Artificial intelligence in cancer therapy

Scalable and accurate deep learning with electronic health records

Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · see citing works

Scalable and accurate deep learning with electronic health records.

Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · eScholarship (California Digital Library) · Cited by 2,242 · see citing works

Estimation of clinical trial success rates and related parameters

Wong, C. H., Siah, K. W., & Lo, A. W. (2019) · Biostatistics · doi:10.1093/biostatistics/kxx069 · Cited by 1,585 · see citing works

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573 · see citing works

Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials

Lin, A., Giuliano, C. J., Palladino, A. C., John, K. M., Abramowicz, C., Yuan, M. L., Sausville, E. L., Lukow, D. A., Liu, L., Chait, A. R., et al. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aaw8412 · Cited by 747 · see citing works

Artificial intelligence for clinical trial design

Harrer, S., Shah, P., Antony, B., & Hu, J. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.05.005 · Cited by 672 · see citing works

Combination Cancer Therapy Can Confer Benefit via Patient-to-Patient Variability without Drug Additivity or Synergy

Palmer, A. C., & Sorger, P. K. (2017) · Cell · doi:10.1016/j.cell.2017.11.009 · Cited by 642 · see citing works

Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer

Zhang, J., Cunningham, J. J., Brown, J. S., & Gatenby, R. A. (2017) · Nature Communications · doi:10.1038/s41467-017-01968-5 · Cited by 557 · see citing works

Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

Menden, M. P., Iorio, F., Garnett, M. J., McDermott, U., Benes, C. H., Ballester, P. J., & Sáez-Rodríguez, J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061318 · Cited by 543 · see citing works

Optimization of Dosing for EGFR-Mutant Non–Small Cell Lung Cancer with Evolutionary Cancer Modeling

Chmielecki, J., Foo, J., Oxnard, G. R., Hutchinson, K. E., Ohashi, K., Somwar, R., Wang, L., Amato, K. R., Arcila, M. E., Sos, M. L., et al. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002356 · Cited by 519 · see citing works

Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer

Enríquez‐Navas, P. M., Kam, Y., Das, T., Hassan, S., Silva, A., Foroutan, P., Ruiz, E., Martinez, G. V., Minton, S. E., Gillies, R. J., et al. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aad7842 · Cited by 323 · see citing works

An image-based deep learning framework for individualising radiotherapy dose: a retrospective analysis of outcome prediction

Lou, B., Doken, S., Zhuang, T., Wingerter, D., Gidwani, M., Mistry, N. N., Ladic, L., Kamen, A., & Abazeed, M. E. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30058-5 · Cited by 207 · see citing works

Individualizing liver transplant immunosuppression using a phenotypic personalized medicine platform

Zarrinpar, A., Lee, D.-K., Silva, A., Datta, N. N., Kee, T. W., Eriksen, C. M., Weigle, K., Agopian, V. G., Kaldas, F. M., Farmer, D. G., et al. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aac5954 · Cited by 152 · see citing works

Modulating BET Bromodomain Inhibitor ZEN‐3694 and Enzalutamide Combination Dosing in a Metastatic Prostate Cancer Patient Using CURATE.AI, an Artificial Intelligence Platform

Pantuck, A. J., Lee, D., Kee, T. W., Wang, P., Lakhotia, S., Silverman, M. H., Mathis, C., Drakaki, A., Belldegrun, A. S., Ho, C., et al. (2018) · Advanced Therapeutics · doi:10.1002/adtp.201800104 · Cited by 127 · see citing works

Optimizing drug combinations against multiple myeloma using a quadratic phenotypic optimization platform (QPOP)

Rashid, M. B. M. A., Toh, T. B., Hooi, L., Silva, A., Zhang, Y., Tan, P. F., Teh, A. L., Karnani, N., Jha, S., Ho, C., et al. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aan0941 · Cited by 125 · see citing works

Postsurgical adjuvant or metastatic renal cell carcinoma therapy models reveal potent antitumor activity of metronomic oral topotecan with pazopanib

Jedeszko, C., Pàez‐Ribes, M., Desidero, T. D., Man, S., Lee, C. R., Xu, P., Bjarnason, G. A., Bocci, G., & Kerbel, R. S. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.3010722 · Cited by 55 · see citing works

Anti-angiogenic agents — overcoming tumour endothelial cell anergy and improving immunotherapy outcomes

Huinen, Z. R., Huijbers, E. J. M., B, J. R. V. B., Nowak-Sliwinska, P. M., & Griffioen, A. W. (2021) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-021-00496-y · Cited by 398 · see citing works

The role of VEGF in cancer-induced angiogenesis and research progress of drugs targeting VEGF

Ghalehbandi, S., Yüzügülen, J., Pranjol, M. Z. I., & Pourgholami, M. H. (2023) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2023.175586 · Cited by 383 · see citing works

Precision medicine in the era of artificial intelligence: implications in chronic disease management

Subramanian, M., Wojtusciszyn, A., Favre, L., Boughorbel, S., Shan, J., Letaief, K. B., Pitteloud, N., & Chouchane, L. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02658-5 · Cited by 362 · see citing works

Longevity leap: mind the healthspan gap

Garmany, A., Yamada, S., & Terzic, A. (2021) · npj Regenerative Medicine · doi:10.1038/s41536-021-00169-5 · Cited by 252 · see citing works

Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials

Esteva, A., Feng, J., van der Wal, D., Huang, S.-C., Simko, J. P., DeVries, S., Chen, E., Schaeffer, E. M., Morgan, T. M., Sun, Y., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00613-w · Cited by 217 · see citing works

Artificial Intelligence Applications in Health Care Practice: Scoping Review

Sharma, M., Savage, C., Nair, M., Larsson, I., Svedberg, P., & Nygren, J. M. (2022) · Journal of Medical Internet Research · doi:10.2196/40238 · Cited by 198 · see citing works

Applications of machine learning in spectroscopy

Ramírez, C., Greenop, M., Ashton, L., & Rehman, I. U. (2020) · Applied Spectroscopy Reviews · doi:10.1080/05704928.2020.1859525 · Cited by 185 · see citing works

The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer

Wölfl, B., Rietmole, H. T., Salvioli, M., Kaznatcheev, A., Thuijsman, F., Brown, J. S., Burgering, B. M. T., & Staňková, K. (2021) · Dynamic Games and Applications · doi:10.1007/s13235-021-00397-w · Cited by 119 · see citing works

Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers

Ng, F. Y. C., Thirunavukarasu, A. J., Cheng, H., Tan, T. F., Gutierrez, L., Lan, Y., Ong, J. C. L., Chong, Y. S., Ngiam, K. Y., Ho, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101230 · Cited by 113 · see citing works

Simulation-based approaches for drug delivery systems: Navigating advancements, opportunities, and challenges

Salahshoori, I., Golriz, M., Nobre, M. A. L., Mahdavi, S., Malekshah, R. E., Javdani-Mallak, A., Jorabchi, M. N., Khonakdar, H. A., Wang, Q., Mohammadi, A. H., et al. (2023) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2023.123888 · Cited by 106 · see citing works

Artificial intelligence in medicine and the disclosure of risks

Kiener, M. (2021) · AI & SOCIETY · doi:10.1007/s00146-020-01085-w · Cited by 97 · see citing works

Patient perspectives on informed consent for medical AI: A web-based experiment

Park, H. J. (2024) · DIGITAL HEALTH · doi:10.1177/20552076241247938 · Cited by 95 · see citing works

The Future of Clinical Trial Design in Oncology

Spreafico, A., Hansen, A. R., Razak, A. R. A., Bedard, P. L., & Siu, L. L. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1301 · Cited by 97 · see citing works

Computer Vision-Based Artificial Intelligence-Mediated Encoding-Decoding for Multiplexed Microfluidic Digital Immunoassay

Zhao, W., Zhou, Y., Feng, Y. X., Niu, X., Zhao, Y., Zhao, J., Dong, Y., Tan, M., Xianyu, Y., & Chen, Y. (2023) · ACS Nano · doi:10.1021/acsnano.3c02941 · Cited by 84 · see citing works

Experimental Models of Hepatocellular Carcinoma—A Preclinical Perspective

Blidișel, A., Marcovici, I., Coricovac, D. E., Huţ, E. F., Dehelean, C. A., & Cretu, O. M. (2021) · Cancers · doi:10.3390/cancers13153651 · Cited by 78 · see citing works

High-dimensional role of AI and machine learning in cancer research

Capobianco, E. (2022) · British Journal of Cancer · doi:10.1038/s41416-021-01689-z · Cited by 76 · see citing works

Artificial Intelligence in Drug Design: Algorithms, Applications, Challenges and Ethics

Arabi, A. A. (2021) · Future Drug Discovery · doi:10.4155/fdd-2020-0028 · Cited by 74 · see citing works

Artificial intelligence for search and discovery of quantum materials

Stanev, V., Choudhary, K. K., Kusne, A. G., Paglione, J., & Takeuchi, I. (2021) · Communications Materials · doi:10.1038/s43246-021-00209-z · Cited by 69 · see citing works

Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment

Kumar, K. S., Miskovic, V., Blasiak, A., Sundar, R., Pedrocchi, A., Pearson, A. T., Prelaj, A., & Ho, D. (2023) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_390084 · Cited by 64 · see citing works

Towards machine learning for hydrogel drug delivery systems

Owh, C., Ho, D., Loh, X. J., & Xue, K. (2022) · Trends in biotechnology · doi:10.1016/j.tibtech.2022.09.019 · Cited by 59 · see citing works

Addressing COVID‐19 Drug Development with Artificial Intelligence

Ho, D. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000070 · Cited by 57 · see citing works

Vascularized Tumor Spheroid-on-a-Chip Model Verifies Synergistic Vasoprotective and Chemotherapeutic Effects

Hu, Z., Cao, Y., Galan, E. A., Hao, L., Zhao, H., Tang, J., Sang, G., Wang, H., Xu, B., & Ma, S. (2022) · ACS Biomaterials Science & Engineering · doi:10.1021/acsbiomaterials.1c01099 · Cited by 57 · see citing works

Artificial 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

Application of artificial intelligence in medical imaging for tumor diagnosis and treatment: a comprehensive approach

Huang, J., Xiang, Y., Gan, S., Wu, L., Yan, J., Dong, Y., & Zhang, J. (2025) · Discover Oncology · doi:10.1007/s12672-025-03307-3 · Cited by 61 · see citing works

Integrating AI into Cancer Immunotherapy—A Narrative Review of Current Applications and Future Directions

Olawade, D. B., David-Olawade, A. C., Adereni, T., Egbon, E., Teke, J., & Boussios, S. (2025) · Diseases · doi:10.3390/diseases13010024 · Cited by 50 · see citing works

IDentif . AI : Rapidly optimizing combination therapy design against severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐Cov‐2) with digital drug development

Blasiak, A., Lim, J. J., Seah, S. G. K., Kee, T. W., Remus, A., Chye, D. H., Wong, P. S., Hooi, L., Truong, A. T. L., Le, N., et al. (2020) · Bioengineering & Translational Medicine · doi:10.1002/btm2.10196 · Cited by 46 · see citing works

Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers

Cabral, B. P., Braga, L. A. M., Syed-Abdul, S., & Mota, F. B. (2023) · Current Oncology · doi:10.3390/curroncol30030260 · Cited by 47 · see citing works

An enhanced cascade-based deep forest model for drug combination prediction

Lin, W., Wu, L., Zhang, Y., Wen, Y., Yan, B., Dai, C., Liu, K., He, S., & Bo, X. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab562 · Cited by 44 · see citing works

Bottom-up design of hydrogels for programmable drug release

Owh, C., Ow, V., Lin, Q., Wong, J. H. M., Ho, D., Loh, X. J., & Xue, K. (2022) · Biomaterials Advances · doi:10.1016/j.bioadv.2022.213100 · Cited by 42 · see citing works

Clinically Oriented CBCT Periapical Lesion Evaluation via 3D CNN Algorithm

Fu, W.-X., Zhu, Q. K., Li, N., Wang, Y., Deng, S., Chen, H. P., Shen, J., Meng, L., & Bian, Z. (2023) · Journal of Dental Research · doi:10.1177/00220345231201793 · Cited by 40 · see citing works

Applications and perspectives of Generative Artificial Intelligence in agriculture

Pallottino, F., Violino, S., Figorilli, S., Pane, C., Aguzzi, J., Colle, G., Nemmi, E. N., Montaghi, A., Chatzievangelou, D., Antonucci, F., et al. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.109919 · Cited by 40 · see citing works

Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy

Zhang, Z., & Wei, X. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.02.005 · Cited by 41 · see citing works

Overcoming Pilotitis in Digital Medicine at the Intersection of Data, Clinical Evidence, and Adoption

Egermark, M., Blasiak, A., Remus, A., Sapanel, Y., & Ho, D. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202200056 · Cited by 38 · see citing works

Artificial Intelligence-Driven Innovations in Oncology Drug Discovery: Transforming Traditional Pipelines and Enhancing Drug Design

Albani, F. G., Alghamdi, S. S., Almutairi, M. M., & Alqahtani, T. (2025) · Drug Design Development and Therapy · doi:10.2147/dddt.s509769 · Cited by 42 · see citing works

Effective blood vessels reconstruction methodology for early detection and classification of diabetic retinopathy using OCTA images by artificial neural network

Abdelsalam, M. M. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100390 · Cited by 38 · see citing works

Ethics for AI in Plastic Surgery: Guidelines and Review

Kenig, N., Echeverría, J. M., & Rubi, C. G. (2024) · Aesthetic Plastic Surgery · doi:10.1007/s00266-024-03932-3 · Cited by 42 · see citing works

AI-enabled chatbots healthcare systems: an ethical perspective on trust and reliability

Basharat, I., & Shahid, S. (2024) · Journal of Health Organization and Management · doi:10.1108/jhom-10-2023-0302 · Cited by 38 · see citing works

Artificial intelligence in cancer research: learning at different levels of data granularity

Cirillo, D., Núñez‐Carpintero, I., & Valencia, A. (2021) · Molecular Oncology · doi:10.1002/1878-0261.12920 · Cited by 41 · see citing works

Physicians’ Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform

Vijayakumar, S., Lee, V. V., Leong, Q. Y., Hong, S. J., Blasiak, A., & Ho, D. (2023) · JMIR Human Factors · doi:10.2196/48476 · Cited by 34 · see citing works

An Inflection Point in Cancer Protein Biomarkers: What was and What's Next

Barker, A. D., Alba, M. M., Mallick, P., Agus, D. B., & Lee, J. S. H. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100569 · Cited by 33 · see citing works

Evolution of artificial intelligence as a modern technology in advanced cancer therapy

Khan, M. S., Alshahrani, M. Y., Wahab, S., Gupta, G., & Kesharwani, P. (2024) · Journal of Drug Delivery Science and Technology · doi:10.1016/j.jddst.2024.105892 · Cited by 35 · see citing works

Improving the therapeutic ratio of radiotherapy against radioresistant cancers: Leveraging on novel artificial intelligence-based approaches for drug combination discovery

Poon, D., Tay, L. M., Ho, D., Chua, M. L. K., Chow, E. K., & Yeo, E. L. L. (2021) · Cancer Letters · doi:10.1016/j.canlet.2021.04.019 · Cited by 30 · see citing works

The IDentif.AI-x pandemic readiness platform: Rapid prioritization of optimized COVID-19 combination therapy regimens

Blasiak, A., Truong, A. T. L., Remus, A., Hooi, L., Seah, S. G. K., Wang, P., Chye, D. H., Lim, A. P. C., Ng, K. T., Teo, S. T., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00627-4 · Cited by 29 · see citing works

Artificial intelligence-driven new drug discovery targeting serine/threonine kinase 33 for cancer treatment

Tran, N. L., Kim, H., Shin, C.-H., Ko, E., & Oh, S. J. (2023) · Cancer Cell International · doi:10.1186/s12935-023-03176-2 · Cited by 27 · see citing works

Application of Photoactive Compounds in Cancer Theranostics: Review on Recent Trends from Photoactive Chemistry to Artificial Intelligence

Szymaszek, P., Tyszka‐Czochara, M., & Ortyl, J. (2024) · Molecules · doi:10.3390/molecules29133164 · Cited by 26 · see citing works

Personalized dose selection platform for patients with solid tumors in the PRECISE CURATE.AI feasibility trial

Blasiak, A., Truong, A. T. L., Foo, N., Tan, L. W. J., Kumar, K. S., Tan, S., Teo, C. B., Tan, B. K. J., Tadeo, X., Tan, H. L., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00835-7 · Cited by 28 · see citing works

Haplotype Motif-Based Models for KIR-Genotype Informed Selection of Hematopoietic Cell Donors Fail to Predict Outcome of Patients With Myelodysplastic Syndromes or Secondary Acute Myeloid Leukemia

Schetelig, J., Baldauf, H., Köster, L., Kuxhausen, M., Heidenreich, F., de Wreede, L. C., Spellman, S. R., van Gelder, M., Bruno, B., Onida, F., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2020.584520 · Cited by 26 · see citing works

Challenges and the Evolving Landscape of Assessing Blood-Based PD-L1 Expression as a Biomarker for Anti-PD-(L)1 Immunotherapy

Wang, T., Denman, D., Bacot, S. M., & Feldman, G. M. (2022) · Biomedicines · doi:10.3390/biomedicines10051181 · Cited by 26 · see citing works

Artificial Intelligence as a Potential Catalyst to a More Equitable Cancer Care

García-Saisó, S., Martí, M., Pesce, K. A., Luciani, S., Mújica, Ó. J., Hennis, A. J. M., & D'Agostino, M. J. (2024) · JMIR Cancer · doi:10.2196/57276 · Cited by 27 · see citing works

Fighting viruses with materials science: Prospects for antivirus surfaces, drug delivery systems and artificial intelligence

Rosa, V., Ho, D., Silva, R. S., Siqueira, W. L., & Silikas, N. (2021) · Dental Materials · doi:10.1016/j.dental.2020.12.004 · Cited by 24 · see citing works

Possible integration of artificial intelligence with photodynamic therapy and diagnosis: A review

Nkune, N. W., & Abrahamse, H. (2024) · Journal of Drug Delivery Science and Technology · doi:10.1016/j.jddst.2024.106210 · Cited by 24 · see citing works

A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery

Ashok, G., & Ramaiah, S. (2022) · Medical Oncology · doi:10.1007/s12032-022-01815-8 · Cited by 21 · see citing works

CURATE.AI-assisted dose titration for anti-hypertensive personalized therapy: study protocol for a multi-arm, randomized, pilot feasibility trial using CURATE.AI (CURATE.AI ADAPT trial)

Truong, A. T. L., Tan, S., Wang, G. Z., Yip, A. W., Egermark, M., Yeung, W. L. K., Lee, V. V., Chan, M. Y., Kumar, K. S., Tan, L. W. J., et al. (2023) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztad063 · Cited by 20 · see citing works

Artificial Intelligence and Cancer Drug Development

Yang, M. Q., Wang, S., Yang, F., Darsey, J. A., Ghosh, A., & Li, H. (2021) · Recent Patents on Anti-Cancer Drug Discovery · doi:10.2174/1574892816666210728123758 · Cited by 20 · see citing works

Artificial Intelligence-Assisted Digital Immunoassay Based on a Programmable-Particle-Decoding Technique for Multitarget Ultrasensitive Detection

Zhou, Y., Zhao, W., Feng, Y. X., Niu, X., Dong, Y., & Chen, Y. (2022) · Analytical Chemistry · doi:10.1021/acs.analchem.2c04703 · Cited by 20 · see citing works

IBD Systems Biology Is Here to Stay

Fiocchi, C., & Iliopoulos, D. (2021) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa343 · Cited by 20 · see citing works

Immune checkpoint inhibitors and immunosuppressive tumor microenvironment: current challenges and strategies to overcome resistance

Gill, G. S., Kharb, S., Goyal, G., Das, P., Kurdia, K. C., Dhar, R., & Karmakar, S. (2025) · Immunopharmacology and Immunotoxicology · doi:10.1080/08923973.2025.2504906 · Cited by 19 · see citing works

Implementing biological markers as a tool to guide clinical care of patients with pancreatic cancer

Iovanna, J. (2021) · Translational Oncology · doi:10.1016/j.tranon.2020.100965 · Cited by 18 · see citing works

Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence

Derbal, Y. (2024) · Cancer Control · doi:10.1177/10732748241264704 · Cited by 17 · see citing works

Artificial Intelligence – Challenges and Chances for Europe

Straus, J. (2020) · European Review · doi:10.1017/s1062798720001106 · Cited by 17 · see citing works

IDentif.AI-Omicron: Harnessing an AI-Derived and Disease-Agnostic Platform to Pinpoint Combinatorial Therapies for Clinically Actionable Anti-SARS-CoV-2 Intervention

Blasiak, A., Truong, A. T. L., Wang, P., Hooi, L., Chye, D. H., Tan, S., You, K., Remus, A., Allen, D. M., Chai, L. Y. A., et al. (2022) · ACS Nano · doi:10.1021/acsnano.2c06366 · Cited by 18 · see citing works

Digital Nanomedicine: A New Frontier for Drug Development

Ho, D. (2022) · ACS Nano · doi:10.1021/acsnano.2c01835 · Cited by 16 · see citing works

Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery

Sahu, R. C., Arora, S., Kumar, D. K., & Agrawal, A. K. (2025) · Med Research · doi:10.1002/mdr2.70043 · Cited by 18 · see citing works

Microfluidic Synthesis of the Tumor Microenvironment-Responsive Nanosystem for Type-I Photodynamic Therapy

Huang, C., Chen, M., Du, L., Xiang, J., Jiang, D., & Liu, W. (2022) · Molecules · doi:10.3390/molecules27238386 · Cited by 15 · see citing works

N-of-1 medicine

Wang, P., Leong, Q. Y., Lau, N. Y., Ng, W. Y., Kwek, S. P., Tan, L. W. J., Song, S.-W., You, K., Chong, L. M., Zhuang, I., et al. (2024) · Singapore Medical Journal · doi:10.4103/singaporemedj.smj-2023-243 · Cited by 14 · see citing works

Personalized dose selection for the first Waldenström macroglobulinemia patient on the PRECISE CURATE.AI trial

Blasiak, A., Tan, L. W. J., Chong, L. M., Tadeo, X., Truong, A. T. L., Kumar, K. S., Sapanel, Y., Poon, M. L. M., Sundar, R., Mel, S. D., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01195-5 · Cited by 13 · see citing works

Artificial intelligence–based 5‐year survival prediction and prognosis of DNp73 expression in rectal cancer patients

Pham, T. D., Fan, C., Zhang, H., & Sun, X. (2020) · Clinical and Translational Medicine · doi:10.1002/ctm2.159 · Cited by 14 · see citing works

Use of digital healthcare solutions for care delivery during a pandemic-chances and (cyber) risks referring to the example of the COVID-19 pandemic

Kaiser, F. K., Wiens, M., & Schultmann, F. (2021) · Health and Technology · doi:10.1007/s12553-021-00541-x · Cited by 17 · see citing works

Nanotechnology‐Driven Drug Delivery Systems for Lung Cancer: Computational Advances and Clinical Perspectives

Yi, M., Li, Y., Jie, H., & Deng, S. (2025) · Thoracic Cancer · doi:10.1111/1759-7714.70134 · Cited by 13 · see citing works

Comparing Performances of Predictive Models of Toxicity after Radiotherapy for Breast Cancer Using Different Machine Learning Approaches

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A Novel Bionic Catalyst-Mediated Drug Delivery System for Enhanced Sonodynamic Therapy

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Digital Oncology in Morocco: Embracing Artificial Intelligence in a New Era

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