The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Artificial Intelligence Transforms the Future of Health Care
Open accessAbstract
Life sciences researchers using artificial intelligence (AI) are under pressure to innovate faster than ever. Large, multilevel, and integrated data sets offer the promise of unlocking novel insights and accelerating breakthroughs. Although more data are available than ever, only a fraction is being curated, integrated, understood, and analyzed. AI focuses on how computers learn from data and mimic human thought processes. AI increases learning capacity and provides decision support system at scales that are transforming the future of health care. This article is a review of applications for machine learning in health care with a focus on clinical, translational, and public health applications with an overview of the important role of privacy, data sharing, and genetic information.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“ML algorithms, which analyze extensive clinical data and identify underlying patterns and trends, offer robust support for disease prediction and risk assessment [8, 9].”
From Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome (2025) · cites this paper as [8]
In the citing paper
“If patients are well informed about the way their information will be collected and used during their medical treatments, they will be able to decide their preferences regarding the privacy protection (Noorbakhsh-Sabet et al., 2019).”
From Responsible AI for Digital Health: a Synthesis and a Research Agenda (2023) · cites this paper as Noorbakhsh-Sabet et al., 2019
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Multiple imputation using chained equations: Issues and guidance for practice
White, I. R., Royston, P., & Wood, A. M. (2011) · Statistics in Medicine · doi:10.1002/sim.4067 · Cited by 9,792
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
Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M., & Carpenter, J. R. (2009) · BMJ · doi:10.1136/bmj.b2393 · Cited by 7,299
Lip, G. Y. H., Nieuwlaat, R., Pisters, R., Lane, D. A., & Crijns, H. J. G. M. (2010) · Chest · doi:10.1378/chest.09-1584 · Cited by 6,879
Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837
Predicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652
Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466
Machine Learning Applications in Cancer Prognosis and Prediction
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.11.005 · Cited by 3,338
Spironolactone for heart failure with preserved ejection fraction
Pitt, B., Pfeffer, M. A., Assmann, S. F., Boineau, R., Anand, I. S., Claggett, B., Clausell, N., Desai, A. S., Diaz, R., Fleg, J. L., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1313731 · Cited by 2,809
Machine learning for medical imaging
Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L. (2017) · RadioGraphics · doi:10.1148/rg.2017160130 · Cited by 1,672
Research works printed but not linked: 3
- Abedi, 2017, Novel screening tool for stroke using artificial neural network, J Stroke., 48, 1678, 10.1161/STROKEAHA.117.017033 · DOI
- Taylor, 2017, Celgene cans phase 3 trial of $710M Crohn's drug GED-0301, Fierce Biotech
- Chen X, Member S, Lin X. Big data deep learning: challenges and perspectives. 2014;2. doi: 10.1109/ACCESS.201.2325029. · DOI · Paper page
Cited by
Sorted by citations
Items 101–200 of 626
Smith, F. (2022) · Springer eBooks · doi:10.1007/978-3-031-13388-6_12 · Cited by 40
Wang, S., Wu, D., Li, G., Zhang, Z., Xiao, W., Li, R., Qiao, A., Jin, L., & Liu, H. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2022.1094743 · Cited by 40
Current applications of artificial intelligence in vascular surgery
Fischer, U. M., Shireman, P. K., & Lin, J. C. (2021) · Seminars in Vascular Surgery · doi:10.1053/j.semvascsurg.2021.10.008 · Cited by 40
Artificial Intelligence Bias in Health Care: Web-Based Survey
Vorisek, C. N., Stellmach, C., Mayer, P. J., Klopfenstein, S. A. I., Bures, D., Diehl, A., Henningsen, M., Ritter, K., & Thun, S. (2023) · Journal of Medical Internet Research · doi:10.2196/41089 · Cited by 45
A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications
Upreti, D., Yang, E., Kim, H., & Seo, C. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.048932 · Cited by 46
AI Chatbots as Sources of STD Information: A Study on Reliability and Readability
Yıldız, H. A., & Söğütdelen, E. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02178-z · Cited by 38
Predicting In-Hospital Mortality at Admission to the Medical Ward: A Big-Data Machine Learning Model
Soffer, S., Klang, E., Barash, Y., Grossman, E., & Zimlichman, E. (2020) · The American Journal of Medicine · doi:10.1016/j.amjmed.2020.07.014 · Cited by 38
The Beginning of a New Era: Artificial Intelligence in Healthcare
Kumar, A., Gadag, S., & Nayak, U. Y. (2020) · Advanced Pharmaceutical Bulletin · doi:10.34172/apb.2021.049 · Cited by 39
van Berkel, N., Bellio, M., Skov, M. B., & BLANDFORD, A. E. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3571815 · Cited by 42
Li, W., Chen, J., Chen, F., Liang, J., & Yu, H. (2023) · Aesthetic Plastic Surgery · doi:10.1007/s00266-023-03660-0 · Cited by 36
Malnutrition, Health and the Role of Machine Learning in Clinical Setting
Sharma, V., Sharma, V., Khan, A., Wassmer, D. J., Schoenholtz, M. D., Hontecillas, R., Bassaganya-Riera, J., Zand, R., & Abedi, V. (2020) · Frontiers in Nutrition · doi:10.3389/fnut.2020.00044 · Cited by 36
Self-citationMähler, M., Auza, C., Albesa, R., Melus, C., & Wu, J. A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820239-5.00010-3 · Cited by 38
Song, D., Topriceanu, C., Ilie-Ablachim, D. C., Kinali, M., & Bisdas, S. (2021) · Neuroradiology · doi:10.1007/s00234-021-02774-z · Cited by 34
Oehrlein, E. M., Schoch, S. C., Burcu, M., McBeth, J. F., Bright, J. L., Pashos, C. L., Willke, R. J., Love, T. R., Mattingly, T. J., & Perfetto, E. M. (2022) · Value in Health · doi:10.1016/j.jval.2022.04.1738 · Cited by 38
Chowdhury, M., Cervantes, E. G., Chan, W.-Y., & Seitz, D. P. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.738466 · Cited by 39
Baron, R., & Haick, H. (2024) · ACS Sensors · doi:10.1021/acssensors.4c00636 · Cited by 37
Sharma, G., Yaffe, M. J., Ghadiri, P., Gandhi, R., Pinkham, L., Gore, G., & Abbasgholizadeh-Rahimi, S. (2025) · JMIR Mental Health · doi:10.2196/70438 · Cited by 38
Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms
Hamdaoui, H. E., Boujraf, S., Chaoui, N. E. H., Alami, B., & Maâroufi, M. (2021) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v17i11.24781 · Cited by 40
Shawli, L., Alsobhi, M. G., Chevidikunnan, M. F., Rosewilliam, S., Basuodan, R., & Khan, F. R. (2024) · Musculoskeletal Science and Practice · doi:10.1016/j.msksp.2024.103152 · Cited by 39
Artificial intelligence in acute respiratory distress syndrome: A systematic review
Rashid, M., Ramakrishnan, M., Chandran, V. P., Siddeshappa, N., Nair, S., Shanbhag, V., & Thunga, G. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102361 · Cited by 36
Okwor, I. A., Hitch, G., Hakkim, S., Akbar, S., Sookhoo, D., & Kainesie, J. (2024) · AI · doi:10.3390/ai5040095 · Cited by 32
Kedra, J., Davergne, T., Braithwaite, B., Servy, H., & Gossec, L. (2021) · Expert Review of Clinical Immunology · doi:10.1080/1744666x.2022.2017773 · Cited by 33
Artificial Intelligence in Medical Education
Garg, T. (2020) · The American Journal of Medicine · doi:10.1016/j.amjmed.2019.08.017 · Cited by 32
González‐Nóvoa, J. A., Campanioni, S., Busto, L., Fariña, J., Rodriguez-Andina, J. J., Vila, D., Íñiguez, A., & Veiga, C. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20043455 · Cited by 30
Current Applications of Artificial Intelligence in Billing Practices and Clinical Plastic Surgery
Zhu, C., Attaluri, P. K., Wirth, P. J., Shaffrey, E. C., Friedrich, J. B., & Rao, V. K. (2024) · Plastic & Reconstructive Surgery Global Open · doi:10.1097/gox.0000000000005939 · Cited by 32
The global geography of artificial intelligence in life science research
Schmallenbach, L., Bärnighausen, T., & Lerchenmueller, M. (2024) · Nature Communications · doi:10.1038/s41467-024-51714-x · Cited by 35
Metabolomic Laboratory-Developed Tests: Current Status and Perspectives
Lichtenberg, S., Trifonova, O. P., Maslov, D. L., Balashova, E. E., & Lokhov, P. G. (2021) · Metabolites · doi:10.3390/metabo11070423 · Cited by 34
Harnessing the Power of AI in Pharmacokinetics and Pharmacodynamics: A Comprehensive Review
Pawar, V. T., PATIL, A. R., Tamboli, F. A., Gaikwad, D., Mali, D. P., & Shinde, A. J. (2023) · International Journal of Pharmaceutical Quality Assurance · doi:10.25258/ijpqa.14.2.31 · Cited by 32
The Application of Artificial Intelligence in Alzheimer's Research
Zhao, Q., Xu, H., Li, J., Akhtar, F., & Qiao, L. (2023) · Tsinghua Science & Technology · doi:10.26599/tst.2023.9010037 · Cited by 34
Schofield, I., Brodbelt, D. C., Kennedy, N., Niessen, S., Church, D. B., Geddes, R. F., & O'Neill, D. G. (2021) · Scientific Reports · doi:10.1038/s41598-021-88440-z · Cited by 36
Cascella, M., Coluccia, S., Monaco, F., SCHIAVO, D., Nocerino, D., Grizzuti, M., Romano, M. C., & Cuomo, A. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11185484 · Cited by 30
AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook
Huang, Y., Cheung, C. Y.-L., Li, D., Tham, Y. C., Sheng, B., Cheng, C., Wang, Y. X., & Wong, T. Y. (2023) · Eye · doi:10.1038/s41433-023-02724-4 · Cited by 38
AI Enabled Internet of Medical Things in Smart Healthcare
Jayachitra, S., Prasanth, A., Hariprasath, S., Begam, R. B., & Madiajagan, M. (2023) · Engineering cyber-physical systems and critical infrastructures · doi:10.1007/978-3-031-31952-5_7 · Cited by 29
Artificial Intelligence at the Service of Precision Medicine
Abbaoui, W., Retal, S., Kharmoum, N., & Ziti, S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-35248-5_9 · Cited by 29
Sharma, A., Al-Haidose, A., Al‐Asmakh, M., & Abdallah, A. M. (2024) · Clinics and Practice · doi:10.3390/clinpract14040112 · Cited by 31
At the Intersection of Gut Microbiome and Stroke: A Systematic Review of the Literature
Sharma, V., Sharma, V., Shahjouei, S., Li, J., Chaudhary, D. P., Khan, A., Wolk, D. M., Zand, R., & Abedi, V. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.729399 · Cited by 30
Self-citationArtificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research
Wu, C.-C., Islam, M. M., Poly, T. N., & Weng, Y.-C. (2024) · Diagnostics · doi:10.3390/diagnostics14040397 · Cited by 31
Machine learning in the EU health care context: exploring the ethical, legal and social issues
Beriaín, Í. D. M., Sanz, B., & Moratinos, G. L. (2020) · Information Communication & Society · doi:10.1080/1369118x.2020.1719185 · Cited by 30
Anastasiadis, A., Koudonas, A., Langas, G., Tsiakaras, S., Memmos, D., Mykoniatis, I., Symeonidis, E. N., Tsiptsios, D., Savvides, E. D., Vakalopoulos, I., et al. (2023) · Asian journal of urology · doi:10.1016/j.ajur.2023.02.002 · Cited by 26
The Modern Impact of Artificial Intelligence Systems in Healthcare: A Concise Analysis
Omotunde, H., & Mouhamed, M. R. (2023) · Mesopotamian Journal of Artificial Intelligence in Healthcare · doi:10.58496/mjaih/2023/013 · Cited by 26
Kapsali, M. Z., Livanis, E., Tsalikidis, C., Oikonomou, P., Voultsos, P. P., & Tsaroucha, A. (2024) · Cureus · doi:10.7759/cureus.54759 · Cited by 27
Artificial intelligence in clinical nutrition and dietetics: A brief overview of current evidence
Atwal, K. (2024) · Nutrition in Clinical Practice · doi:10.1002/ncp.11150 · Cited by 31
Liu, Z., Zou, W., & Lin, C. (2025) · Patient Education and Counseling · doi:10.1016/j.pec.2025.109271 · Cited by 32
A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images
Kathiravelu, P., Sharma, P., Sharma, A., Banerjee, I., Trivedi, H., Purkayastha, S., Sinha, P., Cadrin-Chênevert, A., Safdar, N. M., & Gichoya, J. W. (2021) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-021-00491-w · Cited by 30
Sjöqvist, S., & Otake, K. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065237 · Cited by 26
Liu, L., Bi, B., Cao, L. J., Gui, M., & Ju, F. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1320335 · Cited by 25
Wagner, D. N. (2020) · Telecommunications Policy · doi:10.1016/j.telpol.2020.101954 · Cited by 28
Chohan, S. R., Hu, G., Khan, A. U., Pasha, A. T., & Sheikh, M. A. (2021) · Transforming Government: People, Process and Policy · doi:10.1108/tg-05-2020-0079 · Cited by 26
Maurya, M. R., Riyaz, N. S., Reddy, M. S. B., Yalcin, H. C., Ouakad, H. M., Bahadur, I. M., Al-Maadeed, S. A., & Sadasivuni, K. K. (2021) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02447-2 · Cited by 28
Bednorz, A., Mak, J. K. L., Jylhävä, J., & Religa, D. (2023) · Clinical Interventions in Aging · doi:10.2147/cia.s400887 · Cited by 27
Artificial Intelligence and medical specialties: support or substitution?
Popa, S., Ismaiel, A., Brata, V. D., Turtoi, D. C., Bârsan, M., Czako, Z., Pop, C., Mureşan, L., Stănculete, M. F., & Dumitraşcu, D. I. (2024) · Medicine and Pharmacy Reports · doi:10.15386/mpr-2696 · Cited by 24
Mahmood, T., Rehman, A. U., Saba, T., Alahmadi, T. J., Tufail, M., Bahaj, S. A., & Ahmad, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3435948 · Cited by 29
Application of artificial intelligence in laryngeal lesions: a systematic review and meta-analysis
Marrero‐Gonzalez, A. R., Diemer, T. J., Nguyen, S. A., Camilon, T. J. M., Meenan, K. D., & O’Rourke, A. K. (2024) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-024-09075-0 · Cited by 32
Advances in machine learning for optimizing pharmaceutical drug discovery
Ogbonna, U. E., Ofoezie, E. F., Babalola, O. O., Ottu, P. O., Ogbonna, C. A., Olisakwe, S. C., George, E. T., Babarinde, S., Omaba, O. J., Chukwuemeka, C. G., et al. (2025) · Current Proteomics · doi:10.1016/j.curpro.2025.100015 · Cited by 25
Public Innovation and Digital Transformation
Väyrynen, H., Helander, N., & Jalonen, H. (2022) · Routledge eBooks · doi:10.4324/9781003230854 · Cited by 35
Aljaafari, M. A. H., El-deep, S. E., Abohany, A. A., & Sorour, S. E. (2024) · IEEE Access · doi:10.1109/access.2024.3451369 · Cited by 34
Integration of Multi-Source Medical Data for Medical Diagnosis Question Answering
Peng, Q., Cai, Y., Liu, J., Zou, Q., Chen, X., Zhong, Z., Wang, Z., Xie, J., & Li, Q. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3496862 · Cited by 23
Scorecard for synthetic medical data evaluation
Zamzmi, G., Subbaswamy, A., Sizikova, E., Margerrison, E., Delfino, J. G., & Badano, A. G. (2025) · Communications Engineering · doi:10.1038/s44172-025-00450-1 · Cited by 22
History, current status, and future directions of artificial intelligence
Kubassova, O., Shaikh, F., Melus, C., & Mähler, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820239-5.00002-4 · Cited by 25
Reassessing acquired neonatal intestinal diseases using unsupervised machine learning
Gipson, D. R., Chang, A. L., Lure, A. C., Mehta, S. A., Gowen, T., Shumans, E., Stevenson, D. K., de la Cruz, D., Aghaeepour, N., & Neu, J. (2024) · Pediatric Research · doi:10.1038/s41390-024-03074-x · Cited by 22
Crossing the principle–practice gap in AI ethics with ethical problem-solving
Corrêa, N. K., Santos, J. W., Galvão, C., Pasetti, M., Schiavon, D. E. B., Naqvi, F., Hossain, R., & Oliveira, N. F. D. (2024) · AI and Ethics · doi:10.1007/s43681-024-00469-8 · Cited by 25
Bettanti, A., Beccari, A. R., & Biccarino, M. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00188-3 · Cited by 28
Abdalhalim, A. Z. A., Ahmed, S. N. N., Ezzelarab, A. M. D., Mustafa, M., Al-Basheer, M., Ahmed, R. E. A., & Elsayed, M. B. G. E. (2025) · Cureus · doi:10.7759/cureus.85667 · Cited by 28
Musa, S. S., Ibrahim, A. M., Alhassan, M. Y., Musa, A. H., Jibo, A. G., Auwal, A. R., Okesanya, O. J., Othman, Z. K., Abubakar, M. S., Ahmed, M. M., et al. (2025) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2025.100267 · Cited by 21
Deep Ensemble Network for Quantification and Severity Assessment of Knee Osteoarthritis
Muhammad, M. B., Moinuddin, K. A., Lee, M. T. M., Zhang, Y., Abedi, V., Zand, R., & Yeasin, M. (2019) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2019.00163 · Cited by 19
Self-citationSearching for the Predictors of Response to BoNT-A in Migraine Using Machine Learning Approaches
Martinelli, D., Pocora, M. M., Icco, R. D., Allena, M., Vaghi, G., Sances, G., Castellazzi, G., & Tassorelli, C. (2023) · Toxins · doi:10.3390/toxins15060364 · Cited by 20
An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic Stroke
Sakri, S. B., Basheer, S., Zain, Z. M., Ismail, N. H. A., Nassar, D., Aldehim, G., & Alharaki, M. A. (2024) · IEEE Access · doi:10.1109/access.2024.3386220 · Cited by 20
Bilgin, G. B., Bilgin, C., Childs, D. S., Orme, J. J., Burkett, B. J., Packard, A. T., Johnson, D. R., Thorpe, M. P., Riaz, I. B., Halfdanarson, T. R., et al. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1386718 · Cited by 22
Systems medicine 2030: A Delphi study on implementation in the German healthcare system
Lemmen, C., Woopen, C., & Stock, S. (2020) · Health Policy · doi:10.1016/j.healthpol.2020.11.010 · Cited by 20
Kumar, R., Singh, A. K., Kassar, A. S. A., Humaida, M. I., Joshi, S., & Sharma, M. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.059152 · Cited by 32
A comprehensive review of AI methods in upper extremity/limb bone fracture detection
Pour, Z. M., & Berretti, S. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11296-6 · Cited by 17
Ahmed, M., Hussain, S., Pirzado, F. A., Gárate-Escamilla, A. K., Amaya, I., Ochoa‐Ruiz, G., & Ortíz-Bayliss, J. C. (2025) · Applied Sciences · doi:10.3390/app15148056 · Cited by 19
Yao, H., Derksen, H., Golbus, J. R., Zhang, J., Aaronson, K. D., Gryak, J., & Najarian, K. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3211765 · Cited by 19
Zhang, Z., Lu, Y., Vosoughi, S., Levy, J., Christensen, B. C., & Salas, L. A. (2023) · NAR Cancer · doi:10.1093/narcan/zcad017 · Cited by 18
Cambuli, V. M., & Baroni, M. G. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241713139 · Cited by 19
Hemmet, A. (2023) · American Journal of Smart Technology and Solutions · doi:10.54536/ajsts.v2i2.2239 · Cited by 39
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
Artificial intelligence to enhance the diagnosis of ocular surface squamous neoplasia
Kozma, K., Jánki, Z. R., Bilicki, V., Csutak, A., & Szalai, E. (2025) · Scientific Reports · doi:10.1038/s41598-025-94876-4 · Cited by 18
Rolvien, L., Kruse, L., Rings, S., Zimmer, C., Schauenburg, G., Thams, F., Brähler, A., Rudschies, C., Schneider, I., Moritz, S., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-49764-w · Cited by 19
Artificial intelligence and the medical physics profession - A Swedish perspective
Andersson, J., Nyholm, T., Ceberg, C. P., Almén, A., Bernhardt, P., Fransson, A., & Olsson, L. E. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.07.009 · Cited by 18
Lans, A., Pierik, R., Bales, J. R., Fourman, M. S., Shin, D., Kanbier, L. N., Rifkin, J., DiGiovanni, W., Chopra, R. R., Moeinzad, R., et al. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102396 · Cited by 20
KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction
Liu, L. J., Ortiz-Soriano, V., Neyra, J. A., & Chen, J. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9994931 · Cited by 21
Revolutionizing nursing education and care: The role of artificial intelligence in nursing
Ghane, G., Ghiyasvandian, S., Chekeni, A. M., & Karimi, R. (2024) · Nurse author & editor · doi:10.1111/nae2.12057 · Cited by 22
Harmonizing Tradition and Technology: The Synergy of Artificial Intelligence in Traditional Medicine
Amabie, T., Izah, S. C., Ogwu, M. C., & Hait, M. (2023) · Reference series in phytochemistry · doi:10.1007/978-3-031-21973-3_70-1 · Cited by 19
Kim, S., Jeong, T., Lee, K., Chung, J., Cho, E., Lee, S.-H., Chun, S., Song, J., & Min, W. (2024) · Annals of Laboratory Medicine · doi:10.3343/alm.2024.0082 · Cited by 18
Can Artificial Intelligence Chatbots Improve Mental Health?: A Scoping Review
Gallegos, C., Kausler, R., Alderden, J., Davis, M., & Wang, L. (2024) · CIN: Computers, Informatics, Nursing · doi:10.1097/cin.0000000000001155 · Cited by 18
The prospects of tumor chemosensitivity testing at the single-cell level
Yang, C., Yang, C., Yarden, Y., To, K. K. W., & Fu, L. (2020) · Drug Resistance Updates · doi:10.1016/j.drup.2020.100741 · Cited by 16
Understanding Artificial Intelligence and Predictive Analytics
Cho, B., Geng, E. A., Arvind, V., Valliani, A. A.-A., Tang, J. E., Schwartz, J., Dominy, C. L., Cho, S. K.-W., & Kim, J. (2022) · JBJS Reviews · doi:10.2106/jbjs.rvw.21.00142 · Cited by 18
Alaiti, R. K., Vallio, C. S., Assunção, J. H., Silva, F. B. D. A. E., Gracitelli, M. E. C., Neto, A. A. F., & Malavolta, E. A. (2023) · Orthopaedic Journal of Sports Medicine · doi:10.1177/23259671231206180 · Cited by 17
Impact of Artificial Intelligence in Endodontics: Precision, Predictions, and Prospects
Parinitha, M. S., Doddawad, V. G., Kalgeri, S. H., Gowda, S. S., & Patil, S. (2024) · Journal of Medical Signals & Sensors · doi:10.4103/jmss.jmss_7_24 · Cited by 17
Arshad, M. F., Ahmed, F., Nonnis, F., Tamponi, C., Scala, A., & Varcasia, A. (2025) · Research in Veterinary Science · doi:10.1016/j.rvsc.2025.105776 · Cited by 20
Meeting the challenges of electronic health record (EHR) optimization
Makhni, S., Cerrato, P., Rico, J., Niazi, S., O’Horo, J., Peters, S., Shah, V., & Halamka, J. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02178-w · Cited by 19
Development of Artificial Intelligence to Support Needle Electromyography Diagnostic Analysis
Nam, S., Sohn, M. K., Kim, H.-A., Kong, H., & Jung, I. (2019) · Healthcare Informatics Research · doi:10.4258/hir.2019.25.2.131 · Cited by 16
Chaudhary, D. P., Diaz, J., Lu, Y., Li, J., Abedi, V., & Zand, R. (2022) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2022.120423 · Cited by 16
Self-citationBiomarkers of necrotizing enterocolitis in the era of machine learning and omics
Leiva, T. D., Lueschow, S. R., Burge, K. Y., DeVette, C. I., McElroy, S. J., & Chaaban, H. (2022) · Seminars in Perinatology · doi:10.1016/j.semperi.2022.151693 · Cited by 18
Application of a Multi-Layer Perceptron in Preoperative Screening for Orthognathic Surgery
Chaiprasittikul, N., Thanathornwong, B., Pornprasertsuk‐Damrongsri, S., Raocharernporn, S., Maponthong, S., & Manopatanakul, S. (2023) · Healthcare Informatics Research · doi:10.4258/hir.2023.29.1.16 · Cited by 16
Assessing ChatGPT’s Responses to Otolaryngology Patient Questions
Carnino, J. M., Pellegrini, W. R., Willis, M., Cohen, M. B., Paz‐Lansberg, M., Davis, E. M., Grillone, G. A., & Levi, J. R. (2024) · Annals of Otology Rhinology & Laryngology · doi:10.1177/00034894241249621 · Cited by 16
Future Horizons: The Potential Role of Artificial Intelligence in Cardiology
Patrascanu, O. S., Tutunaru, D., Mușat, C. L., Dragostin, O. M., Fulga, A., Nechita, L., Ciubară, A. B., Piraianu, A. I., Stamate, E., Poalelungi, D. G., et al. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14060656 · Cited by 20
Azzopardi, P. S., Clark, T. C., Renfrew, L., Habito, M., & Ameratunga, S. N. (2024) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2024.04.001 · Cited by 17
Artificial intelligence in differentiating tropical infections: A step ahead
Shenoy, S., Rajan, A. K., Rashid, M., Chandran, V. P., Poojari, P. G., Kunhikatta, V., Acharya, U. D., Nair, S., Varma, M. D., & Thunga, G. (2022) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0010455 · Cited by 16
Metadata
- Authors
- Noorbakhsh-Sabet, N., Zand, R., Zhang, Y., & Abedi, V.
- Affiliations
- St. Jude Children's Research Hospital (US)
- University of Tennessee Health Science Center (US)
- Geisinger Medical Center (US)
- Geisinger Health System (US)
- Corresponding author
- Abedi, Vida
- Venue
- The American Journal of Medicine
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 132
- Issue
- 7
- Pages
- 795-801
- ISSN
- 0002-9343 (ISSN-L)
- PMC
- PMC6669105
- OpenAlex
- W2915053252
- Cited by
- 626
- References
- 68
- Citations by year
- 2026: 44 · 2025: 130 · 2024: 138 · 2023: 137 · 2022: 68 · 2021: 54 · 2020: 44 · 2019: 10
- FWCI
- 2.16
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- The American Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (R56HL116832)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- artificial intelligence in healthcare, machine learning applications, clinical application, translational research, decision support system, data privacy, data sharing, genetic information
- MeSH terms
- Machine Learning, Artificial Intelligence, Delivery of Health Care, Forecasting, Humans, Decision Support Systems, Clinical, Drug Discovery, Translational Research, Biomedical, Epidemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review