Artificial intelligence across the cancer care continuum

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799 · see citing works

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 · see citing works

The personal and clinical utility of polygenic risk scores

Torkamani, A., Wineinger, N. E., & Topol, E. J. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0018-x · Cited by 1,605 · see citing works

Deep learning based tissue analysis predicts outcome in colorectal cancer

Bychkov, D., Linder, N., Turkki, R., Nordling, S., Kovanen, P. E., Verrill, C., Walliander, M., Lundin, M., Haglund, C., & Lundin, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-21758-3 · Cited by 644 · see citing works

From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment.

Swanson, K., Wu, E., Zhang, A., Alizadeh, A. A., & Zou, J. (2023) · Cell · doi:10.1016/j.cell.2023.01.035 · Cited by 597 · see citing works

Systematic review automation technologies

Tsafnat, G., Glasziou, P., Choong, M. K., Dunn, A., Galgani, F., & Coiera, E. (2014) · Systematic Reviews · doi:10.1186/2046-4053-3-74 · Cited by 472 · see citing works

Artificial intelligence in histopathology: enhancing cancer research and clinical oncology

Shmatko, A., Ghaffari Laleh, N., Gerstung, M., & Kather, J. N. (2022) · Nature Cancer · doi:10.1038/s43018-022-00436-4 · Cited by 446 · see citing works

Bias in AI-based models for medical applications: challenges and mitigation strategies

Mittermaier, M., Raza, M. M., & Kvedar, J. C. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00858-z · Cited by 372 · see citing works

An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study

Pantanowitz, L., Quiroga‐Garza, G. M., Bien, L., Heled, R., Laifenfeld, D., Linhart, C., Sandbank, J., Shach, A. A., Shalev, V., Vecsler, M., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30159-x · Cited by 361 · see citing works

A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories

Placido, D., Yuan, B., Hjaltelin, J. X., Zheng, C., Haue, A. D., Chmura, P. J., Yuan, C., Kim, J., Umeton, R., Antell, G., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02332-5 · Cited by 349 · see citing works

Fast and robust online adaptive planning in stereotactic MR-guided adaptive radiation therapy (SMART) for pancreatic cancer

Bohoudi, O., Bruynzeel, A. M. E., Senan, S., Cuijpers, J. P., Slotman, B. J., Lagerwaard, F. J., & Palacios, M. A. (2017) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2017.07.028 · Cited by 318 · see citing works

Leveraging large language models for decision support in personalized oncology

Benary, M., Wang, X. D., Schmidt, M., Soll, D., Hilfenhaus, G., Nassir, M., Sigler, C., Knödler, M., Keller, U., Beule, D., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.43689 · Cited by 306 · see citing works

Using ChatGPT to evaluate cancer myths and misconceptions: artificial intelligence and cancer information

Johnson, S. B., King, A. J., Warner, E. L., Aneja, S., Kann, B. H., & Bylund, C. L. (2023) · JNCI Cancer Spectrum · doi:10.1093/jncics/pkad015 · Cited by 289 · see citing works

Sybil: a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography

Mikhael, P. G., Wohlwend, J., Yala, A., Karstens, L., Xiang, J., Takigami, A. K., Bourgouin, P. P., Chan, P., Mrah, S., Amayri, W., et al. (2023) · Journal of Clinical Oncology · doi:10.1200/jco.22.01345 · Cited by 288 · see citing works

Toward robust mammography-based models for breast cancer risk

Yala, A., Mikhael, P. G., Strand, F., Lin, G., Smith, K., Wan, Y.-L., Lamb, L., Hughes, K., Lehman, C., & Barzilay, R. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.aba4373 · Cited by 287 · see citing works

How AI Responds to Common Lung Cancer Questions: ChatGPT versus Google Bard

Rahsepar, A. A., Tavakoli, N., Kim, G. H. J., Hassani, C., Abtin, F. G., & Bedayat, A. (2023) · Radiology · doi:10.1148/radiol.230922 · Cited by 283 · see citing works

Assessment of Artificial Intelligence Chatbot Responses to Top Searched Queries About Cancer

Pan, A., Musheyev, D., Bockelman, D., Loeb, S., & Kabarriti, A. E. (2023) · JAMA Oncology · doi:10.1001/jamaoncol.2023.2947 · Cited by 278 · see citing works

Use of Artificial Intelligence Chatbots for Cancer Treatment Information

Chen, S., Kann, B. H., Foote, M. B., Aerts, H. J. W. L., Savova, G. K., Mak, R. H., & Bitterman, D. S. (2023) · JAMA Oncology · doi:10.1001/jamaoncol.2023.2954 · Cited by 251 · see citing works

Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives

Tran, T. T. V., Wibowo, A. S., Tayara, H., & Chong, K. T. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00200 · Cited by 241 · see citing works

Convergence of evolving artificial intelligence and machine learning techniques in precision oncology

Fountzilas, E., Pearce, T. E., Baysal, M. A., Chakraborty, A., & Tsimberidou, A. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01471-y · Cited by 219 · see citing works

Large language model (ChatGPT) as a support tool for breast tumor board

Sorin, V., Klang, E., Sklair‐Levy, M., Cohen, I., Zippel, D. B., Lahat, N. B., Konen, E., & Barash, Y. (2023) · npj Breast Cancer · doi:10.1038/s41523-023-00557-8 · Cited by 216 · see citing works

Artificial intelligence-assisted colonoscopy for colorectal cancer screening: a multicenter randomized controlled trial

Xu, H., Tang, R. S. Y., Lam, T. Y. T., Zhao, G., Lau, J. Y. W., Liu, Y., Wu, Q., Rong, L., Xu, W., Li, X., et al. (2023) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2022.07.006 · Cited by 209 · see citing works

Preserving data privacy in machine learning systems

Mestari, S. Z. E., Lenzini, G., & Demirci, H. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103605 · Cited by 180 · see citing works

Screening Performance and Characteristics of Breast Cancer Detected in the Mammography Screening with Artificial Intelligence Trial (MASAI): A Randomised, Controlled, Parallel-Group, Non-Inferiority, Single-Blinded, Screening Accuracy Study

Hernström, V., Josefsson, V., Sartor, H., Schmidt, D., Larsson, A.-M., Hofvind, S., Andersson, I., Rosso, A., Hagberg, O., & Lång, K. (2025) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00267-x · Cited by 174 · see citing works

A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening

Jeon, J., Nim, S., Teyra, J., Datti, A., Wrana, J. L., Sidhu, S. S., Moffat, J., & Kim, P. M. (2014) · Genome Medicine · doi:10.1186/s13073-014-0057-7 · Cited by 169 · see citing works

Personalized prediction model to risk stratify patients with myelodysplastic syndromes

Nazha, A., Komrokji, R., Meggendorfer, M., Jia, X., Radakovich, N., Shreve, J., Hilton, C. B., Nagata, Y., Hamilton, B. K., Mukherjee, S., et al. (2021) · Journal of Clinical Oncology · doi:10.1200/jco.20.02810 · Cited by 169 · see citing works

Evaluating large language models on a highly-specialized topic, radiation oncology physics

Holmes, J., Liu, Z., Zhang, L., Ding, Y., Sio, T. T. W., McGee, L. A., Ashman, J. B., Li, X., Liu, T., Shen, J., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1219326 · Cited by 166 · see citing works

Evaluation of an Ambient Artificial Intelligence Documentation Platform for Clinicians

Stults, C. D., Deng, S., Martinez, M. C., Wilcox, J., Szwerinski, N., Chen, K. H., Driscoll, S., Washburn, J., & Jones, V. G. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.8614 · Cited by 147 · see citing works

Effect of Integrating Machine Learning Mortality Estimates With Behavioral Nudges to Clinicians on Serious Illness Conversations Among Patients With Cancer

Manz, C. R., Parikh, R. B., Small, D. S., Evans, C. N., Chivers, C., Regli, S. H., Hanson, C. W., Bekelman, J. E., Rareshide, C., O'Connor, N. R., et al. (2020) · JAMA Oncology · doi:10.1001/jamaoncol.2020.4759 · Cited by 146 · see citing works

Evaluating ChatGPT as an Adjunct for the Multidisciplinary Tumor Board Decision–Making in Primary Breast Cancer Cases

Lukac, S., Dayan, D., Fink, V., Leinert, E., Hartkopf, A., Veselinovic, K., Janni, W., Rack, B., Pfister, K., Heitmeir, B., et al. (2023) · Archives of Gynecology and Obstetrics · doi:10.1007/s00404-023-07130-5 · Cited by 130 · see citing works

A comprehensive survey of large language models and multimodal large language models in medicine

Xiao, H., Zhou, F., Liu, X., Liu, T., Ли, Ж., Liu, X., & Huang, X. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102888 · Cited by 130 · see citing works

Deep learning modeling using normal mammograms for predicting breast cancer risk

Arefan, D., Mohamed, A. A., Berg, W. A., Zuley, M. L., Sumkin, J. H., & Wu, S. (2019) · Medical Physics · doi:10.1002/mp.13886 · Cited by 129 · see citing works

How Well Do Artificial Intelligence Chatbots Respond to the Top Search Queries About Urological Malignancies?

Musheyev, D., Pan, A., Loeb, S., & Kabarriti, A. E. (2023) · European Urology · doi:10.1016/j.eururo.2023.07.004 · Cited by 126 · see citing works

Is ChatGPT accurate and reliable in answering questions regarding head and neck cancer?

Kuşçu, O., Pamuk, E., Süslü, N., & Hosal, S. A. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1256459 · Cited by 124 · see citing works

Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer

Wang, J., Yu, Y., Tan, Y., Wan, H., Zheng, N., He, Z., Mao, L., Ren, W., Chen, K., Lin, Z., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48705-3 · Cited by 119 · see citing works

Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer

Manz, C. R., Chen, J., Liu, M., Chivers, C., Regli, S. H., Braun, J., Draugelis, M. E., Hanson, C. W., Shulman, L. N., Schuchter, L. M., et al. (2020) · JAMA Oncology · doi:10.1001/jamaoncol.2020.4331 · Cited by 116 · see citing works

Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for ai-assisted medical education and decision making in radiation oncology

Huang, Y., Gomaa, A., Semrau, S., Haderlein, M., Lettmaier, S., Weißmann, T., Grigo, J., Tkhayat, H. B., Frey, B., Gaipl, U. S., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1265024 · Cited by 116 · see citing works

Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity

Shukla, S., Rajkumar, S., Sinha, A., Esha, M., Elango, K., & Sampath, V. (2025) · Scientific Reports · doi:10.1038/s41598-025-95858-2 · Cited by 113 · see citing works

Advances in CANCER RESEARCH

(2023) · Advances in cancer research · doi:10.1016/s0065-230x(23)00056-8 · Cited by 108 · see citing works

AI education for clinicians

Schubert, T., Oosterlinck, T., Stevens, R. D., Maxwell, P. H., & van der Schaar, M. (2025) · eClinicalMedicine · doi:10.1016/j.eclinm.2024.102968 · Cited by 103 · see citing works

Enhancing clinical documentation with ambient artificial intelligence: a quality improvement survey assessing clinician perspectives on work burden, burnout, and job satisfaction

Albrecht, M., Shanks, D., Shah, T., Hudson, T., Thompson, J., Filardi, T., Wright, K., Ator, G. A., & Smith, T. R. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooaf013 · Cited by 103 · see citing works

Can ChatGPT, an Artificial Intelligence Language Model, Provide Accurate and High-quality Patient Information on Prostate Cancer?

Coşkun, B., Ocakoğlu, G., Yetemen, M., & Kaygısız, O. (2023) · Urology · doi:10.1016/j.urology.2023.05.040 · Cited by 97 · see citing works

Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery

Park, S., Park, H.-M., Baek, K., Ahn, H., Lee, I. Y., & Son, G. M. (2020) · World Journal of Gastroenterology · doi:10.3748/wjg.v26.i44.6945 · Cited by 94 · see citing works

Using Information from Historical High-Throughput Screens to Predict Active Compounds

Riniker, S., Wang, Y., Jenkins, J. L., & Landrum, G. A. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500190p · Cited by 93 · see citing works

Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling

Pati, P., Karkampouna, S., Bonollo, F., Compérat, É., Radić, M., Spahn, M., Martinelli, A. L., Wartenberg, M., Julio, M. K., & Rapsomaniki, M. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00889-5 · Cited by 93 · see citing works

Enhancing Triage Efficiency and Accuracy in Emergency Rooms for Patients with Metastatic Prostate Cancer: A Retrospective Analysis of Artificial Intelligence-Assisted Triage Using ChatGPT 4.0

Gebrael, G., Sahu, K. K., Chigarira, B., Tripathi, N., Mathew Thomas, V., Sayegh, N., Maughan, B. L., Agarwal, N., Swami, U., & Li, H. (2023) · Cancers · doi:10.3390/cancers15143717 · Cited by 85 · see citing works

Challenging ChatGPT 3.5 in Senology—An Assessment of Concordance with Breast Cancer Tumor Board Decision Making

Griewing, S., Gremke, N., Wagner, U. A. G., Lingenfelder, M., Kühn, S., & Boekhoff, J. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13101502 · Cited by 78 · see citing works

Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics

Kakileti, S. T., Madhu, H., Manjunath, G., Wee, L., Dekker, A. L. A. J., & Sampangi, S. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101854 · Cited by 75 · see citing works

Artificial intelligence-enabled prediction of chemotherapy-induced cardiotoxicity from baseline electrocardiograms

Yagi, R., Goto, S., Himeno, Y., Katsumata, Y., Hashimoto, M., MacRae, C. A., & Deo, R. C. (2024) · Nature Communications · doi:10.1038/s41467-024-45733-x · Cited by 74 · see citing works

Artificial Intelligence to Automate Network Meta-Analyses: Four Case Studies to Evaluate the Potential Application of Large Language Models

Reason, T., Benbow, E., Langham, J., Gimblett, A., Klijn, S. L., & Malcolm, B. (2024) · PharmacoEconomics - Open · doi:10.1007/s41669-024-00476-9 · Cited by 71 · see citing works

Use of ChatGPT, GPT-4, and Bard to Improve Readability of ChatGPT's Answers to Common Questions About Lung Cancer and Lung Cancer Screening

Haver, H. L., Lin, C. T., Sirajuddin, A., Yi, P. H., & Jeudy, J. J. (2023) · American Journal of Roentgenology · doi:10.2214/ajr.23.29622 · Cited by 68 · see citing works

Development of Prompt Templates for Large Language Model–Driven Screening in Systematic Reviews

Cao, C., Sang, J., Arora, R., Chen, D., Kloosterman, R., Cecere, M., Gorla, J., Saleh, R., Drennan, I., Teja, B., et al. (2025) · Annals of Internal Medicine · doi:10.7326/annals-24-02189 · Cited by 67 · see citing works

Deep neural network improves the estimation of polygenic risk scores for breast cancer

Badré, A., Zhang, L., Muchero, W., Reynolds, J. C., & Pan, C. (2020) · Journal of Human Genetics · doi:10.1038/s10038-020-00832-7 · Cited by 66 · see citing works

Artificial Intelligence–Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach

Yeh, M. C.-H., Wang, Y.-H., Yang, H., Bai, K., Wang, H.-H., & Li, Y. (2021) · Journal of Medical Internet Research · doi:10.2196/26256 · Cited by 66 · see citing works

Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset

Ha, R. S., Chang, P., Karcich, J., Mutasa, S., Sant, E. P. V., Liu, M. Z., & Jambawalikar, S. (2018) · Academic Radiology · doi:10.1016/j.acra.2018.06.020 · Cited by 65 · see citing works

Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence

Eckardt, J., Hahn, W., Röllig, C., Stasik, S., Platzbecker, U., Müller‐Tidow, C., Serve, H. L., Baldus, C. D., Schliemann, C., Schäfer‐Eckart, K., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01076-x · Cited by 65 · see citing works

High-performance automated abstract screening with large language model ensembles

Sanghera, R., Thirunavukarasu, A. J., Khoury, M. E., O’Logbon, J., Chen, Y., Watt, A., Mahmood, M., Butt, H., Nishimura, G., & Soltan, A. A. S. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf050 · Cited by 62 · see citing works

askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model to Inform Patients of Prostate Cancer Treatments Chosen by Similar Men

Auffenberg, G. B., Ghani, K. R., Ramani, S., Usoro, E., Denton, B. T., ROGERS, C. G., Stockton, B. R., Miller, D. C., & Singh, K. (2018) · European Urology · doi:10.1016/j.eururo.2018.09.050 · Cited by 61 · see citing works

Language models for data extraction and risk of bias assessment in complementary medicine

Lai, H., Liu, J., Bai, C., Liu, H., Pan, B., Luo, X., Hou, L., Zhao, W., Xia, D., Tian, J., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01457-w · Cited by 59 · see citing works

Let's chat about cervical cancer: Assessing the accuracy of ChatGPT responses to cervical cancer questions

Hermann, C. E., Patel, J. M., Boyd, L. R., Growdon, W. B., Aviki, E. M., & Stasenko, M. (2023) · Gynecologic Oncology · doi:10.1016/j.ygyno.2023.11.008 · Cited by 58 · see citing works

Collaborative large language models for automated data extraction in living systematic reviews

Khan, M. A., Ayub, U., Naqvi, S. A. A., Khakwani, K. Z. R., Riaz, Z. B., Raina, A., Zhou, S., He, H., Saeidi, A., Hasan, B., et al. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae325 · Cited by 58 · see citing works

Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study

Lee, K.-S., Jang, J.-Y., Yu, Y.-D., Heo, J. S., Han, H.-S., Yoon, Y.-S., Kang, C. M., Hwang, H. K., & Kang, S. (2021) · International Journal of Surgery · doi:10.1016/j.ijsu.2021.106050 · Cited by 55 · see citing works

Quality of information and appropriateness of Open AI outputs for prostate cancer

Lombardo, R., Gallo, G., Stira, J., Turchi, B., Santoro, G., Riolo, S., Romagnoli, M., Cicione, A. M., Tema, G., Pastore, A. L., et al. (2024) · Prostate Cancer and Prostatic Diseases · doi:10.1038/s41391-024-00789-0 · Cited by 53 · see citing works

Exploring Capabilities of Large Language Models such as ChatGPT in Radiation Oncology

Dennstädt, F., Hastings, J., Putora, P. M., Vu, E., Fischer, G. F., Süveg, K., Glatzer, M., Riggenbach, E., Hà, H.-L., & Čihorić, N. (2023) · Advances in Radiation Oncology · doi:10.1016/j.adro.2023.101400 · Cited by 51 · see citing works

Feasibility and acceptability of ChatGPT generated radiology report summaries for cancer patients

Chung, E. M., Zhang, S. C., Nguyen, A. T., Atkins, K. M., Sandler, H. M., & Kamrava, M. R. (2023) · Digital Health · doi:10.1177/20552076231221620 · Cited by 49 · see citing works

Accuracy of ChatGPT in head and neck oncological board decisions: preliminary findings

Lechien, J. R., Chiesa-Estomba, C. M., Baudouin, R., & Hans, S. (2023) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-023-08326-w · Cited by 48 · see citing works

Multi-omics based artificial intelligence for cancer research

Li, L., Sun, M., Wang, J., & Wan, S. (2024) · Advances in cancer research · doi:10.1016/bs.acr.2024.06.005 · Cited by 47 · see citing works

Answering head and neck cancer questions: An assessment of ChatGPT responses

Wei, K., Fritz, C. G., & Rajasekaran, K. (2023) · American Journal of Otolaryngology · doi:10.1016/j.amjoto.2023.104085 · Cited by 46 · see citing works

AI-based histopathology image analysis reveals a distinct subset of endometrial cancers

Darbandsari, A., Farahani, H., Asadi, M., Wiens, M. O., Cochrane, D. R., Mirabadi, A. K., Jamieson, A., Farnell, D., Ahmadvand, P., Douglas, M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49017-2 · Cited by 45 · see citing works

Digital twins: a new paradigm in oncology in the era of big data

Mollica, L., Leli, C., Sottotetti, F., Quaglini, S., Locati, L. D., & Marceglia, S. R. F. (2024) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2024.100056 · Cited by 45 · see citing works

An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical trials

Oikonomou, E. K., Thangaraj, P., Bhatt, D. L., Ross, J. S., Young, L. H., Krumholz, H. M., Suchard, M. A., & Khera, R. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00963-z · Cited by 41 · see citing works

Exploring the landscape of AI-assisted decision-making in head and neck cancer treatment: a comparative analysis of NCCN guidelines and ChatGPT responses

Marchi, F., Bellini, E., Iandelli, A., Sampieri, C., & Peretti, G. (2024) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-024-08525-z · Cited by 41 · see citing works

Ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery

Moglia, A., Morelli, L., D’Ischia, R., Fatucchi, L. M., Pucci, V., Berchiolli, R., Ferrari, M., & Cuschieri, A. (2022) · Surgical Endoscopy · doi:10.1007/s00464-021-08999-6 · Cited by 38 · see citing works

Evaluation of ChatGPT and Google Bard using prompt engineering in cancer screening algorithms

Nguyen, D., Swanson, D., Newbury, A., & Kim, Y. H. (2024) · Academic Radiology · doi:10.1016/j.acra.2023.11.002 · Cited by 38 · see citing works

Evaluation of Oropharyngeal Cancer Information from Revolutionary Artificial Intelligence Chatbot

Davis, R. J., Ayo‐Ajibola, O., Lin, M. E., Swanson, M. S., Chambers, T. N., Kwon, D. I., & Kokot, N. (2023) · The Laryngoscope · doi:10.1002/lary.31191 · Cited by 37 · see citing works

Machine learning for the prediction of toxicities from head and neck cancer treatment: A systematic review with meta-analysis

Araújo, A. L. D., Moraes, M. C., Pérez‐de‐Oliveira, M. E., da Silva, V. M., Saldivia‐Siracusa, C., Pedroso, C. M., Lopes, M. A., Vargas, P. A., Kochanny, S. E., Pearson, A. T., et al. (2023) · Oral Oncology · doi:10.1016/j.oraloncology.2023.106386 · Cited by 36 · see citing works

Urological Cancers and ChatGPT: Assessing the Quality of Information and Possible Risks for Patients

Özgör, F., Çağlar, U., Halis, A., Çakır, H., Aksu, U. C., Ayrancı, A., & Sarılar, Ö. (2024) · Clinical Genitourinary Cancer · doi:10.1016/j.clgc.2023.12.017 · Cited by 36 · see citing works

Augmented-Reality Surgery to Guide Head and Neck Cancer Re-resection: A Feasibility and Accuracy Study

Prasad, K., Miller, A., Sharif, K. F., Colazo, J. M., Ye, W., Necker, F. N., Baik, F. M., Lewis, J. S., Rosenthal, E. L., Wu, J. Y., et al. (2023) · Annals of Surgical Oncology · doi:10.1245/s10434-023-13532-1 · Cited by 35 · see citing works

Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review

Sim, J.-A., Huang, X., Horan, M. R., Baker, J. N., & Huang, I. (2024) · Expert Review of Pharmacoeconomics & Outcomes Research · doi:10.1080/14737167.2024.2322664 · Cited by 35 · see citing works

Exploring the Role of Artificial Intelligence Chatbots in Preoperative Counseling for Head and Neck Cancer Surgery

Lee, J. C., Hamill, C. S., Shnayder, Y., Buczek, E. P., Kakarala, K., & Bur, A. M. (2023) · The Laryngoscope · doi:10.1002/lary.31243 · Cited by 33 · see citing works

Artificial Intelligence and Machine Learning Innovations to Improve Design and Representativeness in Oncology Clinical Trials

Azenkot, T., Rivera, D. R. R., Stewart, M. D., & Patel, S. P. (2025) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-25-473590 · Cited by 31 · see citing works

Application of Artificial Intelligence in Symptom Monitoring in Adult Cancer Survivorship: A Systematic Review

Vakili, S. T., Haywood, D., Kirk, D., Abdou, A. M., Gopalakrishnan, R., Sadeghi, S., Guedes, H., Tan, C. J., Thamm, C., Bernard, R., et al. (2024) · JCO Clinical Cancer Informatics · doi:10.1200/cci.24.00119 · Cited by 30 · see citing works

Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data

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Promise and Perils of Large Language Models for Cancer Survivorship and Supportive Care

Bitterman, D. S., Downing, A., Maués, J., & Lustberg, M. B. (2024) · Journal of Clinical Oncology · doi:10.1200/jco.23.02439 · Cited by 28 · see citing works

Physical Activity Using a Wearable Device as an Alternative to Performance Status in Patients With Advanced Lung Cancer

Ito, K., Suzuki, Y., Sakaguchi, T., Fujiwara, K., Nishii, Y., Yasui, H., Taguchi, O., & Hataji, O. (2024) · JAMA Oncology · doi:10.1001/jamaoncol.2024.0023 · Cited by 26 · see citing works

Auto-delineation of Treatment Target Volume for Radiation Therapy Using Large Language Model-Aided Multimodal Learning

Rajendran, P., Chen, Y., Qiu, L., Niedermayr, T., Liu, W., Buyyounouski, M., Bagshaw, H., Han, B., Yang, Y., Kovalchuk, N., et al. (2024) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2024.07.2149 · Cited by 25 · see citing works

Registered multi-device/staining histology image dataset for domain-agnostic machine learning models

Ochi, M., Komura, D., Onoyama, T., Shinbo, K., Endo, H., Odaka, H., Kakiuchi, M., Katoh, H., Ushiku, T., & Ishikawa, S. (2024) · Scientific Data · doi:10.1038/s41597-024-03122-5 · Cited by 24 · see citing works

Impact of Augmented Intelligence on Utilization of Palliative Care Services in a Real-World Oncology Setting

Gajra, A., Zettler, M. E., Miller, K. A., Frownfelter, J. G., Showalter, J., Valley, A. W., Sharma, S., Sridharan, S., Kish, J. K., & Blau, S. (2021) · JCO Oncology Practice · doi:10.1200/op.21.00179 · Cited by 23 · see citing works

Multi-Cell Type and Multi-Level Graph Aggregation Network for Cancer Grading in Pathology Images

Abbas, S. F., Vuong, T. T. L., Kim, K., Song, B., & Kwak, J. T. (2022) · Medical Image Analysis · doi:10.1016/j.media.2023.102936 · Cited by 23 · see citing works

Transforming Informed Consent Generation Using Large Language Models: Mixed Methods Study

Shi, Q., Luzuriaga, K., Allison, J. J., Oztekin, A., Faro, J. M., Lee, J. L., Hafer, N., McManus, M., & Zai, A. H. (2025) · JMIR Medical Informatics · doi:10.2196/68139 · Cited by 23 · see citing works

Virtual Reality for Preoperative Planning in Complex Surgical Oncology: A Single-Center Experience

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AI-driven diagnostics and personalized treatment planning in oral oncology: Innovations and future directions

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Artificial Intelligence–Guided Segmentation and Path Planning Software for Transthoracic Lung Biopsy

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Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing

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Potential application of artificial intelligence in cancer therapy

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Machine learning models including patient-reported outcome data in oncology: a systematic literature review and analysis of their reporting quality

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Informatics-Enabled Behavioral Medicine in Oncology

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Artificial intelligence to unlock real‐world evidence in clinical oncology: A primer on recent advances

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Machine learning computational model to predict lung cancer using electronic medical records

Levi, M., Lazebnik, T., Kushnir, S., Yosef, N., & Shlomi, D. (2024) · Cancer Epidemiology · doi:10.1016/j.canep.2024.102631 · Cited by 16 · see citing works

Enhancing Prostate Cancer Diagnosis: Artificial intelligence-Driven Virtual Biopsy for Optimal Magnetic Resonance Imaging-Targeted Biopsy Approach and Gleason Grading Strategy

Harder, C., Pryalukhin, A. E., Quaas, A., Eich, M., Tretiakova, M., Klein, S., Seper, A., Heidenreich, A., Netto, G. J., Hulla, W., et al. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100564 · Cited by 15 · see citing works

Development of Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer Patients Using Clinical Risk Factors, Patient-Reported Outcomes, and Serum Cytokine Biomarkers

Lin, N., Aghababazadeh, F. A., Su, J. S., Wu, A. J., Lin, C., Shi, W., Xu, W., Haibe‐Kains, B., Liu, F., & Kwan, J. Y. Y. (2025) · Clinical Breast Cancer · doi:10.1016/j.clbc.2025.03.002 · Cited by 15 · see citing works

Augmented Intelligence to Predict 30-day Mortality in Patients with Cancer

Gajra, A., Zettler, M. E., Miller, K. A., Blau, S. H., Venkateshwaran, S. S., Sridharan, S., Showalter, J., Valley, A. W., & Frownfelter, J. (2021) · Future Oncology · doi:10.2217/fon-2021-0302 · Cited by 14 · see citing works

Therapist feedback and implications on adoption of an artificial intelligence-based co-facilitator for online cancer support groups: mixed methods single-arm usability study

Leung, Y. W., Ng, S., Duan, L., Lam, C., Chan, K., Gancarz, M., Rennie, H., Trachtenberg, L., Chan, K. P., Adikari, A., et al. (2023) · JMIR Cancer · doi:10.2196/40113 · Cited by 12 · see citing works

Supporting Structured Data Capture for Patients With Cancer: An Initiative of the University of Wisconsin Carbone Cancer Center Survivorship Program to Improve Capture of Malignant Diagnosis and Cancer Staging Data

Emamekhoo, H., Carroll, C. B., Stietz, C., Pier, J., Lavitschke, M. D., Mulkerin, D. L., Sesto, M. E., & Tevaarwerk, A. J. (2022) · JCO Clinical Cancer Informatics · doi:10.1200/cci.22.00020 · Cited by 9 · see citing works

Artificial Intelligence–Based Assessment of Preoperative Body Composition is Associated With Early Complications After Radical Cystectomy

Sharma, V., Fadel, A. E., Tollefson, M. K., Psutka, S. P., Blezek, D. J., Frank, I., Thapa, P. B., Tarrell, R. F., Viers, L. D., Potretzke, A. M., et al. (2024) · The Journal of Urology · doi:10.1097/ju.0000000000004292 · Cited by 9 · see citing works

Zero-Shot Large Language Models for Long Clinical Text Summarization with Temporal ReasoningPreprint

Kruse, M., Hu, S., Derby, N., Wu, Y., Stonbraker, S., Yao, B., Wang, D., Goldberg, E., & Gao, Y. (2025) · medRxiv · doi:10.1101/2025.07.21.25331947 · Cited by 9 · see citing works

Enhancing Surgical Vision: Augmented Reality in Otolaryngology—Head and Neck Surgery

Aweeda, M., Adegboye, F. O., Yang, S. F., & Topf, M. C. (2024) · Journal of Medical Extended Reality · doi:10.1089/jmxr.2024.0010 · Cited by 8 · see citing works

Limiting bias in AI models for improved and equitable cancer care

Ghassemi, M., & Gusev, A. (2024) · Nature reviews. Cancer · doi:10.1038/s41568-024-00739-x · Cited by 8 · see citing works

Beyond the hype: Navigating bias in AI-driven cancer detection

Singh, Y., Patel, H., Vera-Garcia, D. V., Hathaway, Q. A., Sarkar, D., & Quaia, E. (2024) · Oncotarget · doi:10.18632/oncotarget.28665 · Cited by 8 · see citing works

Summarizing clinical evidence utilizing large language models for cancer treatments: a blinded comparative analysis

Rubinstein, S. M., Mohsin, A., Banerjee, R., Ma, W., Mishra, S., Kwok, M., Yang, P. C., Warner, J. L., & Cowan, A. J. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1569554 · Cited by 8 · see citing works

SLYMEC II study: Overall survival analysis of the impact of LVSI in apparent early stage endometrioid endometrial cancer

Mauro, J., Mueller, M. D., Perrone, E., Bruno, V., Restaino, S., Ponti, E. D., Imboden, S., García-Pineda, V., Taşkın, S., Grassi, T., et al. (2025) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2025.109750 · Cited by 6 · see citing works

Single-Cell Spatial Analysis of Histopathology Images for Survival Prediction via Graph Attention Network

Li, Z., Jiang, Y., Liu, L., Xia, Y., & Li, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47076-9_12 · Cited by 4 · see citing works

Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System

Goryachev, S. D., Yildirim, C., DuMontier, C., La, J., Dharne, M., Gaziano, J. M., Brophy, M. T., Munshi, N. C., Driver, J. A., Do, N., et al. (2024) · JCO Clinical Cancer Informatics · doi:10.1200/cci.23.00197 · Cited by 4 · see citing works

Using Large Language Models to Create Patient Centered Consent Forms

Beattie, J., Neufeld, S., Yang, D. X., Chukwuma, C., Desai, N. B., Dohopolski, M. J., & Jiang, S. B. (2024) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2024.07.1346 · Cited by 4 · see citing works

Revolutionizing clinical experimental protocol design through the ChatGPT technology

Wei, Y., & Guo, X. (2023) · The American Journal of the Medical Sciences · doi:10.1016/j.amjms.2023.09.004 · Cited by 3 · see citing works

Artificial Intelligence in cancer survivorship care plans: what lies beyond diagnostics?

Jindal, S., Jindal, M., & Bhati, P. (2022) · International Conference on Artificial Intelligence and Speech Technology (AIST) · doi:10.1109/aist55798.2022.10065173 · Cited by 2 · see citing works

Comparative Analysis of Multimodal Large Language Models GPT-4o and o1 vs Clinicians in Clinical Case Challenge QuestionsPreprint

Jung, J., Kim, H., Bae, S., & Park, J. Y. (2025) · medRxiv · doi:10.1101/2025.06.22.25330068 · Cited by 1 · see citing works

Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and PredictionPreprint

Kruse, M., Hu, S., Derby, N., et al. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2501.18724 · Cited by 1 · see citing works

Multi-Modal Grounded Planning and Efficient Replanning For Learning Embodied Agents with A Few ExamplesPreprint

Kim, T.-W., Kim, B., & Choi, J. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2412.17288

Artificial Intelligence (AI) in Pharmaceutical Formulation and Dosage Calculations

Joshi, S., & Sheth, S. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17111440 · Cited by 37 · see citing works

Cancer workforce—a global crisis: a Lancet Oncology Commission

Hricak, H., Ward, Z. J., Moraes, F. Y., Scott, A. M., Dako, F., Ngwa, W. F., Villarreal-Garza, C. M., Abdel-Wahab, M., Avery, S. M., Addai, B. W., et al. (2026) · The Lancet Oncology · doi:10.1016/s1470-2045(26)00065-3 · Cited by 22 · see citing works

Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention

Singh, M., Betgeri, S. N., & Kakar, S. S. (2026) · Journal of Ovarian Research · doi:10.1186/s13048-026-01979-1 · Cited by 8 · see citing works

AI and human expertise in cancer care — striving for synergy

Patel, M., Dee, E. C., & Lee, N. Y. (2025) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-025-01108-9 · Cited by 5 · see citing works

Implementation of AI in oncology: a systematic review of educational and clinical integration in global contexts

Tene, T., Vique López, D. F., Valverde Aguirre, P. E., & Serrano Avalos, K. V. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1764390 · Cited by 5 · see citing works

Trapped in an information provision gap? A scoping review of (unmet) information needs, preferences, and behaviors among long-term cancer survivors

Hopfe, E., Baumann, E., Grünwald, V., Shakib, F. S., & Dirksen, U. (2025) · Frontiers in Communication · doi:10.3389/fcomm.2025.1624452 · Cited by 3 · see citing works

Recent Advances in Analytical Techniques for Cancer Diagnostics and Therapeutics: Combining State-of-the-Art Technologies for Precision Oncology

Gharti, L., Kaur, S. D., Vihal, S., Ramakrishna, K., Thakur, D., Kapoor, D. N., Sharma, L. K., & Kaurav, H. (2026) · Critical Reviews in Analytical Chemistry · doi:10.1080/10408347.2026.2656439 · Cited by 3 · see citing works

Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration

Polit, F., Bahmad, H. F., Kassab, M. B., Elajami, M. K., Recine, M. A., Alghamdi, S., & Poppiti, R. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1833926 · Cited by 3 · see citing works

Applications of Artificial Intelligence in Cancer Diagnosis and Treatment

Xie, Y., Wang, Z., Zeng, Z., Xin, T., Yuan, S., Qin, F., & Chen, Z. (2026) · Cancer Medicine · doi:10.1002/cam4.72143 · Cited by 2 · see citing works

Artificial Intelligence in Oncology: Practical Applications Across Clinical Care, Scholarship, and Translation.

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

From Semantic Modeling to Precision Radiotherapy: An AI Framework Linking Radiobiology, Oncology, and Public Health Integration

de Souza, F. G., Aliaga, J., Duarte, P. C., Crispilho, S. F., Delfino, C. D. S. C., Silva, D. S. B. E., & Silva, F. (2025) · Biomedicines · doi:10.3390/biomedicines13122862 · Cited by 1 · see citing works

Merging artificial intelligence into cancer nursing care: Current applications, challenges, and opportunities

Wang, M., Abu‐Odah, H., & Zeng, Y. (2025) · Asia-Pacific Journal of Oncology Nursing · doi:10.1016/j.apjon.2025.100849 · Cited by 1 · see citing works

Application of artificial intelligence in cancer rehabilitation: A scoping review

Li, N., Wang, Y., Zhang, J., Huang, J., Zou, J., & Zhang, Z. (2026) · Intelligent Oncology · doi:10.1016/j.intonc.2026.100068 · Cited by 1 · see citing works

Beyond static biomarkers: systems biology and AI for decoding cancer dynamics

Dutta, S., & Goli, A. (2026) · Frontiers in Systems Biology · doi:10.3389/fsysb.2026.1855016 · Cited by 3 · see citing works

Perspectives of Gynecologic Oncologists on AI for Women’s Cancers in Low-Resource Settings: A Survey in Iran

Taherikia, S., Galougah, A. F., Lashkenari, M. A., & Lotfi, S. (2025) · InfoScience Trends · doi:10.61882/ist.202502.08.01 · Cited by 1 · see citing works

LLM-Driven Chatbots in Osteosarcoma Care: Stratified Efficacy Testing and Human-Machine Collaboration Framework (Preprint)Preprint

Yang, J., & Li, J. (2025) · doi:10.2196/preprints.88715

AI-based prognostic risk stratification and hope in osteosarcoma and bone metastatic cancer

Wang, J., Liang, L., & Le, X. (2026) · Psycho-Oncologie · doi:10.18282/po5713

Machine Learning and Emerging Technologies in Cancer Care

Rafique, M. A., & Ilyas, Q. M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4312-9.ch009

Artificial intelligence in cancer care: Opportunities and challenges from a nursing perspective

Xu, T. (2026) · Asia-Pacific Journal of Oncology Nursing · doi:10.1016/j.apjon.2026.100935

The Journey of the Journal of Onco-Anaesthesiology and Perioperative Medicine: Milestones, DOAJ Indexing, and Future Directions

Garg, R. (2026) · Journal of onco-anaesthesiology and perioperative medicine. · doi:10.4103/joapm.joapm_1_26

Digital Health Informatics: Bridging Clinical Gaps through AI, Wearable Sensors, and Telemedicine Technologies

Thabassum, Y. H. W., Saliha, Z. H. A., Veronica, V. V., Dharshini, F. M., Sharon, S. J. R., Ramya, B., & Vijiyalakshmi, P. (2026) · Journal of Medico Informatics · doi:10.64659/jomi/211941

Does artificial intelligence provide value for money in oncology care? A personal perspective

Annemans, L. (2026) · Academia oncology. · doi:10.20935/acadonco8235

Decoding Lung Cancer with Artificial Intelligence: A Path to Personalized Diagnostics and Therapy

Masuka, T. C., Dhanashekaran, C. J. M., & Prabhu, V. (2026) · Journal of Environmental Pathology Toxicology and Oncology · doi:10.1615/jenvironpatholtoxicoloncol.2026061968

Evaluation Frameworks for Predictive and Generative Oncology AI: Current Standards, Cancer-Specific Gaps, and a Path Toward Clinical Use

Yost, C. D., Callas, B., Halligan, P., Palumbo, P., Rawal, S. C., Leyfman, Y., & Nguyen, R. H.-T. (2026) · Cancers · doi:10.3390/cancers18121981 · Cited by 2 · see citing works

Physicians’ attitudes and perceptions toward the integration of artificial intelligence into pediatric hematology–oncology in Saudi Arabia

Alblewi, S. M. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1820831

From Symptom Control to Precision Supportive Oncology: Integrating Artificial Intelligence in Supportive Oncology for Gastrointestinal Cancers

Krishnan, A., Davis, M. P., & Kadakia, K. C. (2026) · Current Oncology Reports · doi:10.1007/s11912-026-01810-6

Precision Oncology in Northern Germany: Integrating Multi-Omics and Artificial Intelligence to Support Evidence-Based Molecular Tumor Boards

Vorwerk, J., Leypoldt, L. B., Schwandt, J., Kohler, J., Nimmagadda, S. C., Ahmed, H., Patnana, P. K., Hönemann, H., Helbing, T., Feierabend, S., et al. (2026) · Oncology Research and Treatment · doi:10.1159/000553462

Utility and limitations of generative artificial intelligence in therapeutic decision-making for advanced lung cancer: a simulation study and questionnaire survey

Nakamura, S., Karayama, M., Inoue, Y., Yasui, H., Hozumi, H., Suzuki, Y., Furuhashi, K., Enomoto, N., Inui, N., Suda, T., et al. (2026) · International Journal of Lung Cancer · doi:10.1016/j.ijlcan.2026.100058

Surviving Cancer, Lacking Support: The Hidden Burden of Modern Radiation Oncology in the Treatment of Oligometastatic Disease

Chasty, B., Rembielak, A., Berman, R., & Oldenburger, E. (2026) · Cancers · doi:10.3390/cancers18162584

Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions

Yadav, K., & Gupta, T. (2026) · Journal of the Egyptian National Cancer Institute · doi:10.1186/s43046-026-00399-y

Prospective multi-centre evaluation of guideline−based artificial intelligence to streamline multidisciplinary tumour board

Ivashchuk, O., & Hovornyan, S. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1785951

The Five-Decade Journey of Small Cell Lung Cancer

Chen, R., Xue, C., Zhong, Y., Huang, Q., Fan, Q., Li, Y., Wu, J., Dong, S., Li, X., Xiao, T., et al. (2026) · 癌症：英文版 · doi:10.34133/cancomm.0052

Accuracy of Retrieval-Augmented Large Language Model–Generated Preconsult Summaries in Breast Surgical Oncology

Moo, T.-A. S., James, R., Bayard, S., Giannakou, A., Allen, L. R., Montagna, G., Pan, I., Sevilimedu, V., Eskreis‐Winkler, S., & Morrow, M. (2026) · JCO Clinical Cancer Informatics · doi:10.1200/cci-26-00160

Adaptive preconditioning with low-dose radiation: Translational challenges from radiobiology to radiotherapy

Tong, J., & Zhou, G. (2026) · Radiation Medicine and Protection · doi:10.1016/j.radmp.2026.09.002

AI adoption among French oncology pharmacists: A national survey

Wakil, A., Chevrier, R., Tournamille, J., & Pourroy, B. (2026) · Journal of Oncology Pharmacy Practice · doi:10.1177/10781552261485826 · Cited by 1 · see citing works

Clinical challenges in the adaptation of AI predictive models in radiation oncology for gynaecological cancer: a systematic review by the radiation oncology-AI MITO group

Barcellini, A., Cilla, S., Aquilano, M., Bonome, P., Charalampopoulou, A., Giannone, G., Lombardo, V., Mastroleo, F., Polano, M., Raimondo, I., et al. (2026) · La radiologia medica · doi:10.1007/s11547-026-02272-3

Leveraging traditional and advanced machine learning for comprehensive oncology data integration and outcome prediction

Hey, G. E., Sesto, M., Still, M., Patel, G., Crlenjak, N., Dagra, A., Wu, Y., Guo, Y., & Ghiaseddin, A. P. (2026) · Journal of Neuro-Oncology · doi:10.1007/s11060-026-05774-2

Mapping System Requirements to System Architecture in AI-Enabled Cancer Screening Platforms

Panagiotopoulou, E., Santa, K., Politi, E., Αναστασίου, Α., Dimitrakopoulos, G., Dedoussis, G., Politi, O., Anagnostopoulos, T., Antonopoulos, G., Matzakos, P., et al. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-34571-4_23

Artificial intelligence for symptom assessment and patient-reported outcomes in neuro-oncology

Yogendran, L. V., Avery, C., Crooms, R. C., Ozair, A., Siegel, C. L., Guterman, I., Rhee, J. Y., Binder, J., Minor, M., Alder, L., et al. (2026) · Journal of Neuro-Oncology · doi:10.1007/s11060-026-05832-9

Artificial Intelligence Across the Oncology Continuum: From Prevention to Survivorship

Verma, D. N., Desai, D. R., Kulkarni, M. P., Sharma, V., & Patil, M. A. (2025) · International Journal of Emerging Research in Applied Medical Sciences · doi:10.65477/x4rce342

Artificial intelligence and transforming cancer care

Reddy, A. M., Kaur, G., Ribaya, V. S. D., Ribaya, E. L. A., Reddy, M. C., Shah, T., Shinde, D. A., & Suri, G. S. (2026) · Discover Oncology · doi:10.1007/s12672-026-05242-3 · Cited by 1 · see citing works

Artificial intelligence in oncology

Reddy, D. R. B., -, K. M., & Kiran, A. R. (2026) · International Journal of Pharmacy and Pharmaceutical Science · doi:10.33545/26647222.2026.v8.i8a.494

Artificial Intelligence in Oncology: Advancing Precision, Prediction, and Personalized Cancer Care

Sakshi Mane*, S. M. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.17365749

AI-Driven Innovations in Cancer Care: A Comprehensive Overview

Li, J., Yeang, H. A., & Perera, R. (2026) · Springer eBooks · doi:10.1007/978-3-032-11023-7_6