The DOI matches its Crossref record; 188 of the 188 references it cites were found in a registry or an index.
The Integration of Artificial Intelligence into Clinical Practice
Open accessAbstract
The purpose of this literature review is to provide a fundamental synopsis of current research pertaining to artificial intelligence (AI) within the domain of clinical practice. Artificial intelligence has revolutionized the field of medicine and healthcare by providing innovative solutions to complex problems. One of the most important benefits of AI in clinical practice is its ability to investigate extensive volumes of data with efficiency and precision. This has led to the development of various applications that have improved patient outcomes and reduced the workload of healthcare professionals. AI can support doctors in making more accurate diagnoses and developing personalized treatment plans. Successful examples of AI applications are outlined for a series of medical specialties like cardiology, surgery, gastroenterology, pneumology, nephrology, urology, dermatology, orthopedics, neurology, gynecology, ophthalmology, pediatrics, hematology, and critically ill patients, as well as diagnostic methods. Special reference is made to legal and ethical considerations like accuracy, informed consent, privacy issues, data security, regulatory framework, product liability, explainability, and transparency. Finally, this review closes by critically appraising AI use in clinical practice and its future perspectives. However, it is also important to approach its development and implementation cautiously to ensure ethical considerations are met.
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“AI applications have proven effective in various clinical domains, such as reducing documentation burdens for clinicians, improving image interpretation in radiology, supporting intraoperative surgical guidance, and facilitating public health risk stratification2,3.”
From Developing a Canadian artificial intelligence medical curriculum using a Delphi study (2024) · cites this paper as [3]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Few structured programs thoroughly address both the practical and ethical dimensions of AI in medicine3–10.”
From Developing a Canadian artificial intelligence medical curriculum using a Delphi study (2024) · cites this paper as [3]
Experimental — accuracy not yet measured
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
2 retracted
Bodaghi, A., Fattahi, N., & Ramazani, A. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e13323 · Cited by 371
RetractedWu, L., Wei, D., Yang, N., Lei, H., & Wang, Y. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/4022312 · Cited by 9
RetractedSung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21660 · Cited by 108,699
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Siegel, R. L., Miller, K. D., & Jemal, A. (2018) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21442 · Cited by 16,632
The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary
Louis, D. N., Perry, A., Reifenberger, G., von Deimling, A., Figarella‐Branger, D., Cavenee, W. K., Ohgaki, H., Wiestler, O. D., Kleihues, P., & Ellison, D. W. (2016) · Acta Neuropathologica · doi:10.1007/s00401-016-1545-1 · Cited by 15,703
Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock
Dellinger, R. P., Levy, M. M., Rhodes, A., Annane, D., Gerlach, H., Opal, S. M., Sevransky, J. E., Sprung, C. L., Douglas, I. S., Jaeschke, R., et al. (2013) · Critical Care Medicine · doi:10.1097/ccm.0b013e31827e83af · Cited by 9,694
On computable numbers, with an application to the entscheidungsproblem
Turing, A. M. (1937) · Proceedings of the London Mathematical Society · doi:10.1112/plms/s2-42.1.230 · Cited by 8,283
The potential for artificial intelligence in healthcare
Davenport, T., & Kalakota, R. (2019) · Future Healthcare Journal · doi:10.7861/futurehosp.6-2-94 · Cited by 3,800
30 years of adaptive neural networks: perceptron, Madaline, and backpropagation
Widrow, B. C., & Lehr, M. A. (1990) · Proceedings of the IEEE · doi:10.1109/5.58323 · Cited by 2,270
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
the Precise4Q consortium, Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01332-6 · Cited by 2,203
Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0447-x · Cited by 2,167
Clinical Classification of Age-related Macular Degeneration
Ferris, F. L., Wilkinson, C. P., Bird, A. C., Chakravarthy, U., Chew, E. Y., Csaky, K. G., & Sadda, S. R. (2013) · Ophthalmology · doi:10.1016/j.ophtha.2012.10.036 · Cited by 1,868
Planning chemical syntheses with deep neural networks and symbolic AI
Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature · doi:10.1038/nature25978 · Cited by 1,847
Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002683 · Cited by 1,719 · cited 1×
“Drawing inspiration from various disciplines, the domains of transparency and trace-ability in the context of healthcare and individual patients necessitate adherence to more rigorous criteria [180].”
BackgroundAttia, Z. I., Kapa, S., Lopez-Jimenez, F., McKie, P. M., Ladewig, D. J., Satam, G., Pellikka, P. A., Enriquez-Sarano, M., Noseworthy, P. A., Munger, T. M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0240-2 · Cited by 1,389
Prevalence of chronic pain in the UK: a systematic review and meta-analysis of population studies
Fayaz, A., Croft, P., Langford, R. M., Donaldson, L. J., & Jones, G. T. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-010364 · Cited by 1,355
Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C., & Faisal, A. A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0213-5 · Cited by 1,290
History of artificial intelligence in medicine
Kaul, V., Enslin, S., & Gross, S. A. (2020) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2020.06.040 · Cited by 1,184
Bendixen, M., Jørgensen, O. D., Kronborg, C., Andersen, C., & Licht, P. B. (2016) · The Lancet Oncology · doi:10.1016/s1470-2045(16)00173-x · Cited by 1,146
Solubility Advantage of Amorphous Drugs and Pharmaceutical Cocrystals
Babu, N. J., & Nangia, A. K. (2011) · Crystal Growth & Design · doi:10.1021/cg200492w · Cited by 1,020
Disease variant prediction with deep generative models of evolutionary data
Frazer, J., Notin, P., Dias, M., Gomez, A., Min, J. K., Brock, K., Gal, Y., & Marks, D. S. (2021) · Nature · doi:10.1038/s41586-021-04043-8 · Cited by 791
Artificial Intelligence in Cardiovascular Imaging
Dey, D., Slomka, P. J., Leeson, P., Comaniciu, D., Shrestha, S., Sengupta, P. P., & Marwick, T. H. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.12.054 · Cited by 720
Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial
Repici, A., Badalamenti, M., Maselli, R., Correale, L., Radaelli, F., Rondonotti, E., Ferrara, E., Spadaccini, M., Alkandari, A., Fugazza, A., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.04.062 · Cited by 646
Survey of Explainable AI Techniques in Healthcare
Chaddad, A., Peng, J., Xu, J., & Bouridane, A. (2023) · Sensors · doi:10.3390/s23020634 · Cited by 591
Application of Artificial Intelligence to Gastroenterology and Hepatology
Berre, C. L., Sandborn, W. J., Aridhi, S., Devignes, M., Fournier, L. S., Smaïl‐Tabbone, M., Danese, S., & Peyrin‐Biroulet, L. (2019) · Gastroenterology · doi:10.1053/j.gastro.2019.08.058 · Cited by 541
End-to-End Adversarial Retinal Image Synthesis
Costa, P., Galdrán, A., Meyer, M. I., Niemeijer, M., Abràmoff, M. D., Mendonça, A. M., & Campilho, A. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2759102 · Cited by 540
Tooth detection and numbering in panoramic radiographs using convolutional neural networks
Tuzoff, D. V., Tuzova, L. N., Bornstein, M. M., Krasnov, A. S., Kharchenko, M. A., Nikolenko, S., Sveshnikov, M. M., & Bednenko, G. B. (2019) · Dentomaxillofacial Radiology · doi:10.1259/dmfr.20180051 · Cited by 529
Hassan, C., Spadaccini, M., Iannone, A., Maselli, R., Jovani, M., Chandrasekar, V. T., Antonelli, G., Yu, H., Areia, M., Dinis-Ribeiro, M., et al. (2021) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2020.06.059 · Cited by 529
Wang, K., Lu, X., Zhou, H., Gao, Y., Zheng, J., Tong, M., Wu, C., Liu, C., Huang, L., Jiang, T., et al. (2018) · Gut · doi:10.1136/gutjnl-2018-316204 · Cited by 510
Incidence and progression of diabetic retinopathy: a systematic review
Sabanayagam, C., Banu, R., Chee, M. L., Lee, R. Y., Wang, Y. X., Tan, G. S. W., Jonas, J. B., Lamoureux, E. L., Cheng, C., Klein, B. E. K., et al. (2018) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(18)30128-1 · Cited by 494
Kickingereder, P., Isensee, F., Tursunova, I., Petersen, J., Neuberger, U., Bonekamp, D., Brugnara, G., Schell, M., Keßler, T., Foltyn‐Dumitru, M., et al. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30098-1 · Cited by 485
Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas
Chang, P. D., Grinband, J., Weinberg, B. D., Bardis, M. D., Khy, M., Cadena, G., Su, M., Cha, S., Filippi, C. G., Bota, D. A., et al. (2018) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5667 · Cited by 476
O'Sullivan, S., Nevejans, N., Allen, C., Blyth, A., Leonard, S., Pagallo, U., Holzinger, K., Holzinger, A., Sajid, M. I., & Ashrafian, H. (2019) · The International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.1968 · Cited by 437
Yao, X., Rushlow, D. R., Inselman, J. W., McCoy, R. G., Thacher, T. D., Behnken, E. M., Bernard, M. E., Rosas, S. L., Akfaly, A., Misra, A., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01335-4 · Cited by 424
Narula, S., Shameer, K., Omar, A. M. S., Dudley, J. T., & Sengupta, P. P. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.08.062 · Cited by 414
Chang, K., Bai, H. X., Zhou, H., Su, C., Bi, W. L., Agbodza, E., Kavouridis, V. K., Senders, J. T., Boaro, A., Beers, A. L., et al. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-2236 · Cited by 392
Xue, B., Li, D., Lu, C., King, C. R., Wildes, T. S., Avidan, M. S., Kannampallil, T., & Abraham, J. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.2240 · Cited by 373
Somashekhar, S. P., Sepúlveda, M.-J., Puglielli, S. M., Norden, A. D., Shortliffe, E. H., Rohit Kumar, C., Rauthan, A., Kumar, N., Patil, P., Rhee, K. B. L., et al. (2017) · Annals of Oncology · doi:10.1093/annonc/mdx781 · Cited by 366
Peng, Y., Dharssi, S., Chen, Q., Keenan, T. D. L., Agrón, E., Wong, W. T., Chew, E. Y., & Lu, Z. (2018) · Ophthalmology · doi:10.1016/j.ophtha.2018.11.015 · Cited by 363
The American Society of Anesthesiologists Closed Claims Project
Cheney, F. W. (1999) · Anesthesiology · doi:10.1097/00000542-199908000-00030 · Cited by 332
Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks
Tschandl, P., Rosendahl, C., Akay, B., Argenziano, G., Blum, A., Braun, R., Cabo, H., Gourhant, J., Kreusch, J., Lallas, A., et al. (2019) · JAMA dermatology · doi:10.1001/jamadermatol.2018.4378 · Cited by 313
An overview of deep learning in the field of dentistry
Hwang, J. J., Jung, Y., Cho, B., & Heo, M.-S. (2019) · Imaging Science in Dentistry · doi:10.5624/isd.2019.49.1.1 · Cited by 297
Artificial intelligence in drug design
Zhong, F., Xing, J., Li, X., Liu, X., Fu, Z., Xiong, Z., Lu, D., Wu, X., Zhao, J., Tan, X., et al. (2018) · Science China Life Sciences · doi:10.1007/s11427-018-9342-2 · Cited by 293
Visualizing non-metric similarities in multiple maps
van der Maaten, L., & Hinton, G. E. (2011) · Machine Learning · doi:10.1007/s10994-011-5273-4 · Cited by 290
Freeman, K., Dinnes, J., Chuchu, N., Takwoingi, Y., Bayliss, S. E., Matin, R. N., Jain, A., Walter, F. M., Williams, H. C., & Deeks, J. J. (2020) · BMJ · doi:10.1136/bmj.m127 · Cited by 260
Artificial Intelligence in Healthcare: Review, Ethics, Trust Challenges & Future Research Directions
Kumar, P., Chauhan, S., & Awasthi, L. K. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105894 · Cited by 259
Li, Z., Keel, S., Liu, C., He, Y., Meng, W., Scheetz, J., Lee, P. Y., Shaw, J., Ting, D., Wong, T. Y., et al. (2018) · Diabetes Care · doi:10.2337/dc18-0147 · Cited by 255
Asaoka, R., Murata, H., Hirasawa, K., Fujino, Y., Matsuura, M., Miki, A., Kanamoto, T., Ikeda, Y., Mori, K., Iwase, A., et al. (2018) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2018.10.007 · Cited by 255
Duffy, G., Cheng, P. P., Yuan, N., He, B. D., Kwan, A. C., Shun-Shin, M. J., Alexander, K. M., Ebinger, J. E., Lungren, M. P., Rader, F., et al. (2022) · JAMA Cardiology · doi:10.1001/jamacardio.2021.6059 · Cited by 252
A deep convolutional neural network for classification of red blood cells in sickle cell anemia
Xu, M., Papageorgiou, D. P., Abidi, S. Z., Dao, M., Zhao, H., & Karniadakis, G. E. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005746 · Cited by 240
The Artificial Life Route to Artificial Intelligence
Steels, L., & Brooks, R. A. (2018) · Routledge eBooks · doi:10.4324/9781351001885 · Cited by 239
Hashimoto, R., Requa, J., Dao, T., Ninh, A. Q., Tran, E., Mai, D., Lugo, M., Chehade, N. E.-H., Chang, K. J., Karnes, W. E., et al. (2020) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2019.12.049 · Cited by 237
Artificial Neural Networks in the Diagnosis and Prognosis of Prostate Cancer: A Pilot Study
Snow, P. B., Smith, D. S., & Catàlona, W. J. (1994) · The Journal of Urology · doi:10.1016/s0022-5347(17)32416-3 · Cited by 233
Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI
Zhang, N., Yang, G., Gao, Z., Xu, C., Zhang, Y., Shi, R., Keegan, J., Xu, L., Zhang, H., Fan, Z., et al. (2019) · Radiology · doi:10.1148/radiol.2019182304 · Cited by 233
Pathologist-level classification of histopathological melanoma images with deep neural networks
Hekler, A., Utikal, J. S., Enk, A. H., Berking, C., Klode, J., Schadendorf, D., Jansen, P., Franklin, C., Holland-Letz, T., Krahl, D., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.04.021 · Cited by 231
Zhao, W., Yang, J., Sun, Y., Li, C., Wu, W., Jin, L., Yang, Z., Ni, B., Gao, P., Wang, P., et al. (2018) · Cancer Research · doi:10.1158/0008-5472.can-18-0696 · Cited by 226
Machine learning in pain research
Lötsch, J., & Ultsch, A. (2017) · Pain · doi:10.1097/j.pain.0000000000001118 · Cited by 217
Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review
Spann, A., Yasodhara, A., Kang, J., Watt, K. D. S., Wang, B., Goldenberg, A., & Bhat, M. (2020) · Hepatology · doi:10.1002/hep.31103 · Cited by 209
Novel Artificial Neural Network for Early Detection of Prostate Cancer
Djavan, B., Mesut Remzi, A., Zlotta, A. R., Seitz, C., Snow, P. B., & Marberger, M. (2002) · Journal of Clinical Oncology · doi:10.1200/jco.2002.20.4.921 · Cited by 207
Laukamp, K. R., Thiele, F., Shakirin, G., Zopfs, D., Faymonville, A., Timmer, M., Maintz, D., Perkuhn, M., & Borggrefe, J. (2019) · European Radiology · doi:10.1007/s00330-018-5595-8 · Cited by 205
Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases
Ahn, J. C., Connell, A., Simonetto, D. A., Hughes, C., & Shah, V. H. (2020) · Hepatology · doi:10.1002/hep.31603 · Cited by 201
Bakkar, N., Kovalik, T., Lorenzini, I., Spangler, S., Lacoste, A. M. B., Sponaugle, K., Ferrante, P., Argentinis, E., Sattler, R., & Bowser, R. P. (2017) · Acta Neuropathologica · doi:10.1007/s00401-017-1785-8 · Cited by 200
Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning
Davoudi, A., Malhotra, K. R., Shickel, B., Siegel, S., Williams, S., Ruppert, M. M., Bihorac, E., Ozrazgat‐Baslanti, T., Tighe, P. J., Bihorac, A., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-44004-w · Cited by 195
Hung, A. J., Chen, J., Che, Z., Nilanon, T., Jarc, A., Titus, M., Oh, P. J., Gill, I. S., & Liu, Y. (2018) · Journal of Endourology · doi:10.1089/end.2018.0035 · Cited by 190
Deep learning approach to peripheral leukocyte recognition
Wang, Q., Bi, S., Sun, M., Wang, Y., Wang, D., & Yang, S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218808 · Cited by 182
Real-time use of artificial intelligence in the evaluation of cancer in Barrett’s oesophagus
Ebigbo, A., Mendel, R., Probst, A., Manzeneder, J., Prinz, F., de Souza, L. A., Jr., Papa, J. P., Palm, C., & Messmann, H. (2019) · Gut · doi:10.1136/gutjnl-2019-319460 · Cited by 182
Cao, Z., Duan, L., Yang, G., Yue, T., & Chen, Q. (2019) · BMC Medical Imaging · doi:10.1186/s12880-019-0349-x · Cited by 179
Bachtiger, P., Petri, C. F., Scott, F. E., Ri Park, S., Kelshiker, M. A., Sahemey, H. K., Dumea, B., Alquero, R., Padam, P. S., Hatrick, I. R., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00256-9 · Cited by 167
Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis
Artzi, M., Bressler, I., & Bashat, D. B. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26643 · Cited by 165
Informed Consent and Medical Artificial Intelligence: What to Tell the Patient?
Cohen, I. G. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3529576 · Cited by 165
Schlanitz, F. G., Baumann, B., Kundi, M., Sacu, S., Baratsits, M., Scheschy, U., Shahlaee, A., Mittermüller, T. J., Montuoro, A., Roberts, P. K., et al. (2016) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2016-308422 · Cited by 156
Comparison of Cox Regression With Other Methods for Determining Prediction Models and Nomograms
Kattan, M. W. (2003) · The Journal of Urology · doi:10.1097/01.ju.0000094764.56269.2d · Cited by 153
The Coding of Roentgen Images for Computer Analysis as Applied to Lung Cancer
Lodwick, G. S., Keats, T. E., & Dorst, J. P. (1963) · Radiology · doi:10.1148/81.2.185 · Cited by 146
Trends of nonmelanoma skin cancer from 1960 through 2000 in a Canadian population
Demers, A. A., Nugent, Z. J., Mihalcioiu, C. L. D., Wiseman, M., & Kliewer, E. V. (2005) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2005.03.043 · Cited by 144
Lee, J. H., Joo, I., Kang, T. W., Paik, Y. H., Sinn, D. H., Ha, S. Y., Kim, K., Choi, C., Lee, G., Yi, J., et al. (2020) · European Radiology · doi:10.1007/s00330-019-06407-1 · Cited by 144
Barbieri, C., Molina, M., Ponce, P., Tothová, M., Cattinelli, I., Titapiccolo, J. I., Mari, F., Amato, C., Leipold, F., Wehmeyer, W., et al. (2016) · Kidney International · doi:10.1016/j.kint.2016.03.036 · Cited by 141
Improving the prognosis of health care in the USA
Galvani, A. P., Parpia, A. S., Foster, E. M., Singer, B. H., & Fitzpatrick, M. C. (2020) · The Lancet · doi:10.1016/s0140-6736(19)33019-3 · Cited by 138 · cited 1×
“Insurers may need to adapt their underwriting processes to account for the specific risks posed by AI in healthcare [189].”
BackgroundBrennan, M., Puri, S., Ozrazgat-Baslanti, T., Feng, Z., Ruppert, M., Hashemighouchani, H., Momcilovic, P., Li, X., Wang, D. Z., & Bihorac, A. (2019) · Surgery · doi:10.1016/j.surg.2019.01.002 · Cited by 131
Automated drusen segmentation and quantification in SD-OCT images
Chen, Q., Leng, T., Zheng, L., Kutzscher, L., Ma, J., de Sisternes, L., & Rubin, D. L. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.06.003 · Cited by 129
Bellini, V., Guzzon, M., Bigliardi, B., Mordonini, M., Filippelli, S., & Bignami, E. G. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1512-1 · Cited by 126
Ye, Z., Yang, Y., Li, X., Cao, D., & Ouyang, D. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00816 · Cited by 125
Olsen, T. G., Jackson, B. H., Feeser, T. A., Kent, M. N., Moad, J. C., Krishnamurthy, S., Lunsford, D. D., & Soans, R. E. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_31_18 · Cited by 119
Hossain, S., Kabedev, A., Parrow, A., Bergström, C. A. S., & Larsson, P. (2019) · European Journal of Pharmaceutics and Biopharmaceutics · doi:10.1016/j.ejpb.2019.02.007 · Cited by 119
Chandradevan, R., Aljudi, A. A., Drumheller, B. R., Kunananthaseelan, N., Amgad, M. T., Gutman, D. A., Cooper, L., & Jaye, D. L. (2019) · Laboratory Investigation · doi:10.1038/s41374-019-0325-7 · Cited by 119
Ianni, J. D., Soans, R. E., Sankarapandian, S., Chamarthi, R. V., Ayyagari, D., Olsen, T. G., Bonham, M. J., Stavish, C. C., Motaparthi, K., Cockerell, C. J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-59985-2 · Cited by 117
Akbari, H., Bakas, S., Pisapia, J. M., Nasrallah, M. P., Rozycki, M., Martinez‐Lage, M., Morrissette, J. J. D., Dahmane, N., O’Rourke, D. M., & Davatzikos, C. A. (2018) · Neuro-Oncology · doi:10.1093/neuonc/noy033 · Cited by 113
Ko, B., Wang, Y., Li, J.-L., Li, C., Weng, P.-F., Hsu, S.-C., Hou, H., Huang, H., Yao, M., Lin, C., et al. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2018.10.042 · Cited by 110
Applications of artificial intelligence and machine learning in respiratory medicine
Gonem, S., Janssens, W., Das, N., & Topalovic, M. (2020) · Thorax · doi:10.1136/thoraxjnl-2020-214556 · Cited by 107
Discrimination of paediatric brain tumours using apparent diffusion coefficient histograms
Bull, J. G., Saunders, D. E., & Clark, C. A. (2011) · European Radiology · doi:10.1007/s00330-011-2255-7 · Cited by 106
Utilization of Artificial Intelligence in Echocardiography
Kusunose, K., Haga, A., Abe, T., & Sata, M. (2019) · Circulation Journal · doi:10.1253/circj.cj-19-0420 · Cited by 102
Diagnosis of urinary tract infection based on artificial intelligence methods
Özkan, İ. A., Köklü, M., & Sert, İ. Ü. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.10.007 · Cited by 97
Pépin, J., Letesson, C., Le-Dong, N. N., Dedave, A., Denison, S., Cuthbert, V., Martinot, J., & Gozal, D. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.19657 · Cited by 97
Artificial intelligence in dermatopathology: diagnosis, education, and research
Wells, A., Patel, S., Lee, J. B., & Motaparthi, K. (2021) · Journal of Cutaneous Pathology · doi:10.1111/cup.13954 · Cited by 95
van Hamersvelt, R. W., Zreik, M., Voskuil, M., Viergever, M. A., Išgum, I., & Leiner, T. (2018) · European Radiology · doi:10.1007/s00330-018-5822-3 · Cited by 94
Perioperative care of the obese patient
Carron, M., Fakhr, B. S., Ieppariello, G., & D, M. F. M. (2020) · British journal of surgery · doi:10.1002/bjs.11447 · Cited by 90
Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age
Attallah, O., Sharkas, M., & Gadelkarim, H. (2019) · Brain Sciences · doi:10.3390/brainsci9090231 · Cited by 89
Dong, H.-C., Dong, H.-K. D., Yu, M.-H., Lin, Y.-H., & Chang, C. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17165993 · Cited by 89
AI in drug development: a multidisciplinary perspective
Gallego, V., Naveiro, R., Roca, C., Insua, D. R., & Campillo, N. E. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10266-8 · Cited by 85
Simões, M. F., Silva, G., Pinto, A. C. R., Fonseca, M., Silva, N. E., Pinto, R. M. A., & Simões, S. (2020) · European Journal of Pharmaceutics and Biopharmaceutics · doi:10.1016/j.ejpb.2020.05.012 · Cited by 84
Stivaros, S. M., Garg, S., Tziraki, M., Cai, Y., Thomas, O., Mellor, J., Morris, A. A. M., Jim, C., Szumanska-Ryt, K., Parkes, L. M., et al. (2018) · Molecular Autism · doi:10.1186/s13229-018-0190-z · Cited by 83
Alsalem, M. A., Zaidan, A. A., Zaidan, B. B., Hashim, M. S., Madhloom, H. T., Azeez, N. D., & Alsyisuf, S. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.02.005 · Cited by 82
Application of machine learning techniques to the analysis and prediction of drug pharmacokinetics
Ota, R., & Yamashita, F. (2022) · Journal of Controlled Release · doi:10.1016/j.jconrel.2022.11.014 · Cited by 81
Zhang, J., Friberg, I. M., Kift-Morgan, A. P., Parekh, G., Morgan, M. P. G., Liuzzi, A. R., Lin, C., Donovan, K. L., Colmont, C. S., Morgan, P., et al. (2017) · Kidney International · doi:10.1016/j.kint.2017.01.017 · Cited by 80
A machine learning predictor of facial attractiveness revealing human-like psychophysical biases
Kagian, A., Dror, G., Leyvand, T., Meilijson, I., Cohen‐Or, D., & Ruppin, E. (2008) · Vision Research · doi:10.1016/j.visres.2007.11.007 · Cited by 79
Quinten, V. M., van Meurs, M., Wolffensperger, A. E., ter Maaten, J. C., & Ligtenberg, J. J. M. (2017) · European Journal of Emergency Medicine · doi:10.1097/mej.0000000000000460 · Cited by 76
Automated Assessment of Dynamic Knee Valgus and Risk of Knee Injury During the Single Leg Squat
Kianifar, R., Lee, A. W., Raina, S. M., & Kulić, D. (2017) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2017.2736559 · Cited by 74
Tavolara, T. E., Gürcan, M. N., Segal, S., & Niazi, M. K. K. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104737 · Cited by 72
Artificial Intelligence in Obstetrics and Gynaecology: Is This the Way Forward?
Emin, E. I., Emin, E., Papalois, A. E., Willmott, F., Clarke, S. D., & Sideris, M. C. (2019) · In Vivo · doi:10.21873/invivo.11635 · Cited by 71
Rotondano, G., Cipolletta, L., Grossi, E., Koch, M., Intraligi, M., Buscema, M., & Marmo, R. (2011) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2010.10.006 · Cited by 70
Serati, M., Salvatore, S., Siesto, G., Cattoni, E., Braga, A., Sorice, P., Cromi, A., Ghezzi, F., & Bolis, P. (2011) · European Urology · doi:10.1016/j.eururo.2011.03.010 · Cited by 70
Arai, Y., Kondo, T., Fuse, K., Shibasaki, Y., Masuko, M., Sugita, J., Teshima, T., Uchida, N., Fukuda, T., Kakihana, K., et al. (2019) · Blood Advances · doi:10.1182/bloodadvances.2019000934 · Cited by 69
Predicting Corticosteroid-Free Biologic Remission with Vedolizumab in Crohn’s Disease
Waljee, A. K., Liu, B., Sauder, K. L., Zhu, J., Govani, S. M., Stidham, R. W., & Higgins, P. (2018) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izy031 · Cited by 68
Sakai, A., Komatsu, M., Komatsu, R., Matsuoka, R., Yasutomi, S., Dozen, A., Shozu, K., Arakaki, T., Machino, H., Asada, K., et al. (2022) · Biomedicines · doi:10.3390/biomedicines10030551 · Cited by 68
Steps to use artificial intelligence in echocardiography
Kusunose, K. (2020) · Journal of Echocardiography · doi:10.1007/s12574-020-00496-4 · Cited by 67
Variational Autoencoders for Data Augmentation in Clinical Studies
Papadopoulos, D., & Karalis, V. D. (2023) · Applied Sciences · doi:10.3390/app13158793 · Cited by 67
Self-citationZhang, Y., Jiang, B., Zhang, L., Greuter, M. J. W., de Bock, G. H., Zhang, H., & Xie, X. (2021) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405617666210806125953 · Cited by 66
DENDRAL and Meta-DENDRAL: roots of knowledge systems and expert system applications
Feigenbaum, E. A., & Buchanan, B. G. (1993) · Artificial Intelligence · doi:10.1016/0004-3702(93)90191-d · Cited by 65
Lin, M., He, X., Guo, H., He, M., Zhang, L., Xian, J., Lei, T., Xu, Q., Zheng, J., Feng, J., et al. (2022) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.24843 · Cited by 65
Geddes, C. C., Fox, J. G., Allison, M. E. M., Boulton-Jones, J. M., & Simpson, K. (1998) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/13.1.67 · Cited by 61
Quon, J. L., Han, M., Kim, L. H., Koran, M. E. I., Chen, L. C., Lee, E. H., Wright, J., Ramaswamy, V., Lober, R. M., Taylor, M. D., et al. (2020) · Journal of Neurosurgery Pediatrics · doi:10.3171/2020.6.peds20251 · Cited by 60
Focal Liver Lesions: Computer-aided Diagnosis by Using Contrast-enhanced US Cine Recordings
Ta, C., Kono, Y., Eghtedari, M., Oh, Y. T., Robbin, M. L., Barr, R. G., Kummel, A. C., & Mattrey, R. F. (2017) · Radiology · doi:10.1148/radiol.2017170365 · Cited by 58
Gates, E. D. H., Lin, J. S., Weinberg, J. S., Hamilton, J. D., Prabhu, S. S., Hazle, J. D., Fuller, G. N., Baladandayuthapani, V., Fuentes, D., & Schellingerhout, D. (2019) · Neuro-Oncology · doi:10.1093/neuonc/noz004 · Cited by 57
Zhou, H., Li, L., Liu, Z., Zhao, K., Chen, X., Lu, M., Yin, G., Song, L., Zhao, S., Zheng, H., et al. (2020) · European Radiology · doi:10.1007/s00330-020-07454-9 · Cited by 57
Artificial Intelligence and Collusion
Beneke, F., & Mackenrodt, M.-O. (2018) · GRURRR. Gewerblicher Rechtsschutz und Urheberrecht, Rechtsprechungs-Report/GRUR-DVD/GRUR-CD/IIC/Gewerblicher Rechtsschutz und Urheberrecht/Gewerblicher Rechtsschutz und Urheberrecht. Internationaler Teil · doi:10.1007/s40319-018-00773-x · Cited by 55
Orphanidou-Vlachou, E., Vlachos, N., Davies, N. P., Arvanitis, T. N., Grundy, R. G., & Peet, A. C. (2014) · NMR in Biomedicine · doi:10.1002/nbm.3099 · Cited by 53
Diagnostic yield of genetic testing in a heterogeneous cohort of 1376 HCM patients
Hathaway, J., Heliö, K., Saarinen, I., Tallila, J., Seppälä, E. H., Tuupanen, S., Turpeinen, H., Kangas‐Kontio, T., Schleit, J., Tommiska, J., et al. (2021) · BMC Cardiovascular Disorders · doi:10.1186/s12872-021-01927-5 · Cited by 53
van den Noort, F., van der Vaart, C. H., Grob, A. T. M., van de Waarsenburg, M. K., Slump, C. H., & van Stralen, M. (2019) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.20181 · Cited by 52 · cited 1×
“Deep learning algorithms have been utilized in various applications, including identifying muscle diseases [168], determining cone positioning, and segmentation of muscle imaging [169].”
MethodA Personalized Prediction Model to Risk Stratify Patients with Myelodysplastic Syndromes
Nazha, A., Komrokji, R. S., Meggendorfer, M., Mukherjee, S., Ali, N. E. A., Walter, W., Hütter, S., Padron, E., Madanat, Y. F., Sallman, D. A., et al. (2018) · Blood · doi:10.1182/blood-2018-99-114774 · Cited by 51
Can machine learning optimize the efficiency of the operating room in the era of COVID-19?
Rozario, N., & Rozario, D. (2020) · Canadian Journal of Surgery · doi:10.1503/cjs.016520 · Cited by 51
Niel, O., Bastard, P., Boussard, C., Hogan, J., Kwon, T., & Deschênes, G. (2018) · Pediatric Nephrology · doi:10.1007/s00467-018-4015-2 · Cited by 50
Precision prostate cancer surgery: an overview of new technologies and techniques
Checcucci, E., Amparore, D., LUCA, S. D., Riccardo Autorino, B., Fiori, C., & Porpiglia, F. (2019) · Minerva Urologica e Nefrologica · doi:10.23736/s0393-2249.19.03365-4 · Cited by 50
A Deep Learning System for Synthetic Knee Magnetic Resonance Imaging
Fayad, L. M., Parekh, V. S., Luna, R., Ko, C. C., Tank, D., Fritz, J. M., Ahlawat, S., & Jacobs, M. A. (2020) · Investigative Radiology · doi:10.1097/rli.0000000000000751 · Cited by 47
Essentials of Pharmaceutical Preformulation
Gaisford, S., & Saunders, M. (2012) · Wiley eBooks · doi:10.1002/9781118423226 · Cited by 42
Arsalan, M., Haider, A., Choi, J., & Park, K. R. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020124 · Cited by 42
Liu, S., Zhang, F., Xie, L., Wang, Y., Xiang, Q., Yue, Z., Feng, Y., Yang, Y., Li, J., Luo, L., et al. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.06.001 · Cited by 41
Kaddu, S., Soyer, H. P., Gabler, G., & Kovarik, C. (2009) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2008.12.007 · Cited by 40 · cited 1×
“Notably, there is a significant shortage of dermatologists, particularly in developing countries and remote regions, where increased medical resources, professional consultations, and clinical support are urgently needed [96,97].”
BackgroundMoraes, L. O., Pedreira, C. E., Barrena, S., López, A., & Orfao, A. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.06.014 · Cited by 39
Burgos-Artizzu, X. P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B., Vellvé, K., Eixarch, E., Crispi, F., Bonet-Carne, E., Bennasar, M., & Gratacos, E. (2021) · American Journal of Obstetrics & Gynecology MFM · doi:10.1016/j.ajogmf.2021.100462 · Cited by 39
Culvenor, A. G., Schache, A. G., Vicenzino, B., Pandy, M. G., Collins, N. J., Cook, J. L., & Crossley, K. M. (2014) · Arthritis Care & Research · doi:10.1002/acr.22313 · Cited by 38
Neural networks as a prognostic tool of surgical risk in lung resections
Esteva, H., Marchevsky, A., Núñez, T., Luna, C., & Esteva, M. (2002) · The Annals of Thoracic Surgery · doi:10.1016/s0003-4975(02)03418-5 · Cited by 38
Artificial intelligence can predict GFR decline during the course of ADPKD
Niel, O., Boussard, C., & Bastard, P. (2018) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2018.01.051 · Cited by 38
An update on prostate biopsy in the era of magnetic resonance imaging
Cicione, A. M., Nunzio, C. D., Manno, S., Damiano, R., Posti, A., Lima, E., Tubaro, A., & Balloni, F. (2018) · Minerva Urology and Nephrology · doi:10.23736/s0393-2249.18.03048-5 · Cited by 38
Application of machine learning in prediction of hydrotrope-enhanced solubilisation of indomethacin
Damiati, S. A., Martini, L. G. A., Smith, N. W., Lawrence, M. J., & Barlow, D. J. (2017) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2017.07.048 · Cited by 36
Prediction of Kidney Graft Rejection Using Artificial Neural Network
Tapak, L., Hamidi, O., Amini, P., & Poorolajal, J. (2017) · Healthcare Informatics Research · doi:10.4258/hir.2017.23.4.277 · Cited by 36
Cunningham, R. J., & Loram, I. D. (2020) · Journal of The Royal Society Interface · doi:10.1098/rsif.2019.0715 · Cited by 36
Li, X., Yang, L., Yuan, Z., Lou, J., Fan, Y., Shi, A., Huang, J., Zhao, M., & Wu, Y. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-021-02955-7 · Cited by 36
Deulofeu, M., Kolářová, L., Salvadó, V., Peña‐Méndez, E. M., Almáši, M., Štork, M., Pour, L., Boadas‐Vaello, P., Ševčı́ková, S., Havel, J., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-44215-1 · Cited by 35
Grimm, F., Edl, F., Kerscher, S. R., Nieselt, K., Gugel, I., & Schuhmann, M. U. (2020) · Acta Neurochirurgica · doi:10.1007/s00701-020-04447-x · Cited by 35
Lahner, E. (2005) · World Journal of Gastroenterology · doi:10.3748/wjg.v11.i37.5867 · Cited by 33
Autonomous artificial intelligence in diabetic retinopathy: From algorithm to clinical application
Channa, R., Wolf, R., & Abramoff, M. D. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820909900 · Cited by 33
Emerging use of artificial intelligence in inflammatory bowel disease
Kohli, A., Holzwanger, E. A., & Levy, A. N. (2020) · World Journal of Gastroenterology · doi:10.3748/wjg.v26.i44.6923 · Cited by 32
Alber, S., Kumar, S., Liu, J., Huang, Z., Paez, D., Hong, J. J., Chang, H.-W., Bhutani, T., Gensler, L. S., & Liao, W. J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.838636 · Cited by 31
Olsen, H. P., Slosser, J. L., Hildebrandt, T. T., & Wiesener, C. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3402974 · Cited by 28
Clinical applications of AI in MSK imaging: a liability perspective
Harvey, H. B., & Gowda, V. (2022) · Skeletal Radiology · doi:10.1007/s00256-021-03782-z · Cited by 28
Mole Mapping for Management of Pigmented Skin Lesions
Berk-Krauss, J., Polsky, D., & Stein, J. (2017) · Dermatologic Clinics · doi:10.1016/j.det.2017.06.004 · Cited by 27
Veronese, F., Branciforti, F., Zavattaro, E., Tarantino, V., Romano, V. C., Meiburger, K. M., Salvi, M., Seoni, S., & Savoia, P. (2021) · Diagnostics · doi:10.3390/diagnostics11030451 · Cited by 27
Ermer, S. C., Farney, R. J., Johnson, K. B., Orr, J. A., Egan, T. D., & Brewer, L. (2020) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000004498 · Cited by 26
Computerized dermatopathologic diagnosis
Potter, B., & Ronan, S. G. (1987) · Journal of the American Academy of Dermatology · doi:10.1016/s0190-9622(87)70183-2 · Cited by 25
Raji, C. A., Wang, M. B., Nguyen, N.-N., Owen, J. P., Palacios, E. M., Yuh, E. L., & Mukherjee, P. (2020) · Pediatric Radiology · doi:10.1007/s00247-020-04743-9 · Cited by 24
The role of artificial intelligence in paediatric neuroradiology
Pringle, C., Kilday, J., Kamaly-Asl, I. D., & Stivaros, S. M. (2022) · Pediatric Radiology · doi:10.1007/s00247-022-05322-w · Cited by 23
Optometrist's perspectives of Artificial Intelligence in eye care
Scanzera, A. C., Shorter, E. S., Kinnaird, C., Valikodath, N. G., Al-Khaled, T., Cole, E. D., Kravets, S., Hallak, J., McMahon, T., & Chan, R. V. P. (2022) · Journal of Optometry · doi:10.1016/j.optom.2022.06.006 · Cited by 23
Kaissis, G. A., Jungmann, F., Ziegelmayer, S., Lohöfer, F. K., Harder, F. N., Schlitter, A. M., Muckenhuber, A., Steiger, K., Schirren, R., Frieß, H., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9051250 · Cited by 22
Zhang, Y., Zhu, S., Yuan, Z., Li, Q., Ding, R., Bao, X., Zhen, T., Fu, Z., Fu, H., Xing, K., et al. (2020) · BMC Cancer · doi:10.1186/s12885-020-07626-2 · Cited by 21
New Horizons: Artificial Intelligence for Digital Breast Tomosynthesis
Goldberg, J. E., Reig, B., Lewin, A. A., Gao, Y., Heacock, L. C., Heller, S. L., & Moy, L. P. (2022) · Radiographics · doi:10.1148/rg.220060 · Cited by 19
Models of Neurons and Perceptrons: Selected Problems and Challenges
Bielecki, A. (2018) · Studies in computational intelligence · doi:10.1007/978-3-319-90140-4 · Cited by 18
Use of mobile applications in dermatology
Kaliyadan, F., & Ashique, K. T. (2020) · Indian Journal of Dermatology · doi:10.4103/ijd.ijd_422_20 · Cited by 18
Yang, Z., Zhang, M., Li, C., Meng, Z., Li, Y., Chen, Y., & Liu, L. (2022) · IEEE Access · doi:10.1109/access.2022.3178380 · Cited by 17
Ingrande, J., Gabriel, R. A., McAuley, J., Krasinska, K. M., Chien, A., & Lemmens, H. J. M. (2020) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000004897 · Cited by 16
Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC)
Kumar, S., Patnaik, S., & Dixit, A. (2020) · PeerJ · doi:10.7717/peerj.9656 · Cited by 16
Constructing a prediction model for difficult intubation of obese patients based on machine learning
Zhou, C.-M., Xue, Q., Ye, H., Wang, Y., Tong, J., Ji, M., & Yang, J. (2021) · Journal of Clinical Anesthesia · doi:10.1016/j.jclinane.2021.110278 · Cited by 16
Unexpected Inequality: Disparate-Impact From Artificial Intelligence in Healthcare Decisions.
Takshi, S. (2021) · PubMed · Cited by 16
Gaffney, R., & Rao, B. (2016) · Global Dermatology · doi:10.15761/god.1000156 · Cited by 15
Büchner, A., Kendlbacher, M., Nuhn, P., Tüllmann, C., Haseke, N., Stief, C. G., & Staehler, M. D. (2011) · Clinical Genitourinary Cancer · doi:10.1016/j.clgc.2011.10.001 · Cited by 14
How Artificial Intelligence for Healthcare Look Like in the Future?
Denecke, K., & Gabarrón, E. (2021) · Studies in health technology and informatics · doi:10.3233/shti210301 · Cited by 14
Machine Learning in Bioequivalence: Towards Identifying an Appropriate Measure of Absorption Rate
Karalis, V. D. (2022) · Applied Sciences · doi:10.3390/app13010418 · Cited by 14
Self-citationNeural network-based detection of esophageal intubation in anesthetized patients
León, M. A., & Rasanen, J. O. (1996) · The Journal of Clinical Monitoring · doi:10.1007/bf02078138 · Cited by 13
Patel, S. S., Sekeres, M. A., & Nazha, A. (2017) · Leukemia & lymphoma/Leukemia and lymphoma · doi:10.1080/10428194.2017.1307361 · Cited by 13
Wu, Y., Shen, Y. G., & Sun, H. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/7758206 · Cited by 13
Karalis, V. D. (2023) · Applied Sciences · doi:10.3390/app13042257 · Cited by 13
Self-citationProgress and prospects for artificial intelligence in clinical practice: learning from COVID-19
Ferrara, P., Battiato, S., & Polosa, R. (2022) · Internal and Emergency Medicine · doi:10.1007/s11739-022-03080-z · Cited by 10
Stivaros, S. M., Radon, M. R., Mileva, R., Connolly, D. J. A., Cowell, P. E., Hoggard, N., Wright, N., Tang, V., Gledson, A., Batty, R., et al. (2015) · Pediatric Radiology · doi:10.1007/s00247-015-3444-3 · Cited by 9
Chronic Pain Challenge: A Statistical Machine-learning Method for Chronic Pain Assessment
Navani, A., & Li, G. (2016) · Journal on Recent Advances in Pain · doi:10.5005/jp-journals-10046-0048 · Cited by 7
Quality use of artificial intelligence in medical imaging: What do radiologists need to know?
Goergen, S., Frazer, H. M. L., & Reddy, S. (2022) · Journal of Medical Imaging and Radiation Oncology · doi:10.1111/1754-9485.13379 · Cited by 7
An In Silico Approach toward the Appropriate Absorption Rate Metric in Bioequivalence
Karalis, V. D. (2023) · Pharmaceuticals · doi:10.3390/ph16050725 · Cited by 7
Self-citationPseudo-marginal Bayesian inference for Gaussian process latent variable models
Gadd, C., Wade, S., & Shah, A. A. (2021) · Machine Learning · doi:10.1007/s10994-021-05971-2 · Cited by 5
Kang, H., Witanto, J. N., Pratama, K., Lee, D., Choi, K., Choi, S., Kim, K., Kim, M., Kim, J., Kim, Y., et al. (2022) · Neuro-Oncology · doi:10.1093/neuonc/noac174.282 · Cited by 2
Charles River licenses ERS Genomics' CRISPR/Cas9 technology
Philippidis, A. (2018) · Inside Precision Medicine · doi:10.1089/clinomi.05.01.04 · Cited by 1
Research works printed but not linked: 1
- Russell, S., and Norvig, P. (2021). Artificial Intelligence: A Modern Approach, Pearson. [4th ed.].
Cited by
Sorted by citations
Fahim, Y. A., Hasani, I. W., Kabba, S., & Ragab, W. M. (2025) · European Journal of Medical Research · doi:10.1186/s40001-025-03196-w · Cited by 329
Kale, M. B., Wankhede, N. L., Pawar, R. S., Ballal, S., Kumawat, R., Goswami, M., Khalid, M., Taksande, B. G., Upaganlawar, A. B., Umekar, M. J., et al. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102497 · Cited by 208
AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis
Alum, E. U. (2025) · Discover Oncology · doi:10.1007/s12672-025-02064-7 · Cited by 159
Next-Generation Immunotherapy: Advancing Clinical Applications in Cancer Treatment
Garg, P. K., Pareek, S., Kulkarni, P., Horne, D., Salgia, R., & Singhal, S. S. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13216537 · Cited by 131
Parvin, N., Joo, S., Jung, J. H., & Mandal, T. K. (2025) · Nanomaterials · doi:10.3390/nano15120895 · Cited by 131
Metadata
- Authors
- Karalis, V. D.
- Affiliations
- National and Kapodistrian University of Athens (GR)
- Corresponding author
- Karalis, Vangelis D.
- ORCID
- Karalis, Vangelis D.
- Venue
- Applied Biosciences
- Publisher
- MDPI AG
- Published
- 2024-01-01
- Volume
- 3
- Issue
- 1
- Pages
- 14-44
- ISSN
- 2813-0464 (ISSN-L)
- OpenAlex
- W4390509775
- Cited by
- 255
- References
- 188
- Citations by year
- 2026: 64 · 2025: 135 · 2024: 55
- FWCI
- 24.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Biosciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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 › Artificial Intelligence Applications
- Keywords
- artificial intelligence in healthcare, clinical practice, diagnosis, personalized treatment, legal and ethical considerations, data privacy, data security, regulatory framework, explainability, transparency, informed consent, product liability
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