackgroundAl-Tashi, Q., Saad, M. B., Muneer, A., Qureshi, R., Mirjalili, S., Sheshadri, A., Le, X., Vokes, N. I., Zhang, J. J., & Wu, J. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24097781 · Cited by 163
“Another category of metrics assesses calibration, which measures the consistency between predicted probabilities and actual probabilities [98].”
BackgroundXAI Framework for Cardiovascular Disease Prediction Using Classification Techniques
Guleria, P., Srinivasu, P. N., Ahmed, S., Almusallam, N., & Alarfaj, F. K. (2022) · Electronics · doi:10.3390/electronics11244086 · Cited by 157
Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images
Teixeira, L. O., Pereira, R. M., Bertolini, D. S., Oliveira, L. S., Nanni, L., Cavalcanti, G. D. C., & Costa, Y. M. G. (2021) · Sensors · doi:10.3390/s21217116 · Cited by 152
“We do not want to claim state-of-the-art classification results at this time for a couple of reasons: i) there are some initiatives to build a comprehensive COVID-19 CXR database; however, we still do not have a reliable database that can be used as a definitive benchmark; ii) in clinical practice, a small difference…”
BackgroundCrossnohere, N. L., Elsaid, M. I., Paskett, J., Bose‐Brill, S., & Bridges, J. F. P. (2022) · Journal of Medical Internet Research · doi:10.1016/j.jval.2022.04.1285 · Cited by 148
“Effectiveness is concerned with matters such as data quality and model fit during the development of AI models [48].”
Woodman, R. J., & Mangoni, A. A. (2023) · Aging Clinical and Experimental Research · doi:10.1007/s40520-023-02552-2 · Cited by 163
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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
“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
Bone, J. M., Childs, C. M., Menon, A. K., Póczos, B., Feinberg, A. W., LeDuc, P. R., & Washburn, N. R. (2020) · ACS Biomaterials Science & Engineering · doi:10.1021/acsbiomaterials.0c00755 · Cited by 117
Challenges of artificial intelligence in medicine and dermatology
Grzybowski, A., Jin, K., & Wu, H. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2023.12.013 · Cited by 115
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“Several guidelines for developing ML models with healthcare data have recently been published 24 – 28 .”
MethodTan, K. R., Seng, J. J. B., Kwan, Y. H., Chen, Y. J., Zainudin, S. B., Loh, D. H. F., Liu, N., & Low, L. L. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/19322968211056917 · Cited by 107
“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 122
“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 98
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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
“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
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“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 97
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Zhao, Y., Wood, E. P., Mirin, N., Vedanthan, R., Cook, S., & Chunara, R. (2020) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2021.04.016 · Cited by 93
Keleş, E., & Bağcı, U. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00941-5 · Cited by 99
Introduction to Artificial Intelligence and Machine Learning for Pathology
Harrison, J. H., Gilbertson, J. R., Hanna, M. G., Olson, N. H., Seheult, J. N., Sorace, J. M., & Stram, M. N. (2021) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2020-0541-cp · Cited by 91
“The general elements required for machine learning model development 66,119 are similar to those for statistical predictive models.”
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“…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 76
“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 71
“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 76
“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
“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
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
Ratti, E., & Graves, M. (2022) · AI and Ethics · doi:10.1007/s43681-022-00141-z · Cited by 61
“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
Wollborn, J., Hassenzahl, L. O., Reker, D., Staehle, H. F., Staehle, A. M., Baar, W., Kaufmann, K. B., Ulbrich, F., Wunder, C., Utzolino, S., et al. (2021) · Annals of Intensive Care · doi:10.1186/s13613-021-00965-8 · Cited by 60
Agliari, E., Adriano Barra, A., Barra, O. A., Fachechi, A., Vento, L. F., & Moretti, L. (2020) · Scientific Reports · doi:10.1038/s41598-020-64083-4 · Cited by 62
Castaldo, R., Cavaliere, C., Soricelli, A., Salvatore, M., Pecchia, L., & Franzese, M. (2021) · Journal of Medical Internet Research · doi:10.2196/22394 · Cited by 51
“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 55
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 52
Song, B., Zhou, M. L., & Zhu, J. (2023) · Medical Science Monitor · doi:10.12659/msm.938835 · Cited by 60
“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 52
Machine Learning Models for Inpatient Glucose Prediction
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“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
McCradden, M. D., Joshi, S. D., Anderson, J. A., & London, A. J. (2023) · Patterns · doi:10.1016/j.patter.2023.100864 · Cited by 59
Discriminating BTX Molecules by the Nonselective Metal Oxide Sensor-Based Smart Sensing System
Liu, H., Meng, G., Deng, Z., Nagashima, K., Wang, S., Dai, T., Li, L., Yanagida, T., & Fang, X. (2021) · ACS Sensors · doi:10.1021/acssensors.1c01704 · Cited by 48
GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows
Pati, S., Thakur, S. P., Hamamcı, İ. E., Baid, U., Baheti, B., Bhalerao, M., Güley, O., Mouchtaris, S., Lang, D., Thermos, S., et al. (2023) · Communications Engineering · doi:10.1038/s44172-023-00066-3 · Cited by 68
Machine-Learning Implementation in Clinical Anesthesia: Opportunities and Challenges
Char, D. S., & Burgart, A. M. (2020) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000004656 · Cited by 45
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 48
“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
“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
Chen, S., Qian, G., Ghanem, B., Wang, Y., Shu, Z., Zhao, X., Yang, L., Liao, X., & Zheng, Y. (2022) · Advanced Science · doi:10.1002/advs.202203460 · Cited by 42
Karapapas, C., & Goumopoulos, C. (2021) · Applied Sciences · doi:10.3390/app11178184 · Cited by 40
“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
“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
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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
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
Juang, W., Hsu, M.-H., Cai, Z.-X., & Chen, C.-M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0276501 · Cited by 36
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 44
“Chen, Liu, and Peng 2019).”
MethodSelf-citationNishida, N., & Kudo, M. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.594580 · Cited by 36
“The process of selecting a model ’ s architecture and training essentially involves a balance between model under fi tting and over fi tting (17).”
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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 43
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 38
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Cohen, R. Y., & Kovacheva, V. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3259395 · Cited by 41
“Yet, most sophisticated AI models exist only in high-profile publications, and only a few are implemented in clinical practice [4], [5].”
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Artificial Intelligence to Improve Antibiotic Prescribing: A Systematic Review
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“, 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
“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 32
“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
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“…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
“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].”
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Combining Clinical Symptoms and Patient Features for Malaria Diagnosis: Machine Learning Approach
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“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
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“In general, deep learning performs better with increasing amounts of data [69], although this amount can vary substantially for different approaches.”
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“There is increasing interest in the potential role that data science and machine learning can play in healthcare [1-5].”
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“estimations, as well as numerous regularisation algorithms, for example, early stopping [67].”
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“Prior literature only focused on free or fee consultation services (Li et al. 2019b; Liu et al. 2019; Mezemir 2014) instead of comparing them.”
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Ground Truth Or Dare: Factors Affecting The Creation Of Medical Datasets For Training AI
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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
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“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
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“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
“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].”
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“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].”
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“When adequately implemented, LLMs exhibit substantial potential for supporting clinical practice [4,5].”
Metadata
- 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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