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Using Natural Language Processing to Extract Abnormal Results From Cancer Screening Reports

Moore, C., Farrag, A., & Ashkin, E. (2014) · Journal of Patient Safety · doi:10.1097/pts.0000000000000127 · Cited by 27

Improvement in prediction of prostate cancer prognosis with somatic mutational signatures

Zhang, S., Xu, Y., Hui, X., Yang, F., Hu, Y., Shao, J., Liang, H., & Wang, Y. (2017) · Journal of Cancer · doi:10.7150/jca.21261 · Cited by 26

A web-based automated machine learning platform to analyze liquid biopsy data

Shen, H., Liu, T., Cui, J., Borole, P., Benjamin, A. S., Kording, K. P., & Issadore, D. (2020) · Lab on a Chip · doi:10.1039/d0lc00096e · Cited by 26

Applications of digital Medicine in oncology: Prospects and challenges

Ge, H., Li, L., Zhang, D., & Ma, F. (2022) · Cancer Innovation · doi:10.1002/cai2.37 · Cited by 25

Profiling Lung Cancer Patients Using Electronic Health Records

Menasalvas, E., Tuñas, J. M., Bermejo, G., Gonzalo‐Martín, C., Rodríguez‐González, A., Zanin, M., de Pedro, C. G., Méndez, M., Zaretskaia, O., Jimenez, J. R., et al. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-0975-9 · Cited by 25

Using machine learning for mortality prediction and risk stratification in atezolizumab‐treated cancer patients: Integrative analysis of eight clinical trials

Wu, Y., Zhu, W., Wang, J., Liu, L., Zhang, W., Wang, Y., Shi, J., Xia, J., Gu, Y., Qian, Q., et al. (2022) · Cancer Medicine · doi:10.1002/cam4.5060 · Cited by 24

Exploring the role of ChatGPT in oncology: providing information and support for cancer patients

Cè, M., Chiarpenello, V., Bubba, A., Felisaz, P. F., Oliva, G., Irmici, G., & Cellina, M. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4020049 · Cited by 24

Dissecting cancer heterogeneity based on dimension reduction of transcriptomic profiles using extreme learning machines

Wang, K., Duan, X., Gao, F., Wang, W., Liu, L., & Wang, X. (2018) · PLoS ONE · doi:10.1371/journal.pone.0203824 · Cited by 24

A Deep Learning Radiomics Analysis for Survival Prediction in Esophageal Cancer

Wang, J., Zeng, J., Li, H., & Yu, X. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4034404 · Cited by 23

High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders

Mkrtchyan, G. V., Veviorskiy, A., Izumchenko, E., Shneyderman, A., Pun, F. W., Ozerov, I. V., Aliper, A., Zhavoronkov, A. A., & Scheibye‐Knudsen, M. (2022) · Cell Death and Disease · doi:10.1038/s41419-022-05437-w · Cited by 23

Strengthening and promoting digital health practice: results from a Global Digital Health Partnership’s survey

Cascini, F., Gentili, A., Causio, F. A., Altamura, G., Melnyk, A., Beccia, F., Pappalardo, C., Lontano, A., & Ricciardi, W. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1147210 · Cited by 21

A Novel Pipeline for Drug Repurposing for Bladder Cancer Based on Patients’ Omics Signatures

Mokou, M., Lygirou, V., Angelioudaki, I., Paschalidis, N., Stroggilos, R., Frantzi, M., Latosińska, A., Bamias, A., Hoffmann, M. J., Mischak, H., et al. (2020) · Cancers · doi:10.3390/cancers12123519 · Cited by 20

Adequacy and Effectiveness of Watson For Oncology in the Treatment of Thyroid Carcinoma

Yun, H. J., Kim, H. J., Kim, S. Y., Lee, Y. S., Lim, C. Y., Chang, H., & Park, C. S. (2021) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.585364 · Cited by 19

Use of Multiple Correspondence Analysis and K-means to Explore Associations Between Risk Factors and Likelihood of Colorectal Cancer: Cross-sectional Study

Florensa, D., Mateo, J., Solsona, F., Aige, T. P., Mesas, M., Piñol, R., & Godoy, P. (2022) · Journal of Medical Internet Research · doi:10.2196/29056 · Cited by 17

Noncoding RNAs and Deep Learning Neural Network Discriminate Multi-Cancer Types

Wang, A., Hai, R., Rider, P. J. F., & He, Q. (2022) · Cancers · doi:10.3390/cancers14020352 · Cited by 16

Breast Cancer Risk Prediction Using Electronic Health Records

Wu, Y., Burnside, E. S., Cox, J., Fan, J., Yuan, M., Yin, J., Peissig, P. L., Cobian, A. G., Page, D., & Craven, M. W. (2017) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2017.62 · Cited by 16

Accurate prediction of pan-cancer types using machine learning with minimal number of DNA methylation sites

Wei, N., Wu, T., Wu, C., Wang, S., Tao, Z., Wang, G., Zhao, X., Diao, K., Wang, J., Chen, J., et al. (2023) · Journal of Molecular Cell Biology · doi:10.1093/jmcb/mjad023 · Cited by 15

Cancer prediction with gene expression profiling and differential evolution

Lakshmi, T. R. V., & Reddy, C. V. K. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02396-9 · Cited by 15

Identifying Biomarkers Using Support Vector Machine to Understand the Racial Disparity in Triple-Negative Breast Cancer

Sahoo, B., Pinnix, Z., Sims, S., & Zelikovsky, A. (2023) · Journal of Computational Biology · doi:10.1089/cmb.2022.0422 · Cited by 14

Electronic Health Record–Based Absolute Risk Prediction Model for Esophageal Cancer in the Chinese Population: Model Development and External Validation

Han, Y., Zhu, X., Hu, Y., Yu, C., Guo, Y., Hang, D., Pang, Y., Pei, P., Ma, H., Sun, D., et al. (2023) · JMIR Public Health and Surveillance · doi:10.2196/43725 · Cited by 13

Interoperability as a Catalyst for Digital Health and Therapeutics: A Scoping Review of Emerging Technologies and Standards (2015–2025)

Adegoke, K., Adegoke, A., Dawodu, D., Adekoya, A., Bayowa, A., Kayode, T., & Singh, M. (2025) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph22101535 · Cited by 45

Revolutionizing Oncology Through AI: Addressing Cancer Disparities by Improving Screening, Treatment, and Survival Outcomes via Integration of Social Determinants of Health

Srivastav, A. K., Singh, A., Singh, S., Rivers, B. M., Lillard, J. W., & Singh, R. (2025) · Cancers · doi:10.3390/cancers17172866 · Cited by 37

Bridging technology and medicine: artificial intelligence in targeted anticancer drug delivery

Khorsandi, D., Farahani, A., Zarepour, A., Khosravi, A., Iravani, S., & Zarrabi, A. (2025) · RSC Advances · doi:10.1039/d5ra03747f · Cited by 18

Overcoming the Black Box Challenge: Building Trust in Artificial Intelligence Algorithms in Oncology

Alum, E. U., Egwu, C. K., Manjula, V. S., Ekpang, P. O., Ekpang, J. E., Echegu, D. A., Alum, B. N., & Uti, D. E. (2026) · Technology in Cancer Research & Treatment · doi:10.1177/15330338261434649 · Cited by 12

Artificial intelligence in Nigerian oncology practice: A qualitative exploration of oncologists' perspectives

Olawade, D. B., Ojo, I. O., Oisakede, E. O., Joel-Medewase, V. I., & Wada, O. Z. (2025) · Journal of Cancer Policy · doi:10.1016/j.jcpo.2025.100626 · Cited by 10

Artificial Intelligence in Oncology- Technologies being used and scope in India

Sharma, A., Sharma, S., Trehan, M., & Sharma, S. (2021) · doi:10.52977/ujmfs.2021.1.2.8 · Cited by 1

ROLE OF ARTIFICIAL INTELLIGENCE IN CANCER MANAGEMENT: A REVIEW

Singh*, D., Singh, S., & Singh, V. K. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.17010477

The Adoption of artificial Intelligence with multifaceted Challenges and promising Opportunities in Asian Countries: A case Study of India

Chhaperia, D., & Khanna, K. (2024) · Clinical Social Work and Health Intervention · doi:10.22359/cswhi_15_2_07 · Cited by 1

Transforming cancer care in emerging economies: India’s AI innovation

Sharma, A. K., Behera, A., Qazi, S., Gupta, S., Dhamija, E., Pandey, R., Haresh, K. P., Panat, L., Yenishetti, S., Kataria, B., et al. (2026) · The BRICS Health Journal · doi:10.47093/3034-4700.2026.3.2.103

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