Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions
Open accessAbstract
Cancer remains one of the leading causes of mortality worldwide, driving the need for innovative approaches in research and treatment. Artificial intelligence (AI) has emerged as a powerful tool in oncology, with the potential to revolutionize cancer diagnosis, treatment, and management. This paper reviews recent advancements in AI applications within cancer research, focusing on early detection through computer-aided diagnosis, personalized treatment strategies, and drug discovery. We survey AI-enhanced diagnostic applications and explore AI techniques such as deep learning, as well as the integration of AI with nanomedicine and immunotherapy for cancer care. Comparative analyses of AI-based models versus traditional diagnostic methods are presented, highlighting AI’s superior potential. Additionally, we discuss the importance of integrating social determinants of health to optimize cancer care. Despite these advancements, challenges such as data quality, algorithmic biases, and clinical validation remain, limiting widespread adoption. The review concludes with a discussion of the future directions of AI in oncology, emphasizing its potential to reshape cancer care by enhancing diagnosis, personalizing treatments and targeted therapies, and ultimately improving patient outcomes.
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
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?
AI-enabled digital health tools are maturing rapidly.
“AI-enabled digital health tools are maturing rapidly and can serve to supplement care access where there are fewer providers physically available, particularly in under-resourced regions.3”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [3]
The Introduction and Conclusions describe rapid advances in oncology AI and its potential to expand access to cancer care. Section 6 also discusses healthcare disparities. None of these passages addresses supplementing care where providers are physically scarce, particularly in under-resourced regions; the paper’s support is narrower than the full citing sentence. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAI-enabled digital health tools can serve to supplement care access where there are fewer providers physically available, particularly in under-resourced regions.
“AI-enabled digital health tools are maturing rapidly and can serve to supplement care access where there are fewer providers physically available, particularly in under-resourced regions.3”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [3]
The Introduction says AI diagnostic tools can expand access to pathology and radiology interpretation, and Section 2 describes their improving performance and role as aids to clinicians. The paper does not specifically say that digital health tools supplement care where providers are physically scarce, particularly in under-resourced regions; the claim extends its oncology-diagnostics discussion to a broader setting. (LLM, conf=0.84)
Experimental — accuracy not yet measuredGenerative AI systems can summarize studies, identify key themes, and surface relevant literature more efficiently than manual review.
“Generative AI (GenAI) systems can summarize studies, identify key themes, and surface relevant literature more efficiently than manual review.8,9”
From Artificial Intelligence in Oncology: Practical Applications Across Clinical Care, Scholarship, and Translation. (2026) · cites this paper as [9]
The Introduction to AI in Cancer Research and §6.2, Harvesting Unstructured Data Potential (page numbers unavailable), discuss AI pattern recognition and extracting information from clinical notes, not reviewing research literature. Searches for generative AI, study summarization, literature reviews, and comparisons with manual review found no passage addressing the attributed claim. (LLM, conf=0.74)
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.
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R. L., Soerjomataram, I., & Jemal, A. (2024) · CA: a cancer journal for clinicians · doi:10.3322/caac.21834 · Cited by 27,064 · see citing works
The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth
de Visser, K. E., & Joyce, J. A. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.02.016 · Cited by 4,059 · see citing works
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · see citing works
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-021-01614-0 · Cited by 3,212 · see citing works
Tumor microenvironment as a therapeutic target in cancer
Xiao, Y. Q., & Yu, D. (2020) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2020.107753 · Cited by 1,977 · see citing works
Artificial intelligence in cancer imaging: Clinical challenges and applications
Bi, W. L., Hosny, A., Schabath, M. B., Giger, M. L., Birkbak, N. J., Mehrtash, A., Allison, T., Arnaout, O., Abbosh, C., Dunn, I. F., et al. (2019) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21552 · Cited by 1,846 · see citing works
Mortality due to cancer treatment delay: systematic review and meta-analysis
Hanna, T. P., King, W. D., Thibodeau, S., Jalink, M., Paulin, G. A., Harvey-Jones, E., O’Sullivan, D. E., Booth, C. M., Sullivan, R., & Aggarwal, A. (2020) · BMJ · doi:10.1136/bmj.m4087 · Cited by 1,742 · see citing works
Artificial intelligence: A powerful paradigm for scientific research
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726 · see citing works
Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C. P., et al. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002686 · Cited by 1,449 · see citing works
Benjamens, S., Dhunnoo, P., & Meskó, B. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00324-0 · Cited by 1,215 · see citing works
Neal, R. D., Tharmanathan, P., France, B., Din, N. U., Cotton, S., Fallon‐Ferguson, J., Hamilton, W. T., Hendry, A., Hendry, M., Lewis, R. A., et al. (2015) · British Journal of Cancer · doi:10.1038/bjc.2015.48 · Cited by 1,013 · see citing works
Carrera, P. M., Kantarjian, H. M., & Blinder, V. S. (2018) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21443 · Cited by 876 · see citing works
Predicting cancer outcomes with radiomics and artificial intelligence in radiology
Bera, K., Braman, N., Gupta, A., Velcheti, V., & Madabhushi, A. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-021-00560-7 · Cited by 819 · see citing works
Progress in Nanomedicine: Approved and Investigational Nanodrugs.
Ventola, C. L. (2017) · Europe PMC (PubMed Central) · Cited by 795 · see citing works
Artificial intelligence for multimodal data integration in oncology
Lipkova, J., Chen, R. J., Chen, B., Lu, M. Y., Barbieri, M., Shao, D., Vaidya, A. J., Chen, C., Zhuang, L., Williamson, D. F. K., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.012 · Cited by 777 · see citing works
Urban, G., Tripathi, P. V., Alkayali, T., Mittal, M., Jalali, F., Karnes, W. E., & Baldi, P. (2018) · Gastroenterology · doi:10.1053/j.gastro.2018.06.037 · Cited by 746 · see citing works
Nanopharmaceuticals and Nanomedicines Currently on the Market: Challenges and Opportunities
Farjadian, F., Ghasemi, A., Gohari, O., Roointan, A., Karimi, M., & Hamblin, M. R. (2018) · Nanomedicine · doi:10.2217/nnm-2018-0120 · Cited by 678 · see citing works
Medical Care Costs Associated with Cancer Survivorship in the United States
Mariotto, A. B., Enewold, L., Zhao, J., Zeruto, C. A., & Yabroff, K. R. (2020) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-19-1534 · Cited by 667 · see citing works
AI-based pathology predicts origins for cancers of unknown primary
Lu, M. Y., Chen, T. Y., Williamson, D. F. K., Zhao, M., Shady, M., Lipkova, J., & Mahmood, F. (2021) · Nature · doi:10.1038/s41586-021-03512-4 · Cited by 636 · see citing works
Bias in artificial intelligence algorithms and recommendations for mitigation
Nazer, L. H., Zatarah, R., Waldrip, S., Ke, J. X. C., Moukheiber, M., Khanna, A. K., Hicklen, R. S., Moukheiber, L., Moukheiber, D., Ma, H., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000278 · Cited by 621 · cited 1× · see citing works
“Moreover, achieving AI’s full impact in healthcare will necessitate a collaborative effort from clinicians, AI researchers, patient advocacy groups, health equity scholars, government agencies, and industry stakeholders [135].”
Challenges in diagnosis of pancreatic cancer
Zhang, L., Sanagapalli, S., & Stoita, A. (2018) · World Journal of Gastroenterology · doi:10.3748/wjg.v24.i19.2047 · Cited by 600 · see citing works
A pathology foundation model for cancer diagnosis and prognosis prediction
Wang, X., Zhao, J., Marostica, E., Yuan, W., Jin, J., Zhang, J., Li, R., Tang, H., Wang, K., Li, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07894-z · Cited by 580 · see citing works
Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers
Trebeschi, S., Drago, S. G., Birkbak, N. J., Kurilova, I., Calin, A., Pizzi, A. D., Lalezari, F., Lambregts, D. M. J., Rohaan, M. W., Parmar, C., et al. (2019) · Annals of Oncology · doi:10.1093/annonc/mdz108 · Cited by 567 · see citing works
Neural network models and deep learning
Kriegeskorte, N., & Golan, T. (2019) · Current Biology · doi:10.1016/j.cub.2019.02.034 · Cited by 554 · see citing works
Nanomedicines: addressing the scientific and regulatory gap
Tinkle, S. S., McNeil, S. E., Mühlebach, S., Bawa, R., Borchard, G., Barenholz, Y., Tamarkin, L., & Desai, N. P. (2014) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12403 · Cited by 552 · see citing works
Global Disparities of Cancer and Its Projected Burden in 2050
Bizuayehu, H. M., Ahmed, K. Y., Kibret, G. D., Dadi, A. F., Belachew, S. A., Bagade, T., Tegegne, T. K., Venchiarutti, R. L., Kibret, K. T., Habte, A., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.43198 · Cited by 456 · see citing works
Luo, H., Xu, G., Li, C., He, L., Luo, L., Wang, Z., Jing, B., Deng, Y., Jin, Y., Yin, L., et al. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30637-0 · Cited by 417 · see citing works
Saha, A., Bosma, J. S., Twilt, J. J., van Ginneken, B., Bjartell, A. S., Padhani, A. R., Bonekamp, D., Villeirs, G. M., Salomon, G., Giannarini, G., et al. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(24)00220-1 · Cited by 373 · see citing works
High-sensitivity nanosensors for biomarker detection
Swierczewska, M., Liu, G., Lee, S., & Chen, X. S. (2011) · Chemical Society Reviews · doi:10.1039/c1cs15238f · Cited by 363 · see citing works
Boehm, K. M., Aherne, E. A., Ellenson, L., Nikolovski, I., Alghamdi, M., Vázquez-García, I., Zamarin, D., Long Roche, K., Liu, Y., Patel, D., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00388-9 · Cited by 357 · see citing works
A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs
Rangayyan, R. M., Ayres, F. J., & Desautels, J. E. L. (2006) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2006.09.003 · Cited by 338 · see citing works
Development of Pharmaceutical Nanomedicines: From the Bench to the Market
Halwani, A. A. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14010106 · Cited by 337 · see citing works
Prasad, M., Lambe, U. P., Brar, B., Shah, I., Manimegalai, J., Ranjan, K., Rao, R., Kumar, S., Mahant, S., Khurana, S. K., et al. (2017) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2017.11.026 · Cited by 328 · see citing works
Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning
Yang, Y., Yan, L., Zhang, X., Han, Y., Nan, H., Hu, Y., Hu, B., Yan, S., Zhang, J., Cheng, D.-L., et al. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00804 · Cited by 317 · see citing works
Artificial intelligence in oncology: current applications and future perspectives
Luchini, C., Pea, A., & Scarpa, A. (2022) · British Journal of Cancer · doi:10.1038/s41416-021-01633-1 · Cited by 281 · see citing works
Park, S., Ock, C.-Y., Kim, H., Pereira, S., Park, S., Ma, M., Choi, S., Kim, S., Shin, S., Aum, B. J., et al. (2022) · Journal of Clinical Oncology · doi:10.1200/jco.21.02010 · Cited by 280 · see citing works
Large language models to identify social determinants of health in electronic health records
Guevara, M., Chen, S., Thomas, S., Chaunzwa, T. L., Franco, I., Kann, B. H., Moningi, S., Qian, J. M., Goldstein, M., Harper, S., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-023-00970-0 · Cited by 278 · see citing works
Long-term cancer survival prediction using multimodal deep learning
Vale-Silva, L. A., & Rohr, K. (2021) · Scientific Reports · doi:10.1038/s41598-021-92799-4 · Cited by 268 · see citing works
Improved prediction of immune checkpoint blockade efficacy across multiple cancer types
Chowell, D., Yoo, S., Valero, C., Pastore, A., Krishna, C., Lee, M., Hoen, D. R., Shi, H., Kelly, D. W., Patel, N., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-01070-8 · Cited by 265 · see citing works
Co-Learning Feature Fusion Maps From PET-CT Images of Lung Cancer
Kumar, A., FULHAM, M. J., Feng, D., & Kim, J. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2923601 · Cited by 258 · see citing works
Hirani, R., Noruzi, K. A., Khuram, H., Hussaini, A. S., Aifuwa, E., Ely, K., Lewis, J. M., Gabr, A. E., Smiley, A., Tiwari, R. K., et al. (2024) · Life · doi:10.3390/life14050557 · Cited by 256 · see citing works
Self-citationArtificial intelligence aids in development of nanomedicines for cancer management
Tan, P., Chen, X., Zhang, H., Wei, Q., & Luo, K. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.01.005 · Cited by 254 · see citing works
Artificial intelligence-based methods for fusion of electronic health records and imaging data
Mohsen, F., Ali, H., El Hajj, N., & Shah, Z. (2022) · Scientific Reports · doi:10.1038/s41598-022-22514-4 · Cited by 250 · see citing works
Death or Debt? National Estimates of Financial Toxicity in Persons with Newly-Diagnosed Cancer
Gilligan, A. M., Alberts, D. S., Roe, D. J., & Skrepnek, G. H. (2018) · The American Journal of Medicine · doi:10.1016/j.amjmed.2018.05.020 · Cited by 234 · see citing works
A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN
Naskath, J., Sivakamasundari, G., & Begum, A. A. S. (2022) · Wireless Personal Communications · doi:10.1007/s11277-022-10079-4 · Cited by 218 · see citing works
Cancer immunotherapy beyond immune checkpoint inhibitors
Marin-Acevedo, J. A., Soyano, A. E., Dholaria, B., Knutson, K. L., & Lou, Y. (2018) · Journal of Hematology & Oncology · doi:10.1186/s13045-017-0552-6 · Cited by 213 · see citing works
Image based Machine Learning for identification of macrophage subsets
Rostam, H. M., Reynolds, P. M., Alexander, M. R., Gadegaard, N., & Ghaemmaghami, A. M. (2017) · Scientific Reports · doi:10.1038/s41598-017-03780-z · Cited by 196 · see citing works
Artificial intelligence to bring nanomedicine to life
Serov, N., & Vinogradov, V. V. (2022) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2022.114194 · Cited by 194 · see citing works
Guest editorial computer-aided diagnosis in medical imaging
Giger, M. L., Karssemeijer, N., & Armato, S. G. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2001.974915 · Cited by 187 · see citing works
Lee, B., Yoon, S., Lee, J.-W., Kim, Y., Chang, J., Yun, J., Ro, J., Lee, J.-S., & Lee, J.-H.-O. (2020) · ACS Nano · doi:10.1021/acsnano.0c06809 · Cited by 177 · see citing works
Independent real‐world application of a clinical‐grade automated prostate cancer detection system
da Silva, L. M., Pereira, E. M., Salles, P. G., Godrich, R., Ceballos, R., Kunz, J. D., Casson, A., Viret, J., Chandarlapaty, S., Ferreira, C. G., et al. (2021) · The Journal of Pathology · doi:10.1002/path.5662 · Cited by 175 · see citing works
An introduction to digital determinants of health
Chidambaram, S., Jain, B., Jain, U., Mwavu, R., Baru, R., Thomas, B., Greaves, F., Jayakumar, S., Jain, P., Rojo, M., et al. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000346 · Cited by 175 · see citing works
Artificial intelligence predictive model for hormone therapy use in prostate cancer
Spratt, D. E., Tang, S., Sun, Y., Huang, H.-C., Chen, E., Mohamad, O., Armstrong, A. J., Tward, J. D., Nguyen, P. L., Lang, J. M., et al. (2023) · NEJM Evidence · doi:10.1056/evidoa2300023 · Cited by 153 · see citing works
Computational Staining of Pathology Images to Study the Tumor Microenvironment in Lung Cancer
Wang, S., Rong, R., Yang, D. M., Fujimoto, J., Yan, S. X., Cai, L., Yang, L., Luo, D., Behrens, C., Parra, E. R., et al. (2020) · Cancer Research · doi:10.1158/0008-5472.can-19-1629 · Cited by 149 · see citing works
The artificial intelligence and machine learning in lung cancer immunotherapy
Gao, Q., Yang, L., Lu, M., Jin, R., Ye, H., & Ma, T. (2023) · Journal of Hematology & Oncology · doi:10.1186/s13045-023-01456-y · Cited by 148 · cited 1× · see citing works
“Better understanding of these biomarkers is crucial for assessing and predicting immunotherapy responses [107].”
Namas, R. A., Yoram Vodovotz, B., Almahmoud, K., Abdul-Malak, O. M., Zaaqoq, A. M., Namas, R. A., Mi, Q., Barclay, D. A., Zuckerbraun, B. S., Peitzman, A. B., et al. (2014) · Annals of Surgery · doi:10.1097/sla.0000000000001001 · Cited by 141 · see citing works
Yoo, S., Fitzgerald, C. W. R., Cho, B. A., Fitzgerald, B. G., Han, C. Y., Koh, E. S., Pandey, A., Sfreddo, H. J., Crowley, F., Rudshteyn, M., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03398-5 · Cited by 139 · see citing works
Analysis of free text in electronic health records for identification of cancer patient trajectories
Jensen, K., Soguero-Ruíz, C., Mikalsen, K. Ø., Lindsetmo, R., Kouskoumvekaki, I. A., Girolami, M., Skrøvseth, S. O., & Augestad, K. M. (2017) · Scientific Reports · doi:10.1038/srep46226 · Cited by 133 · see citing works
Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review
Ramadan, S. Z. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/9162464 · Cited by 118 · see citing works
Yin, X., Liao, H., Hong, Y., Lin, N., Li, S., Xiang, Y., & Ma, X. (2022) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2022.08.002 · Cited by 110 · see citing works
Deep Learning Approaches for Medical Image Analysis and Diagnosis
Thakur, G. K., Thakur, A., Kulkarni, S., Khan, N., & Khan, S. (2024) · Cureus · doi:10.7759/cureus.59507 · Cited by 106 · see citing works
Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry
Li, Y., Mahjoubfar, A., Chen, C. L., Niazi, K. R., Pei, L., & Jalali, B. (2019) · Scientific Reports · doi:10.1038/s41598-019-47193-6 · Cited by 104 · see citing works
Social determinants of health and US cancer screening interventions: A systematic review
Korn, A. R., Walsh‐Bailey, C., Correa-Méndez, M., DelNero, P. F., Pilar, M. R., Sandler, B., Brownson, R. C., Emmons, K. M., & Oh, A. (2023) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21801 · Cited by 104 · see citing works
Bao, H., Sun, X., Zhang, Y. S., et al. (2020) · Cancer Medicine · doi:10.1002/cam4.3296 · Cited by 102 · see citing works
Artificial Intelligence in Genetics
Vilhekar, R. S., & Rawekar, A. (2024) · Cureus · doi:10.7759/cureus.52035 · Cited by 98 · see citing works
Systematic Review of PD-1/PD-L1 Inhibitors in Oncology: From Personalized Medicine to Public Health
Chang, E., Pelosof, L. C., Lemery, S. J., Gong, Y. U. T., Goldberg, K. B., Farrell, A. T., Keegan, P., Veeraraghavan, J., Wei, G., Blumenthal, G. M., et al. (2021) · The Oncologist · doi:10.1002/onco.13887 · Cited by 90 · see citing works
Understanding the black-box: towards interpretable and reliable deep learning models
Qamar, T., & Bawany, N. Z. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1629 · Cited by 88 · see citing works
Mohan, G., & Chattopadhyay, S. K. (2020) · JAMA Oncology · doi:10.1001/jamaoncol.2020.1460 · Cited by 87 · see citing works
Alard, E. L., Butnariu, A.-B., Grillo, M., Kirkham, C., Zinovkin, D. A., Newnham, L. J., Macciochi, J., & Pranjol, M. Z. I. (2020) · Cancers · doi:10.3390/cancers12071826 · Cited by 86 · see citing works
The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review
Istasy, P., Lee, W. S., Iansavitchene, A., Upshur, R. E. G., Gyawali, B., Burkell, J., Sadiković, B., Lazo‐Langner, A., & Chin-Yee, B. H. (2022) · Journal of Medical Internet Research · doi:10.2196/39748 · Cited by 85 · see citing works
Liu, M., Wu, J., Wang, N., Zhang, X., Bai, Y., Guo, J., Zhang, L., Liu, S., & Tao, K. (2023) · PLOS ONE · doi:10.1371/journal.pone.0273445 · Cited by 84 · see citing works
Guo, L., Wang, Z., Du, Y., Mao, J., Zhang, J., Yu, Z., Guo, J., Zhao, J., Zhou, H., Wang, H., et al. (2020) · Cancer Cell International · doi:10.1186/s12935-020-01274-z · Cited by 65 · see citing works
Kumar, Y., Shrivastav, S., Garg, K., Modi, N., Wiltos, K., Woźniak, M., & Ijaz, M. F. (2024) · Scientific Reports · doi:10.1038/s41598-024-75876-2 · Cited by 65 · see citing works
Jin, W., Yang, Q., Chi, H., Wei, K., Zhang, P., Zhao, G., Chen, S., Xia, Z., & Li, X. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1025330 · Cited by 64 · see citing works
Sidey-Gibbons, C. J., Pfob, A., Asaad, M., Boukovalas, S., Lin, Y.-L., Selber, J. C., Butler, C. E., & Offodile, A. C. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00088 · Cited by 60 · see citing works
Niikura, R., Aoki, T., Shichijo, S., et al. (2021) · Endoscopy · doi:10.1055/a-1660-6500 · Cited by 60 · see citing works
Li, T., Li, Y., Zhu, X., He, Y., Wu, Y., Ying, T., & Xie, Z. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.02.007 · Cited by 59 · see citing works
Faruqui, N., Yousuf, M. A., Kateb, F., Hamid, M. A., & Monowar, M. M. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e21520 · Cited by 59 · see citing works
Yamada, A., Niikura, R., Otani, K., et al. (2020) · Endoscopy · doi:10.1055/a-1266-1066 · Cited by 58 · see citing works
Shirzad, M., Salahvarzi, A., Razzaq, S., Javid‐Naderi, M. J., Rahdar, A., Fathi‐karkan, S., Ghadami, A., Kharaba, Z. J., & Ferreira, L. F. R. (2025) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2025.104653 · Cited by 58 · see citing works
Huang, L., Huhulea, E. N., Abraham, E., Bienenstock, R., Aifuwa, E., Hirani, R., Schulhof, A., Tiwari, R. K., & Etienne, M. (2025) · Medicina · doi:10.3390/medicina61020358 · Cited by 58 · see citing works
Self-citationArtificial intelligence-aided optical imaging for cancer theranostics
Xu, M., Chen, Z., Zheng, J., Zhao, Q., & Yuan, Z. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.06.003 · Cited by 55 · see citing works
Darbandi, M. R., Darbandi, M., Darbandi, S., Bado, I., Hadizadeh, M. R., & Khorshid, H. R. K. (2024) · European Journal of Cancer · doi:10.1016/j.ejca.2024.114227 · Cited by 54 · see citing works
Artificial intelligence: illuminating the depths of the tumor microenvironment
Xie, T., Huang, A., Yan, H., Ju, X., Xiang, L., & Yuan, J. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05609-6 · Cited by 54 · see citing works
Self-citationAgrahari, V., Choonara, Y. E., Mosharraf, M., Patel, S. K., & Zhang, F. (2024) · Pharmaceutical Research · doi:10.1007/s11095-024-03798-9 · Cited by 54 · see citing works
Artificial Intelligence for Autonomous Molecular Design: A Perspective
Joshi, R. P., & Kumar, N. (2021) · Molecules · doi:10.3390/molecules26226761 · Cited by 52 · see citing works
Computer-aided detection (CAD) in mammography: Does it help the junior or the senior radiologist?
Balleyguier, C., Kinkel, K., Fermanian, J., et al. (2005) · European Journal of Radiology · doi:10.1016/j.ejrad.2004.11.021 · Cited by 51 · see citing works
Ambreen, S., Umar, M., Noor, A., Jain, H., & Ali, R. (2024) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2024.117164 · Cited by 50 · see citing works
Addendum: International evaluation of an AI system for breast cancer screening
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A. W., et al. (2020) · Nature · doi:10.1038/s41586-020-2679-9 · Cited by 42 · see citing works
Artificial Intelligence-based Radiomics in the Era of Immuno-oncology
Kang, C., Duarte, S. E., Kim, H. S., Kim, E., Park, J., Lee, A., Kim, Y., Kim, L., Cho, S., Oh, Y., et al. (2022) · The Oncologist · doi:10.1093/oncolo/oyac036 · Cited by 42 · see citing works
Synergistic Potential of Nanomedicine in Prostate Cancer Immunotherapy: Breakthroughs and Prospects
Jiang, Y., Wang, C., Zu, C., Rong, X., Yu, Q., & Jiang, J. (2024) · International Journal of Nanomedicine · doi:10.2147/ijn.s466396 · Cited by 41 · see citing works
Zhou, S., Xie, Y., Feng, X., Li, Y., Shen, L., & Chen, Y. (2025) · Cancer Letters · doi:10.1016/j.canlet.2025.217555 · Cited by 40 · see citing works
Marx, C., Malich, A., Facius, M., Grebenstein, U., Sauner, D., Pfleiderer, S. O. R., & Kaiser, W. A. (2003) · European Journal of Radiology · doi:10.1016/s0720-048x(03)00144-x · Cited by 39 · see citing works
Sedghi, A., Mehrtash, A., Jamzad, A., Amalou, A., Wells, W. M., Kapur, T., Kwak, J. T., Türkbey, B., Choyke, P. L., Pinto, P. A., et al. (2020) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-020-02172-5 · Cited by 39 · see citing works
Vollmer, A., Hartmann, S., Vollmer, M., Shavlokhova, V., Brands, R. C., Kübler, A. C., Wollborn, J., Hassel, F., Couillard‐Després, S., Lang, G. M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-56172-5 · Cited by 39 · see citing works
Yu, Z., Yang, X., Dang, C., Wu, S., Adekkanattu, P., Pathak, J., George, T. J., Hogan, W. R., Guo, Y., Bian, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.04949 · Cited by 38 · see citing works
Malich, A., Azhari, T., Böhm, T., et al. (2000) · European Journal of Radiology · doi:10.1016/s0720-048x(00)00189-3 · Cited by 37 · see citing works
Yu, Z., Peng, C., Yang, X., Dang, C., Adekkanattu, P., Gopal Patra, B., Peng, Y., Pathak, J., Wilson, D. L., Chang, C.-Y., et al. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104642 · Cited by 37 · see citing works
Carreras, J., Roncador, G., & Hamoudi, R. (2022) · Cancers · doi:10.3390/cancers14215318 · Cited by 36 · see citing works
Wang, P., Wang, S., Liu, F., et al. (2023) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.2c01000 · Cited by 35 · see citing works
Urine cell image recognition using a deep‐learning model for an automated slide evaluation system
Kaneko, M., Tsuji, K., Masuda, K., et al. (2021) · British Journal of Urology · doi:10.1111/bju.15518 · Cited by 34 · see citing works
Taha, B. A., Abdulrahm, Z. M., Addie, A. J., Haider, A. J., Alkawaz, A. N., Yaqoob, I. A. M., & Arsad, N. (2025) · Talanta · doi:10.1016/j.talanta.2025.127693 · Cited by 34 · see citing works
Liu, B., Zhu, Y., Yang, Z., Yan, H. H. N., Leung, S. Y., & Shi, J. (2023) · Cancer Research · doi:10.1158/0008-5472.can-23-1100 · Cited by 31 · see citing works
Orenuga, S., Jordache, P., Mirzai, D. A., Monteros, T., Gonzalez, E., Madkoor, A., Hirani, R., Tiwari, R. K., & Etienne, M. (2025) · Life · doi:10.3390/life15030424 · Cited by 31 · see citing works
Self-citationLabel-free tumor cells classification using deep learning and high-content imaging
Piansaddhayanon, C., Koracharkornradt, C., Laosaengpha, N., Tao, Q., Ingrungruanglert, P., Israsena, N., Chuangsuwanich, E., & Sriswasdi, S. (2023) · Scientific Data · doi:10.1038/s41597-023-02482-8 · Cited by 28 · see citing works
Bang, Y. H., Lee, C., Bang, K., Kim, H., Kim, K., Jeong, J. H., Park, I., Ryoo, B., Lee, D. K., Choi, H. J., et al. (2024) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-24-1265 · Cited by 28 · see citing works
Cancer immunotherapy from biology to nanomedicine
Abdelbaky, S. B., Ibrahim, M. T., Samy, H., Mohamed, M., Mohamed, H., Mustafa, M., Abdelaziz, M. M., Forrest, M. L., & Khalil, I. A. H. (2021) · Journal of Controlled Release · doi:10.1016/j.jconrel.2021.06.025 · Cited by 27 · see citing works
Takita, H., Matsumoto, T., Tatekawa, H., et al. (2023) · Radiology · doi:10.1148/radiol.223016 · Cited by 27 · see citing works
Hayat, H., Nukala, A., Nyamira, A., Fan, J., & Wang, P. (2021) · Biomedical Materials · doi:10.1088/1748-605x/ac15b2 · Cited by 26 · see citing works
Artificial Intelligence-Assisted Transcriptomic Analysis to Advance Cancer Immunotherapy
Gui, Y., He, X., Yu, J., & Jing, J. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12041279 · Cited by 26 · see citing works
Stabellini, N., Nazha, A., Agrawal, N., Huhn, M., Shanahan, J., Hamerschlak, N., Waite, K. A., Barnholtz‐Sloan, J. S., & Montero, A. J. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.22.00143 · Cited by 26 · see citing works
Neal, R. D., Robbé, I., Lewis, M., et al. (2014) · Primary Health Care Research & Development · doi:10.1017/s1463423614000516 · Cited by 25 · see citing works
Immune Checkpoint Inhibitors: Recent Clinical Advances and Future Prospects
Banday, A. H., & Abdalla, M. (2022) · Current Medicinal Chemistry · doi:10.2174/0929867329666220819115849 · Cited by 25 · see citing works
Deep learning-based multimodal spatial transcriptomics analysis for cancer
Rajdeo, P., Aronow, B. J., & Prasath, V. B. S. (2024) · Advances in cancer research · doi:10.1016/bs.acr.2024.08.001 · Cited by 23 · see citing works
Samarasena, J., Yang, D. J., & Berzin, T. M. (2023) · Gastroenterology · doi:10.1053/j.gastro.2023.07.010 · Cited by 21 · see citing works
Chen, Y., Han, H., Cheng, J., Cheng, Q., Zhu, S., Zhan, P., Liu, H., Song, Y., & Lv, T. (2024) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-024-03734-1 · Cited by 20 · see citing works
Path-BigBird: An AI-Driven Transformer Approach to Classification of Cancer Pathology Reports
Chandrashekar, M., Lyngaas, I., Hanson, H. A., Gao, S. B., Wu, X., & Gounley, J. P. (2024) · JCO Clinical Cancer Informatics · doi:10.1200/cci.23.00148 · Cited by 19 · see citing works
From multi-omics to predictive biomarker: AI in tumor microenvironment
Hai, L., Jiang, Z., Zhang, H., & Sun, Y. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1514977 · Cited by 19 · see citing works
Lin, G., Wang, X.-J., Ye, H., & Cao, W. (2023) · Technology in Cancer Research and Treatment · doi:10.1177/15330338231218227 · Cited by 18 · see citing works
Thompson, W., Brecht, H.-P. F., Ivanov, V., et al. (2023) · Journal of Biomedical Optics · doi:10.1117/1.jbo.28.3.036001 · Cited by 16 · see citing works
Experimenting with ChatGPT: Concerns for academic medicine
Hirani, R., Farabi, B., & Marmon, S. (2023) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2023.04.045 · Cited by 13 · see citing works
Self-citationHachache, R., Yahyaouy, A., Riffi, J., Tairi, H., Abibou, S., Adoui, M. E., & Benjelloun, M. (2024) · BMC Cancer · doi:10.1186/s12885-024-13049-0 · Cited by 13 · see citing works
Impact of a CAD system in a screen-film mammography screening program: A prospective study
Gómez, S. S., Tabanera, M. T., Bolívar, A. V., Miranda, M. S., Mazo, A., Diaz, M. R., Miravete, P. M., Lag-Asturiano, E., Muñoz, P., & Macias, T. D. (2010) · European Journal of Radiology · doi:10.1016/j.ejrad.2010.08.031 · Cited by 12 · see citing works
Lee, J., Hung, Y., Chou, K.-Y., Lee, C.-Y., Lin, S., Tsai, Y.-H., Lai, W.-Y., Shao, Y., Hsu, C., Hsu, C., et al. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.1008855 · Cited by 11 · see citing works
Artificial intelligence in immunotherapy PET/SPECT imaging
McGale, J., Chen, D. L., Trebeschi, S., Farwell, M. D., Wu, A. M., Cutler, C. S., Schwartz, L. H., & Dercle, L. (2024) · European Radiology · doi:10.1007/s00330-024-10637-3 · Cited by 11 · see citing works
FakET: Simulating cryo-electron tomograms with neural style transfer
Harár, P., Herrmann, L., Grohs, P., & Haselbach, D. (2025) · Structure · doi:10.1016/j.str.2025.01.020 · Cited by 11 · see citing works
From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images
Arslan, M., Haider, A., Khurshid, M., Bakar, S. S. U. A., Jani, R., Masood, F., Tahir, T., Mitchell, K., Panchagnula, S. V., & Mandair, S. (2023) · Cureus · doi:10.7759/cureus.45587 · Cited by 10 · see citing works
Damsees, R., Jaghbir, M., Salam, M., Al‐Omari, A., & Al-Rawashdeh, N. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-069529 · Cited by 8 · see citing works
Unveiling biases of artificial intelligence in healthcare: Navigating the promise and pitfalls
Rashid, D., Hirani, R., Khessib, S., et al. (2024) · Injury · doi:10.1016/j.injury.2024.111358 · Cited by 8 · see citing works
Self-citationZhang, S., Jin, J., Zheng, Q., & Wang, Z. (2025) · Medicine · doi:10.1097/md.0000000000041370 · Cited by 8 · see citing works
Predicting Social Determinants of Health in Patient Navigation: Case Study
Iacobelli, F., Yang, A., Tom, L., Leung, I. S., Crissman, J., Salgado, R., & Simon, M. A. (2022) · JMIR Formative Research · doi:10.2196/42683 · Cited by 7 · see citing works
Issues of Artificial Intelligence Application in Digital Marketing
Velev, D. G., & Златева, П. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230716 · Cited by 6 · see citing works
Solving the Malady of Financial Toxicity Using Augmented Intelligence
Bhattacharyya, G. S., Bothra, S. J., Malhotra, H., et al. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00155 · Cited by 4 · see citing works
Deboever, N., Al-Tashi, Q., Eisenberg, M. A., et al. (2023) · Cancer Research · doi:10.1158/1538-7445.am2023-726 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Froicu, E.-M., Creangă-Murariu, I., Afrăsânie, V.-A., Gafton, B., Alexa-Stratulat, T., Miron, L., Pușcașu, D. M., Poroch, V., Bacoanu, G., Radu, I., et al. (2025) · Current Oncology Reports · doi:10.1007/s11912-025-01698-8 · Cited by 38 · see citing works
He, J., Xue, W., & Li, Y. (2026) · International Journal of Nanomedicine · doi:10.2147/ijn.s560983 · Cited by 16 · see citing works
Girdwood, T., Kheirinejad, S., Kheirkhah, P., White, B., Davis, R., Schwartz, D., & Shaban-Nejad, A. (2026) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2026.100694 · Cited by 3 · see citing works
Hundal, J., Veettil, A. A. V., Chung, C., & Hosseini, M. (2026) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-26-517280 · Cited by 1 · see citing works
- Authors
- Huhulea, E. N., Huang, L., Eng, S., Sumawi, B., Huang, A., Aifuwa, E., Hirani, R., Tiwari, R. K., & Etienne, M.
- Affiliations
- New York Medical College (US)
- The University of Texas at San Antonio Health Science Center (US)
- Corresponding author
- Etienne, Mill
- Venue
- Biomedicines
- Publisher
- MDPI AG
- Published
- 2025-04-13
- Volume
- 13
- Issue
- 4
- Pages
- 951
- ISSN
- 2227-9059 (ISSN-L)
- PMC
- PMC12025054
- OpenAlex
- W4409456828
- Cited by
- 74
- References
- 135
- Citations by year
- 2026: 52 · 2025: 22
- FWCI
- 7.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Biomedicines (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Touro Scholar (Touro College) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence in oncology, cancer diagnosis, computer-aided diagnosis, early cancer detection, precision oncology, anticancer drug discovery, deep learning, nanomedicine, immunotherapy, social determinants of health, algorithmic bias, clinical validation, data quality
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
