Questions for Artificial Intelligence in Health Care

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697

Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data

Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.3763 · Cited by 1,395

Fully Automated Echocardiogram Interpretation in Clinical Practice: Feasibility and Diagnostic Accuracy

Zhang, J., Gajjala, S., Agrawal, P., Tison, G. H., Hallock, L. A., Beussink-Nelson, L., Lassen, M. H., Fan, E., Aras, M. A., Jordan, C., et al. (2018) · Circulation · doi:10.1161/circulationaha.118.034338 · Cited by 1,006

What This Computer Needs Is a Physician: Humanism and Artificial Intelligence

Verghese, A., Shah, N. H., & Harrington, R. A. (2018) · JAMA · doi:10.1001/jama.2017.19198 · Cited by 468

Clinical Implications and Challenges of Artificial Intelligence and Deep Learning

Stead, W. W. (2018) · JAMA · doi:10.1001/jama.2018.11029 · Cited by 250

The Role of the Sharing Economy and Artificial Intelligence in Health Care: Opportunities and Challenges

Wu, H., Chan, N., Zhang, C. J. P., & Ming, W. (2019) · Journal of Medical Internet Research · doi:10.2196/13469 · Cited by 24

Artificial intelligence in outcomes research: a systematic scoping review

Graili, P., Ieraci, L., Hosseinkhah, N., & Argent-Katwala, M. (2021) · Expert Review of Pharmacoeconomics & Outcomes Research · doi:10.1080/14737167.2021.1886083 · Cited by 25

A framework to identify ethical concerns with ML-guided care workflows: a case study of mortality prediction to guide advance care planning

Cagliero, D., Deuitch, N. T., Shah, N. H., Feudtner, C., & Char, D. S. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad022 · Cited by 23

Building materials in a circular economy

Dalton, T., Dorignon, L., Boehme, T., Kempton, L., Iyer‐Raniga, U., Oswald, D., Amirghasemi, M., & Moore, T. (2023) · AHURI Final Report · doi:10.18408/ahuri5328401 · Cited by 26

Meaningful Explanation Effect on User’s Trust in an AI Medical System: Designing Explanations for Non-Expert Users

Larasati, R. A., Liddo, A. D., & Motta, E. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3631614 · Cited by 24

Feasibility of artificial intelligence its current status, clinical applications, and future direction in cardiovascular disease

Sandeep, B., Liu, X., Huang, X., Wang, X., Mao, L., & Xiao, Z. (2023) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2023.102349 · Cited by 22

Strengths, weaknesses, opportunities and threats (SWOT) analysis of artificial intelligence adoption in nursing care

Rony, M. K. K., Akter, K., Debnath, M., Rahman, M. M., Johra, F. T., Akter, F., Das, D. C., Mondal, S., Das, M., Uddin, M. J., et al. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100113 · Cited by 28

What is morally at stake when using algorithms to make medical diagnoses? Expanding the discussion beyond risks and harms

de Boer, B., & Kudina, O. (2021) · Metamedicine · doi:10.1007/s11017-021-09553-0 · Cited by 26

Application of artificial intelligence in the assessment of thyroid eye disease (TED) - a scoping review

Chng, C. L., Zheng, K., Kwee, A. K., Lee, M. H., Ting, D. S. W., Wong, C. P., Hu, G., Ooi, B. C., & Kheok, S. W. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1300196 · Cited by 22

Deep-Learning–Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study

Yuen, V. L., Ran, A. R., Shi, J., Sham, K., Yang, D., Chan, V. T. T., Chan, R. N. C., Yam, J. C. S., Tham, C. C. Y., McKay, G. J., et al. (2021) · Translational Vision Science & Technology · doi:10.1167/tvst.10.11.16 · Cited by 20

Clinical Workflow Analysis, Process Redesign, and Quality Improvement

Ozkaynak, M., Unertl, K. M., Johnson, S. A., Brixey, J. J., & Haque, S. N. (2022) · Springer eBooks · doi:10.1007/978-3-030-93765-2_8 · Cited by 21

Executive summary of the 2019 ASHP Commission on Goals: Impact of artificial intelligence on healthcare and pharmacy practice

(2019) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/zxz205 · Cited by 20

Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy

Morris, A. H., Horvat, C. M., Stagg, B. C., Grainger, D. W., Lanspa, M. J., Orme, J. F., Clemmer, T. P., Weaver, L. K., Thomas, F. O., Grissom, C. K., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac143 · Cited by 21

Artificial intelligence in breast imaging: potentials and challenges

Li, J.-W., Sheng, D., Chen, J., You, C., Liu, S., Xu, H., & Chang, C. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acfade · Cited by 22

Learning Health System for Breast Cancer: Pilot Project Experience

Levine, M. N., Alexander, G., Sathiyapalan, A., Agrawal, A., & Pond, G. R. (2019) · JCO Clinical Cancer Informatics · doi:10.1200/cci.19.00032 · Cited by 20

Ascertaining Design Requirements for Postoperative Care Transition Interventions

Abraham, J., King, C. R., & Meng, A. (2021) · Applied Clinical Informatics · doi:10.1055/s-0040-1721780 · Cited by 20

Empowering nurses in the AI era: investigating the interplay between professionalism, AI readiness, and self-efficacy

Elbassal, N. A. M. M., El‐Sayed, A. A. I., & Elgamal, H. G. (2025) · BMC Nursing · doi:10.1186/s12912-025-03896-y · Cited by 26

Prediction of patient disposition: comparison of computer and human approaches and a proposed synthesis

Barak‐Corren, Y., Agarwal, I., Michelson, K. A., Lyons, T. W., Neuman, M. I., Lipsett, S. C., Kimia, A. A., Eisenberg, M. A., Capraro, A., Levy, J. A., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab076 · Cited by 18

Artificial Intelligence–Based Consumer Health Informatics Application: Scoping Review

Asan, O., Choi, E., & Wang, X. (2023) · Journal of Medical Internet Research · doi:10.2196/47260 · Cited by 25

Utilization of Artificial Intelligence Algorithms for Advanced Cancer Detection in the Healthcare Domain

Hariharan, M. G., Saranya, S. M., Velavan, P., Soji, E. S., Rajest, S. S., & Reddi, L. T. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-5946-4.ch021 · Cited by 20

Current issues regarding artificial intelligence in cancer and health care. Implications for medical physicists and biomedical engineers

Marcu, L. G., Boyd, C., & Bezak, E. (2019) · Health and Technology · doi:10.1007/s12553-019-00348-x · Cited by 22

Toward a Patient-Centered, Data-Driven Cardiology

Ribeiro, A. L., & Oliveira, G. M. M. D. (2019) · Arquivos Brasileiros de Cardiologia · doi:10.5935/abc.20190069 · Cited by 18

Addressing the challenges of artificial intelligence in medicine

Smith, M., & Jeffery, R. C. H. (2020) · Internal Medicine Journal · doi:10.1111/imj.15017 · Cited by 20

Algorithmic transparency and interpretability measures improve radiologists’ performance in BI-RADS 4 classification

Jungmann, F., Ziegelmayer, S., Lohöfer, F. K., Metz, S., Müller-Leisse, C., Englmaier, M., Makowski, M. R., Kaissis, G. A., & Braren, R. (2022) · European Radiology · doi:10.1007/s00330-022-09165-9 · Cited by 20

Diachronic and synchronic variation in the performance of adaptive machine learning systems: the ethical challenges

Hatherley, J. J., & Sparrow, R. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac218 · Cited by 18

Research progress and application of artificial intelligence in thyroid associated ophthalmopathy

Diao, J., Chen, X., Shen, Y., Li, J., Chen, Y., He, L., Chen, S., Mou, P., Ma, X., & Wei, R.-L. (2023) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2023.1124775 · Cited by 19

Pathologists’ first opinions on barriers and facilitators of computational pathology adoption in oncological pathology: an international study

Swillens, J. E. M., Nagtegaal, I. D., Engels, S., Lugli, A., Hermens, R., & van der Laak, J. A. W. M. (2023) · Oncogene · doi:10.1038/s41388-023-02797-1 · Cited by 18

healthAIChain: Improving Security and Safety using Blockchain Technology Applications in AI-based Healthcare Systems

Kshetri, N., Hutson, J., & Revathy, G. (2023) · International Conference on Innovative Mechanisms for Industry Applications · doi:10.1109/icimia60377.2023.10426362 · Cited by 20

Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy

Ji, Y., Chen, N., Liu, S., Yan, Z., Qian, H., Zhu, S., Zhang, J., Wang, M., Jiang, Q., & Yang, W. (2022) · Disease Markers · doi:10.1155/2022/3406890 · Cited by 20

Role of Trust in AI-Driven Healthcare Systems: Discussion from the Perspective of Patient Safety

Ünver, M. B., & Asan, O. (2022) · Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care · doi:10.1177/2327857922111026 · Cited by 18

Cognitive plausibility in voice-based AI health counselors

Kannampallil, T., Smyth, J. M., Jones, S., Payne, P., & Ma, J. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0278-7 · Cited by 17

Artificial Intelligence Revolutionizing Plastic Surgery Scientific Publications

Mir, M. A. (2023) · Cureus · doi:10.7759/cureus.40770 · Cited by 17

The challenges and opportunities of adopting artificial intelligence (AI) in Jordan’s healthcare transformation

Karim, S., Sandu, N., & Kayastha, M. (2021) · Global Journal of Information Technology Emerging Technologies · doi:10.18844/gjit.v11i2.6546 · Cited by 16

The pharmacist's guide to the future: Are we there yet?

Gregório, J., & Cavaco, A. M. D. N. (2020) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2020.05.029 · Cited by 22

Restoring Trust: The Need for Precision Medicine in Infectious Diseases, Public Health and Vaccines

Equils, O., Bakaj, A., Wilson-Mifsud, B., & Chatterjee, A. (2023) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2023.2234787 · Cited by 15

An in-depth review of AI-powered advancements in cancer drug discovery

Le, M. H. N., Nguyen, P. K., Nguyen, T. P. T., Nguyen, H. Q., Tam, D. N. H., Huynh, H. H., Huynh, P. K., & Le, N. Q. K. (2025) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2025.167680 · Cited by 15

Registered Trials on Artificial Intelligence Conducted in Emergency Department and Intensive Care Unit: A Cross-Sectional Study on ClinicalTrials.gov

Liu, G., Li, N., Chen, L., Yang, Y., & Zhang, Y. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.634197 · Cited by 15

An Update of AI and Radiomics in Precision Oncology: Insights from Liver Tumors as Case Models

Granata, V., Fusco, R., Setola, S. V., Santorsola, M., Ottaiano, A., Cerrone, M., Pizza, N., Ferrara, G., Zerunian, M., Caruso, D., et al. (2025) · Technology in Cancer Research & Treatment · doi:10.1177/15330338251387928 · Cited by 14

Controversies in artificial intelligence

Liu, T. Y. A., & Bressler, N. M. (2020) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000694 · Cited by 15

GCLP: An automated asthma detection model based on global chaotic logistic pattern using cough sounds

Kilic, M., Barua, P. D., Keleş, T., Yıldız, A. M., Tuncer, I., Doğan, Ş., Bayğın, M., Tuncer, T., Kuluöztürk, M., Tan, R., et al. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107184 · Cited by 16

Challenges and Facilitation Approaches for the Participatory Design of AI-Based Clinical Decision Support Systems: Protocol for a Scoping Review

Rambach, T., Gleim, P., Mandelartz, S., Heizmann, C., Kunze, C., & Kellmeyer, P. (2024) · JMIR Research Protocols · doi:10.2196/58185 · Cited by 15

Enabling individualised health in learning healthcare systems

Wongvibulsin, S., & Zeger, S. L. (2019) · BMJ Evidence-Based Medicine · doi:10.1136/bmjebm-2019-111190 · Cited by 14

Achieving a trusted, reliable, AI-ready infrastructure for military medicine and civilian trauma care

Crump, C., & Schlachta-Fairchild, L. (2020) · Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications · doi:10.1117/12.2557514 · Cited by 14

What is needed to mainstream artificial intelligence in health care?

Scott, I. A., Abdel‐Hafez, A., Barras, M. A., & Canaris, S. (2021) · Australian Health Review · doi:10.1071/ah21034 · Cited by 16

Health Technology for All: An Equity-Based Paradigm Shift Opportunity

MacDonald, J., Demiris, G., Shevin, M., Israni, S. T., Carney, T. J., & Cupito, A. (2022) · NAM Perspectives · doi:10.31478/202212a · Cited by 13

Artificial Intelligence in Medicine

Riaño, D., Szymon Wilk, A., & ten Teije, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21642-9 · Cited by 13

A Systematic Review

Iqbal, S., Ahmad, S., Bano, B., Akkour, K., Alghamdi, M. A. A., & Alothri, A. M. (2021) · International Journal of Intelligent Information Technologies · doi:10.4018/ijiit.2021010101 · Cited by 13

Prediction and classification of diabetes mellitus using genomic data

Awotunde, J. B., Ayo, F. E., Jimoh, R. G., Ogundokun, R. O., Matiluko, O. E., Oladipo, I. D., & AbdulRaheem, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821187-8.00009-5 · Cited by 14

Ageism and Artificial Intelligence: Protocol for a Scoping Review

Chu, C. H., Leslie, K., Shi, J., Nyrup, R., Bianchi, A., Khan, S. S., Rahimi, S. A., Lyn, A., & Grenier, A. M. (2022) · JMIR Research Protocols · doi:10.2196/33211 · Cited by 17

Artificial Intelligence Technology and its Relation to Staff Nurses' Professional Identity and Problem Solving Abilities

El-Monem, A. A., Rashed, S. E., & Hasanin, A. G. (2023) · International Egyptian Journal of Nursing Sciences and Research · doi:10.21608/ejnsr.2023.277890 · Cited by 13

Establishing a comprehensive artificial intelligence lifecycle framework for laboratory medicine and pathology: A series introduction

Garcia, C. A., Reed, K. A., Lantz, E., Day, P. L., Zarella, M. D., Hart, S. N., Will, E., Skiffington, J. G., Rice, M., Novak, D. A., et al. (2025) · American Journal of Clinical Pathology · doi:10.1093/ajcp/aqaf069 · Cited by 15

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

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

Mode of delivery and maternal vitamin D deficiency: an optimized intelligent Bayesian network algorithm analysis of a stratified randomized controlled field trial

Amiri, M., Rostami, M., Sheidaei, A., Fallahzadeh, A., & Tehrani, F. R. (2023) · Scientific Reports · doi:10.1038/s41598-023-35838-6 · Cited by 12

Power and perils of prediction in palliative care

Porter, A. S., Harman, S. M., & Lakin, J. R. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30318-4 · Cited by 12

Data Classification Model for COVID-19 Pandemic

Folorunso, S. O., Awotunde, J. B., Adeboye, N. O., & Matiluko, O. E. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-77302-1_6 · Cited by 13

The Ethics of Algorithms in Healthcare

Oxholm, C., Christensen, A.-M. S., & Nielsen, A. S. (2022) · Cambridge Quarterly of Healthcare Ethics · doi:10.1017/s0963180121000864 · Cited by 11

Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review

Alqahtani, M. Μ., Alanazi, A., Algarni, S. S., Aljohani, H., Alenezi, F. K., Alotaibi, T. F., Alotaibi, M. M., Alqahtani, M. K., Alahmari, M. A., Alwadeai, K. S., et al. (2024) · Interactive Journal of Medical Research · doi:10.2196/57271 · Cited by 12

Federated Learning-Based Model for Predicting Mortality: Systematic Review and Meta-Analysis

Tahir, N., Jung, C., Lee, S., Azizah, N., Ho, W., & Li, T. (2025) · Journal of Medical Internet Research · doi:10.2196/65708 · Cited by 16

Artificial Intelligence in Healthcare

Islam, S. (2021) · International Journal of Engineering Materials and Manufacture · doi:10.26776/ijemm.06.04.2021.08 · Cited by 11

Exploring the Characteristics of Physical Exercise in Students and the Path of Health Education

Peng, X., & Tang, L. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.663922 · Cited by 12

Artificial intelligence in medicine and dermatology

Woźniacka, A. J., Patrzyk, S., & Mikołajczyk, M. (2021) · Advances in Dermatology and Allergology · doi:10.5114/ada.2020.101259 · Cited by 11

Artificial Intelligence and Human Rights

(2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.001.0001 · Cited by 10

Clinical perspectives on AI integration: assessing readiness and training needs among healthcare practitioners

Masawi, T. J., Miller, E. I., Rees, D. J., & Thomas, R. A. (2025) · Journal of Decision System · doi:10.1080/12460125.2025.2458874 · Cited by 15

Why Real Citizens Would Turn to Artificial Leaders

Spatola, N., & MacDorman, K. F. (2021) · Digital Government Research and Practice · doi:10.1145/3447954 · Cited by 11

Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease – What the Clinician Needs to Know

Chen, D. J., Fulmer, C. G., Gordon, I. O., Syed, S., Stidham, R. W., Casteele, N. V., Qin, Y., Falloon, K., Cohen, B. L., Wyllie, R., et al. (2021) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjab169 · Cited by 10

Insurance payment for artificial intelligence technology: Methods used by a stroke artificial intelligence system and strategies to qualify for the new technology add-on payment

Murray, N. M., Phan, P., Hager, G., Menard, A., Chin, D., Liu, A., & Hui, F. K. (2022) · The Neuroradiology Journal · doi:10.1177/19714009211067408 · Cited by 10

Perspective Toward Machine Learning Implementation in Pediatric Medicine: Mixed Methods Study

Alexander, N., Aftandilian, C. C., Guo, L. L., Plenert, E., Posada, J., Fries, J., Fleming, S. L., Johnson, A. E. W., Shah, N. H., & Sung, L. (2022) · JMIR Medical Informatics · doi:10.2196/40039 · Cited by 10

A Cross-Sectional Online Study of the Use of Artificial Intelligence in Nursing Research as Perceived by Nursing Students

Algunmeeyn, A., & MRAYYAN, M. T. (2025) · SAGE Open Nursing · doi:10.1177/23779608251330866 · Cited by 10

Assessing Clinicians’ Legal Concerns and the Need for a Regulatory Framework for AI in Healthcare: A Mixed-Methods Study

Alanazi, A. (2025) · Healthcare · doi:10.3390/healthcare13131487 · Cited by 11

ETHICS of AI Adoption and Deployment in Health Care: Progress, Challenges, and Next Steps

Oleribe, O. O., Taylor-Robinson, A. W., Chimezie, C., & Taylor-Robinson, S. D. (2025) · JMIR AI · doi:10.2196/67626 · Cited by 9

Data-driven machine learning: A new approach to process and utilize biomedical data

., K., Srivastava, A., & Jha, S. S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-99864-2.00017-2 · Cited by 9

Automated Segmentation of Sacral Chordoma and Surrounding Muscles Using Deep Learning Ensemble

Boussioux, L., Ma, Y., Thomas, N. K., Bertsimas, D., Shusharina, N., Pursley, J., Chen, Y., DeLaney, T. F., Qian, J. M., & Bortfeld, T. R. (2023) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2023.03.078 · Cited by 9

Automatic Video Analysis Framework for Exposure Region Recognition in X-Ray Imaging Automation

Sun, J., Wu, Z., Yu, Z., Chen, H., Du, C., Liang, X., Zhong, J., Feng, J. J., Coatrieux, G., Coatrieux, J.-L., et al. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3172369 · Cited by 9

Evolution in fracture risk assessment: artificial versus augmented intelligence

Hans, D. B., Shevroja, E., & Leslie, W. D. (2021) · Osteoporosis International · doi:10.1007/s00198-020-05737-x · Cited by 9

Factors Associated with a Recommendation for Operative Treatment for Fracture of the Distal Radius

Langerhuizen, D. W. G., Janssen, S. J., Kortlever, J. T. P., Ring, D., Kerkhoffs, G. M. M. J., Jaarsma, R. L., & Doornberg, J. N. (2021) · Journal of Wrist Surgery · doi:10.1055/s-0041-1725962 · Cited by 8

Human-AI Collaboration in Healthcare

Gurusamy, V., Muniasamy, A., Gauthaman, S. A., & Abdulla, H. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2787-7.ch012 · Cited by 9

Machine Learning Feasibility in Cochlear Implant Speech Perception Outcomes—Moving Beyond Single Biomarkers for Cochlear Implant Performance Prediction

Shew, M. A., Pavelchek, C., Michelson, A. P., Ortmann, A. J., Lefler, S. M., Walia, A., Durakovic, N., Phillips, A., Rejepova, A., Herzog, J. A., et al. (2025) · Ear and Hearing · doi:10.1097/aud.0000000000001664 · Cited by 9

Ethical AI in the re-ablement of older people: Opportunities and challenges

Lanne, M., & Leikas, J. (2021) · Gerontechnology · doi:10.4017/gt.2021.20.2.26-473.11 · Cited by 9

Bridging the Digital Divide

Marzo, R. R. (2024) · Advances in business strategy and competitive advantage book series · doi:10.4018/979-8-3693-6720-9.ch006 · Cited by 7

Cheminformatics and Bioinformatics at the Interface with Systems Biology

Kaushik, A. C., Mehmood, A., Wei, D., Nawab, S., Sahi, S., & Kumar, A. (2023) · Royal Society of Chemistry eBooks · doi:10.1039/9781839166037 · Cited by 7

Introduction to artificial intelligence for cardiovascular clinicians

Chang, A. C., & Limon, A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90534-3.00010-x · Cited by 12

Investigating the efficiency of medical and health resources and its influencing factors in the Yangtze River Delta urban agglomerations: based on the undesirable super-efficiency SBM-Malmquist-Tobit model

Chen, Y. W., Li, Q., Zhang, Y., Zhang, X., & Zheng, H. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1527424 · Cited by 7

Artificial Intelligence in Clinical Toxicology

Sinha, M., Praveen, G. B., Sachan, D. K., & Parthasarathi, R. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_137-1 · Cited by 8

Learning from data: A recurring feature on the science and practice of data‐driven learning health systems

Embí, P. J., Payne, P., & Friedman, C. P. (2021) · Learning Health Systems · doi:10.1002/lrh2.10302 · Cited by 7

Advancing infection prevention and control through artificial intelligence: a scoping review of applications, barriers, and a decision-support checklist

Gastaldi, S., Tartari, E., Satta, G., & Allegranzi, B. (2025) · Antimicrobial Stewardship & Healthcare Epidemiology · doi:10.1017/ash.2025.10191 · Cited by 6

Real-World Outcomes of Patients with Advanced Epidermal Growth Factor Receptor-Mutated Non-Small Cell Lung Cancer in Canada Using Data Extracted by Large Language Model-Based Artificial Intelligence

Moulson, R., Law, J. H., Sacher, A. G., Liu, G., Shepherd, F. A., Bradbury, P. A., Eng, L., Iczkovitz, S., Abbie, E., Elia-Pacitti, J., et al. (2024) · Current Oncology · doi:10.3390/curroncol31040146 · Cited by 6

Artificial Intelligence and Fair Trial Rights

Molbæk-Steensig, H., & Quemy, A. (2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.003.0018 · Cited by 7

Needs Assessment Survey Identifying Research Processes Which may be Improved by Automation or Artificial Intelligence: ICU Community Modeling and Artificial Intelligence to Improve Efficiency (ICU-Comma)

Lau, V. I., Binnie, A., Basmaji, J., Baig, N., Opgenorth, D. L., Cameron, S., O’Hearn, K., McDonald, E., Senaratne, J. M., Sligl, W., et al. (2021) · Journal of Intensive Care Medicine · doi:10.1177/08850666211064844 · Cited by 6

Artificial Intelligence and Freedom of Expression

Gregorio, G. D., & Dunn, P. (2023) · Oxford University Press eBooks · doi:10.1093/law/9780192882486.003.0005 · Cited by 6

Perspectives on Assurance Case Development for Retinal Disease Diagnosis Using Deep Learning

Picardi, C., & Habli, I. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21642-9_46 · Cited by 6

From Clinic to Computer and Back Again: Practical Considerations When Designing and Implementing Machine Learning Solutions for Pediatrics

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