Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Hallmarks of artificial intelligence contributions to precision oncology

Nature Cancer volume 6, pages 417–431 (2025)Cite this article

Subjects

Abstract

The integration of artificial intelligence (AI) into oncology promises to revolutionize cancer care. In this Review, we discuss ten AI hallmarks in precision oncology, organized into three groups: (1) cancer prevention and diagnosis, encompassing cancer screening, detection and profiling; (2) optimizing current treatments, including patient outcome prediction, treatment planning and monitoring, clinical trial design and matching, and developing response biomarkers; and (3) advancing new treatments by identifying treatment combinations, discovering cancer vulnerabilities and designing drugs. We also survey AI applications in interventional clinical trials and address key challenges to broader clinical adoption of AI: data quality and quantity, model accuracy, clinical relevance and patient benefit, proposing actionable solutions for each.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Evolution of AI and key models and major data types in precision oncology.
Fig. 2: Ten AI hallmarks characterizing its contributions to precision oncology.
Fig. 3: Collaboration among key stakeholder groups for AI-driven patient benefit.

Similar content being viewed by others

Data availability

The raw data from Trialtrove are available under restricted access only to license holders of Trialtrove at https://citeline.informa.com/trials/results. The processed data are available at https://github.com/ruppinlab/ProcessTrialtrove/blob/main/SupplementaryTableInterventionalTrialsUsingAI.xlsx.

Code availability

The Python programs used in this study are available at https://github.com/ruppinlab/ProcessTrialtrove. Running the programs requires a Trialtrove license and download of the trial data. Other readers may find the programs useful to read to understand in detail how we processed the Trialtrove data.

References

  1. Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).

    Article  PubMed  Google Scholar 

  2. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    Article  CAS  PubMed  Google Scholar 

  3. Friedman, A. A., Letai, A., Fisher, D. E. & Flaherty, K. T. Precision medicine for cancer with next-generation functional diagnostics. Nat. Rev. Cancer 15, 747–756 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Mateo, J. et al. Delivering precision oncology to patients with cancer. Nat. Med. 28, 658–665 (2022).

    Article  CAS  PubMed  Google Scholar 

  5. Jiang, P. et al. Big data in basic and translational cancer research. Nat. Rev. Cancer 22, 625–639 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Dlamini, Z., Francies, F. Z., Hull, R. & Marima, R. Artificial intelligence (AI) and big data in cancer and precision oncology. Comput. Struct. Biotechnol. J. 18, 2300–2311 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Elemento, O., Leslie, C., Lundin, J. & Tourassi, G. Artificial intelligence in cancer research, diagnosis and therapy. Nat. Rev. Cancer 21, 747–752 (2021).

    Article  CAS  PubMed  Google Scholar 

  9. Huang, S., Yang, J., Fong, S. & Zhao, Q. Artificial intelligence in cancer diagnosis and prognosis: opportunities and challenges. Cancer Lett. 471, 61–71 (2020).

    Article  CAS  PubMed  Google Scholar 

  10. Swanson, K., Wu, E., Zhang, A., Alizadeh, A. A. & Zou, J. From patterns to patients: advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. Cell 186, 1772–1791 (2023).

    Article  CAS  PubMed  Google Scholar 

  11. Lång, K. et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol. 24, 936–944 (2023).

    Article  PubMed  Google Scholar 

  12. Pearl, J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann, 1988).

  13. Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273–297 (1995).

    Article  Google Scholar 

  14. Quinlan, J. R. Induction of decision trees. Mach. Learn. 1, 81–106 (1986).

    Article  Google Scholar 

  15. Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 1106–1114 (NeurIPS, 2012).

  16. Sarker, I. H. Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput. Sci. 2, 420 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. https://papers.nips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html (2017).

  18. Minaee, S. et al. Large language models: a survey. Preprint at https://arxiv.org/abs/2402.06196 (2024).

  19. Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J. & Shah, S. P. Harnessing multimodal data integration to advance precision oncology. Nat. Rev. Cancer 22, 114–126 (2022).

    Article  CAS  PubMed  Google Scholar 

  20. Clark, K. et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26, 1045–1057 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Cao, R. M. et al. Joint prostate cancer detection and Gleason score prediction in mp-MRI via FocalNet. IEEE Trans. Med. Imaging 38, 2496–2506 (2019).

    Article  PubMed  Google Scholar 

  22. Lu, M. T., Raghu, V. K., Mayrhofer, T., Aerts, H. & Hoffmann, U. Deep learning using chest radiographs to identify high-risk smokers for lung cancer screening computed tomography: development and validation of a prediction model. Ann. Intern. Med. 173, 704–713 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Coudray, N. et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559–1567 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. McKinney, S. M. et al. International evaluation of an AI system for breast cancer screening. Nature 577, 89–94 (2020).

    Article  CAS  PubMed  Google Scholar 

  25. Areia, M. et al. Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study. Lancet Digit. Health 4, E436–E444 (2022).

    Article  CAS  PubMed  Google Scholar 

  26. Qian, X. J. et al. Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning. Nat. Biomed. Eng. 5, 522–532 (2021).

    Article  PubMed  Google Scholar 

  27. Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jiang, L. Y. et al. Health system-scale language models are all-purpose prediction engines. Nature 619, 357–362 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Gunjur, A. et al. A gut microbial signature for combination immune checkpoint blockade across cancer types. Nat. Med. 30, 797–809 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Sammut, S. J. et al. Multi-omic machine learning predictor of breast cancer therapy response. Nature 601, 623–629 (2022).

    Article  CAS  PubMed  Google Scholar 

  32. Chakraborty, S., Hosen, M. I., Ahmed, M. & Shekhar, H. U. Onco-multi-OMICS approach: a new frontier in cancer research. BioMed Res. Int. 2018, 9836256 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Chang, T.-G. et al. LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features. Nat. Cancer 5, 1158–1175 (2024).

    Article  PubMed  Google Scholar 

  34. de Koning, H. J. et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N. Engl. J. Med. 382, 503–513 (2020).

    Article  PubMed  Google Scholar 

  35. Knudsen, A. B. et al. Estimated US cancer deaths prevented with increased use of lung, colorectal, breast, and cervical cancer screening. JAMA Netw. Open 6, e2344698 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Gregoor, A. M. S. et al. An artificial intelligence based app for skin cancer detection evaluated in a population based setting. NPJ Digit. Med. 6, 90 (2023).

    Article  Google Scholar 

  37. Nyberg, T. et al. CanRisk-Prostate: a comprehensive, externally validated risk model for the prediction of future prostate cancer. J. Clin. Oncol. 41, 1092–1104 (2023).

    Article  CAS  PubMed  Google Scholar 

  38. Crosby, D. et al. Early detection of cancer. Science 375, eaay9040 (2022).

    Article  CAS  PubMed  Google Scholar 

  39. Torre, L. A., Siegel, R. L., Ward, E. M. & Jemal, A. Global cancer incidence and mortality rates and trends—an update. Cancer Epidemiol. Biomarkers Prev. 25, 16–27 (2016).

    Article  PubMed  Google Scholar 

  40. Shiels, M. S. et al. Opportunities for achieving the Cancer Moonshot goal of a 50% reduction in cancer mortality by 2047. Cancer Discov. 13, 1084–1099 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Lotter, W. et al. Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach. Nat. Med. 27, 244–249 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bejnordi, B. E. et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318, 2199–2210 (2017).

    Article  Google Scholar 

  43. Baldwin, D. R. et al. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax 75, 306–312 (2020).

    Article  PubMed  Google Scholar 

  44. Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020).

    Article  CAS  PubMed  Google Scholar 

  45. Cohen, J. D. et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359, 926–930 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lu, M. Y. et al. AI-based pathology predicts origins for cancers of unknown primary. Nature 594, 106–110 (2021).

    Article  PubMed  Google Scholar 

  47. André, F., Rassy, E., Marabelle, A., Michiels, S. & Besse, B. Forget lung, breast or prostate cancer: why tumour naming needs to change. Nature 626, 26–29 (2024).

    Article  PubMed  Google Scholar 

  48. Wang, X. Y. et al. A pathology foundation model for cancer diagnosis and prognosis prediction. Nature 634, 970–978 (2024).

    Article  CAS  PubMed  Google Scholar 

  49. Kather, J. N. et al. Pan-cancer image-based detection of clinically actionable genetic alterations. Nat. Cancer 1, 789–799 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Fu, Y. et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat. Cancer 1, 800–810 (2020).

    Article  CAS  PubMed  Google Scholar 

  51. Wagner, S. J. et al. Transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study. Cancer Cell 41, 1650–1661 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Jain, M. S. & Massoud, T. F. Predicting tumour mutational burden from histopathological images using multiscale deep learning. Nat. Mach. Intell. 2, 356–362 (2020).

    Article  Google Scholar 

  53. Shamai, G. et al. Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer. Nat. Commun. 13, 6753 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Rakaee, M. et al. Association of machine learning-based assessment of tumor-infiltrating lymphocytes on standard histologic images with outcomes of immunotherapy in patients with NSCLC. JAMA Oncol. 9, 51–60 (2023).

    Article  PubMed  Google Scholar 

  55. Hoang, D.-T. et al. Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning. Nat. Med. 30, 1952–1961 (2024).

    Article  CAS  PubMed  Google Scholar 

  56. Hoang, D. T. et al. A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics. Nat. Cancer 5, 1305–1317 (2024).

    Article  CAS  PubMed  Google Scholar 

  57. Erho, N. et al. Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS ONE 8, e66855 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Parikh, R. B. et al. Machine learning approaches to predict 6-month mortality among patients with cancer. JAMA Netw. Open 2, e1915997 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Manz, C. R. et al. Validation of a machine learning algorithm to predict 180-day mortality for outpatients with cancer. JAMA Oncol. 6, 1723–1730 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Nunes, L. et al. Prognostic genome and transcriptome signatures in colorectal cancers. Nature 633, 137–146 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Volinsky-Fremond, S. et al. Prediction of recurrence risk in endometrial cancer with multimodal deep learning. Nat. Med. 30, 1962–1973 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Sun, D., Wang, M. & Li, A. A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE/ACM Trans. Comput. Biol. Bioinform. 16, 841–850 (2018).

    Article  Google Scholar 

  63. Chen, R. J. et al. Pan-cancer integrative histology—genomic analysis via multimodal deep learning. Cancer Cell 40, 865–878 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. McIntosh, C. et al. Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer. Nat. Med. 27, 999–1005 (2021).

    Article  CAS  PubMed  Google Scholar 

  65. Gallaher, J. A., Enriquez-Navas, P. M., Luddy, K. A., Gatenby, R. A. & Anderson, A. R. Spatial heterogeneity and evolutionary dynamics modulate time to recurrence in continuous and adaptive cancer therapies. Cancer Res. 78, 2127–2139 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Zhang, J., Cunningham, J. J., Brown, J. S. & Gatenby, R. A. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat. Commun. 8, 1816 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Gallagher, K. et al. Mathematical model-driven deep learning enables personalized adaptive therapy. Cancer Res. 84, 1929–1941 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Timilsina, M., Tandan, M. & Nováček, V. Machine learning approaches for predicting the onset time of the adverse drug events in oncology. Mach. Learn. Appl. 9, 100367 (2022).

    Google Scholar 

  69. Ricciuti, B. et al. Genomic and immunophenotypic landscape of acquired resistance to PD-(L)1 blockade in non-small-cell lung cancer. J. Clin. Oncol. 42, 1311–1321 (2024).

    Article  CAS  PubMed  Google Scholar 

  70. Unger, J. M., Shulman, L. N., Facktor, M. A., Nelson, H. & Fleury, M. E. National estimates of the participation of patients with cancer in clinical research studies based on Commission on Cancer accreditation data. J. Clin. Oncol. 42, 2139–2142 (2024).

    Article  PubMed  Google Scholar 

  71. Stensland, K. D. et al. Adult cancer clinical trials that fail to complete: an epidemic? J. Natl Cancer Inst. 106, dju229 (2014).

    Article  PubMed  Google Scholar 

  72. Murthy, V. H., Krumholz, H. M. & Gross, C. P. Participation in cancer clinical trials: race-, sex-, and age-based disparities. JAMA 291, 2720–2726 (2004).

    Article  CAS  PubMed  Google Scholar 

  73. Kwiatkowski, K., Coe, K., Bailar, J. C. & Swanson, G. M. Inclusion of minorities and women in cancer clinical trials, a decade later: have we improved? Cancer 119, 2956–2963 (2013).

    Article  PubMed  Google Scholar 

  74. Kammula, A. V., Schäffer, A. A., Rajagopal, P. S., Kurzrock, R. & Ruppin, E. Outcome differences by sex in oncology clinical trials. Nat. Commun. 15, 2608 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Liu, R. et al. Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature 592, 629–633 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Alexander, M. et al. Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients. JAMIA Open 3, 209–215 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Beck, J. T. et al. Artificial intelligence tool for optimizing eligibility screening for clinical trials in a large community cancer center. JCO Clin. Cancer Inform. 4, 50–59 (2020).

    Article  PubMed  Google Scholar 

  78. Kather, J. N., Ferber, D., Wiest, I. C., Gilbert, S. & Truhn, D. Large language models could make natural language again the universal interface of healthcare. Nat. Med. 30, 2708–2710 (2024).

    Article  CAS  PubMed  Google Scholar 

  79. Jin, Q. et al. Matching patients to clinical trials with large language models. Nat. Commun. 15, 9074 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Wornow, M. et al. Zero-shot clinical trial patient matching with LLMs. NEJM AI 2, AIcs2400360 (2024).

  81. Kim, K. et al. Predicting clinical benefit of immunotherapy by antigenic or functional mutations affecting tumour immunogenicity. Nat. Commun. 11, 951 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Jiang, P. et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat. Med. 24, 1550–1558 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Foersch, S. et al. Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer. Nat. Med. 29, 430–439 (2023).

    Article  CAS  PubMed  Google Scholar 

  84. Lu, Y.-T. et al. Cell-free DNA methylation as a predictive biomarker of response to neoadjuvant chemotherapy for patients with muscle-invasive bladder cancer in SWOG S1314. Eur. Urol. Oncol. 6, 516–524 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Chang, T.-G. et al. Tumor and blood B-cell abundance outperforms established immune checkpoint blockade response prediction signatures in head and neck cancer. Ann. Oncol. https://doi.org/10.1016/j.annonc.2024.11.008 (2024).

  86. Litchfield, K. et al. Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition. Cell 184, 596–614 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Cao, Y., Chang, T.-G., Sahni, S. & Ruppin, E. Reusability report: leveraging supervised learning to uncover phenotype-relevant biology from single-cell RNA sequencing data. Nat. Mach. Intell. 6, 307–314 (2024).

    Article  Google Scholar 

  88. Sinha, S. et al. PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors. Nat. Cancer 5, 938–952 (2024).

    Article  PubMed  Google Scholar 

  89. Chen, J. et al. Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data. Nat. Commun. 13, 6494 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Ma, J. et al. Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients. Nat. Cancer 2, 233–244 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Park, S. et al. A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors. Nat. Cancer 5, 996–1009 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Ren, S. X., Cooper, G. F., Chen, L. J. & Lu, X. H. An interpretable deep learning framework for genome-informed precision oncology. Nat. Mach. Intell. 6, 742–743 (2024).

    Article  Google Scholar 

  93. Chefer, H., Gur, S. & Wolf, L. Transformer interpretability beyond attention visualization. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (eds. Pereira, F. et al.) 782–791 (CVPR, 2021).

  94. El Nahhas, O. S. M. et al. Regression-based deep-learning predicts molecular biomarkers from pathology slides. Nat. Commun. 15, 1253 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Saednia, K., Tran, W. T. & Sadeghi-Naini, A. A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre-treatment tumor biopsies. Med. Phys. 50, 7852–7864 (2023).

    Article  CAS  PubMed  Google Scholar 

  96. Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206–215 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Mokhtari, R. B. et al. Combination therapy in combating cancer. Oncotarget 8, 38022–38043 (2017).

    Article  PubMed Central  Google Scholar 

  98. Jin, H., Wang, L. & Bernards, R. Rational combinations of targeted cancer therapies: background, advances and challenges. Nat. Rev. Drug Discov. 22, 213–234 (2023).

    Article  CAS  PubMed  Google Scholar 

  99. Loewe, S. The problem of synergism and antagonism of combined drugs. Arzneimittelforschung 3, 285–290 (1953).

    CAS  PubMed  Google Scholar 

  100. Yadav, B., Wennerberg, K., Aittokallio, T. & Tang, J. Searching for drug synergy in complex dose–response landscapes using an interaction potency model. Comput. Struct. Biotechnol. J. 13, 504–513 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Palmer, A. C. & Sorger, P. K. Combination cancer therapy can confer benefit via patient-to-patient variability without drug additivity or synergy. Cell 171, 1678–1691 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Kuenzi, B. M. et al. Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell 38, 672–684 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Bertin, P. et al. RECOVER identifies synergistic drug combinations in vitro through sequential model optimization. Cell Rep. Methods 3, 100599 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Wu, L. et al. A hybrid deep forest-based method for predicting synergistic drug combinations. Cell Rep. Methods 3, 100411 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Rafiei, F. et al. DeepTraSynergy: drug combinations using multimodal deep learning with transformers. Bioinformatics 39, btad438 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Li, T. et al. CancerGPT for few shot drug pair synergy prediction using large pretrained language models. NPJ Digit. Med. 7, 40 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  107. Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens. Nature 568, 511–516 (2019).

    Article  CAS  PubMed  Google Scholar 

  108. Schaffer, A. A., Chung, Y. M., Kammula, A., Ruppin, E. & Lee, J. S. A systematic analysis of the landscape of synthetic lethality-driven precision oncology. Med 5, 73–89 (2024).

    Article  PubMed  Google Scholar 

  109. Lord, C. J. & Ashworth, A. PARP inhibitors: synthetic lethality in the clinic. Science 355, 1152–1158 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Cupit-Link, M. et al. Response to PARP inhibition in BARD1-mutated refractory neuroblastoma. N. Engl. J. Med. 391, 659–661 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  111. Lee, J. S. et al. Synthetic lethality-mediated precision oncology via the tumor transcriptome. Cell 184, 2487–2502 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Dinstag, G. et al. Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome. Med 4, 15–30 (2023).

    Article  CAS  PubMed  Google Scholar 

  113. Shi, X. et al. Building a translational cancer dependency map for the Cancer Genome Atlas. Nat. Cancer 5, 1176–1194 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Vandereyken, K., Sifrim, A., Thienpont, B. & Voet, T. Methods and applications for single-cell and spatial multi-omics. Nat. Rev. Genet. 24, 494–515 (2023).

    Article  CAS  PubMed  Google Scholar 

  115. Kwon, J. et al. Single-cell mapping of combinatorial target antigens for CAR switches using logic gates. Nat. Biotechnol. 41, 1593–1605 (2023).

    Article  CAS  PubMed  Google Scholar 

  116. Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38, 333–342 (2020).

    Article  CAS  PubMed  Google Scholar 

  117. Wang, Y. L., Ma, S. Y. & Ruzzo, W. L. Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities. Sci. Rep. 10, 3490 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Calcoen, D., Elias, L. & Yu, X. What does it take to produce a breakthrough drug? Nat. Rev. Drug Discov. 14, 161–162 (2015).

    Article  PubMed  Google Scholar 

  119. Zhavoronkov, A. et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol. 37, 1038–1040 (2019).

    Article  CAS  PubMed  Google Scholar 

  120. Hie, B. L. et al. Efficient evolution of human antibodies from general protein language models. Nat. Biotechnol. 42, 275–283 (2024).

    Article  CAS  PubMed  Google Scholar 

  121. Munson, B. P. et al. De novo generation of multi-target compounds using deep generative chemistry. Nat. Commun. 15, 3636 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Huang, L. et al. A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets. Nat. Commun. 15, 2657 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Wang, T. et al. Ab initio characterization of protein molecular dynamics with AI2BMD. Nature 635, 1019–1027 (2024).

  126. Shanehsazzadeh, A. et al. Unlocking de novo antibody design with generative artificial intelligence. Preprint at bioRxiv https://doi.org/10.1101/2023.01.08.523187 (2023).

  127. Stergiopoulos, S., Getz, K. A. & Blazynski, C. Evaluating the completeness of ClinicalTrials.gov. Ther. Innov. Regul. Sci. 53, 307–317 (2019).

    Article  PubMed  Google Scholar 

  128. Chopra, H., Baig, A. A., Gautam, R. K. & Kamal, M. A. Application of artificial intelligence in drug discovery. Curr. Pharm. Des. 28, 2690–2703 (2022).

    Article  CAS  PubMed  Google Scholar 

  129. Jayatunga, M. K., Ayers, M., Bruens, L., Jayanth, D. & Meier, C. How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons. Drug Discov. Today 29, 104009 (2024).

    Article  Google Scholar 

  130. Maharana, K., Mondal, S. & Nemade, B. A review: data pre-processing and data augmentation techniques. Glob. Transit. Proc. 3, 91–99 (2022).

    Article  Google Scholar 

  131. Zhuang, F. et al. A comprehensive survey on transfer learning. In Proceedings of the IEEE 43–76 (IEEE, 2020).

  132. Yang, Q., Liu, Y., Chen, T. & Tong, Y. Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10, 12 (2019).

    Article  Google Scholar 

  133. Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at https://arxiv.org/abs/2108.07258 (2021).

  134. Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023).

    Article  CAS  PubMed  Google Scholar 

  135. Qiu, X. P. et al. Pre-trained models for natural language processing: a survey. Sci. China Technol. Sci. 63, 1872–1897 (2020).

    Article  Google Scholar 

  136. Cui, H. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat. Methods 21, 1470–1480 (2024).

    Article  CAS  PubMed  Google Scholar 

  137. Bhinder, B., Gilvary, C., Madhukar, N. S. & Elemento, O. Artificial intelligence in cancer research and precision medicine. Cancer Discov. 11, 900–915 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Kush, R. D. et al. FAIR data sharing: the roles of common data elements and harmonization. J. Biomed. Inform. 107, 103421 (2020).

    Article  CAS  PubMed  Google Scholar 

  139. Fortier, I. et al. Maelstrom research guidelines for rigorous retrospective data harmonization. Int. J. Epidemiol. 46, 103–105 (2017).

    PubMed  Google Scholar 

  140. Lehman, C. D. et al. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern. Med. 175, 1828–1837 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  141. Freeman, K. et al. Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. BMJ 368, m645 (2020).

    Google Scholar 

  142. Collins, G. S., Reitsma, J. B., Altman, D. G. & Moons, K. G. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) the TRIPOD statement. Circulation 131, 211–219 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  143. Lipkova, J. et al. Artificial intelligence for multimodal data integration in oncology. Cancer Cell 40, 1095–1110 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Cascarano, A. et al. Machine and deep learning for longitudinal biomedical data: a review of methods and applications. Artif. Intell. Rev. 56, 1711–1771 (2023).

    Article  Google Scholar 

  145. Placido, D. et al. A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nat. Med. 29, 1113–1122 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Hanahan, D. Hallmarks of cancer: new dimensions. Cancer Discov. 12, 31–46 (2022).

    Article  CAS  PubMed  Google Scholar 

  147. Derosa, L. et al. Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome. Cell 187, 3373–3389 (2024).

    Article  CAS  PubMed  Google Scholar 

  148. Teixeira, M. et al. A review of machine learning methods for cancer characterization from microbiome data. NPJ Precis. Oncol. 8, 123 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  149. Barkley, D. et al. Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment. Nat. Genet. 54, 1192–1201 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Hao, M. S. et al. Large-scale foundation model on single-cell transcriptomics. Nat. Methods 21, 1481–1491 (2024).

    Article  CAS  PubMed  Google Scholar 

  151. Shulman, E. D. et al. Path2Space: an AI approach for cancer biomarker discovery via histopathology inferred spatial transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2024.10.16.618609 (2024).

  152. Cisternino, F. et al. Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types. Nat. Commun. 15, 5906 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Arrieta, A. B. et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020).

    Article  Google Scholar 

  154. Wang, D. et al. ‘Brilliant AI doctor’ in rural clinics: challenges in AI-powered clinical decision support system deployment. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems 697 (IBM, 2021).

  155. Flanagin, A., Frey, T., Christiansen, S. L. & AMA Manual of Style Committee. Updated guidance on the reporting of race and ethnicity in medical and science journals. JAMA 326, 621–627 (2021).

    Article  PubMed  Google Scholar 

  156. Goodfellow, I. J., Shlens, J. & Szegedy, C. Explaining and harnessing adversarial examples. Preprint at https://arxiv.org/abs/1412.6572 (2014).

  157. Clusmann, J. et al. Prompt injection attacks on large language models in oncology. Preprint at https://arxiv.org/abs/2407.18981 (2024).

  158. Yang, J. W. et al. Poisoning medical knowledge using large language models. Nat. Mach. Intell. 6, 1156–1168 (2024).

    Article  Google Scholar 

  159. Hager, P. et al. Evaluation and mitigation of the limitations of large language models in clinical decision-making. Nat. Med. 30, 2613–2622 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Bedi, S. et al. Testing and evaluation of health care applications of large language models: a systematic review. JAMA https://doi.org/10.1001/jama.2024.21700 (2024).

  161. US Food and Drug Administration. Artificial intelligence and machine learning in software as a medical device. FDA https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device (2024).

  162. US Food and Drug Administration. Artificial intelligence and machine learning (AI/ML)-enabled medical devices. FDA https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices (2024).

  163. US Food and Drug Administration. Artificial intelligence and medical products. FDA https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-medical-products (2024).

  164. Liu, X. et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit. Health 2, e537–e548 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  165. US Food and Drug Administration. Artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD) action plan. FDA https://www.fda.gov/media/145022/download (2021).

  166. Yala, A. et al. Multi-institutional validation of a mammography-based breast cancer risk model. J. Clin. Oncol. 40, 1732–1740 (2022).

    Article  PubMed  Google Scholar 

  167. Yala, A. et al. Toward robust mammography-based models for breast cancer risk. Sci. Transl. Med. 13, eaba4373 (2021).

    Article  PubMed  Google Scholar 

  168. Richter, A. N. & Khoshgoftaar, T. M. A review of statistical and machine learning methods for modeling cancer risk using structured clinical data. Artif. Intell. Med. 90, 1–14 (2018).

    Article  PubMed  Google Scholar 

  169. Gentile, F. & Malara, N. Artificial intelligence for cancer screening and surveillance. ESMO Real World Data Digit. Oncol. 5, 100046 (2024).

    Article  Google Scholar 

  170. Hunter, B., Hindocha, S. & Lee, R. W. The role of artificial intelligence in early cancer diagnosis. Cancers 14, 1524 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Nassif, A. B., Abu Talib, M., Nasir, Q., Afadar, Y. & Elgendy, O. Breast cancer detection using artificial intelligence techniques: a systematic literature review. Artif. Intell. Med. 127, 102276 (2022).

    Article  PubMed  Google Scholar 

  172. Kumar, Y., Gupta, S., Singla, R. & Hu, Y. C. A systematic review of artificial intelligence techniques in cancer prediction and diagnosis. Arch. Comput. Methods Eng. 29, 2043–2070 (2022).

    Article  PubMed  Google Scholar 

  173. Dildar, M. et al. Skin cancer detection: a review using deep learning techniques. Int. J. Environ. Res. Public Health 18, 5479 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  174. Saba, T. Recent advancement in cancer detection using machine learning: systematic survey of decades, comparisons and challenges. J. Infect. Public Health 13, 1274–1289 (2020).

    Article  PubMed  Google Scholar 

  175. Alharbi, F. & Vakanski, A. Machine learning methods for cancer classification using gene expression data: a review. Bioengineering 10, 173 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. Tandel, G. S. et al. A review on a deep learning perspective in brain cancer classification. Cancers 11, 111 (2019).

    Article  PubMed Central  Google Scholar 

  177. Yaqoob, A., Musheer Aziz, R. & Verma, N. K. Applications and techniques of machine learning in cancer classification: a systematic review. Hum. Centric Intell. Syst. 3, 588–615 (2023).

    Article  Google Scholar 

  178. Shao, J., Ma, J., Zhang, Q., Li, W. & Wang, C. Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology. Semin. Cancer Biol. 91, 1–15 (2023).

    Article  CAS  PubMed  Google Scholar 

  179. Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V. & Fotiadis, D. I. Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13, 8–17 (2015).

    Article  CAS  PubMed  Google Scholar 

  180. Zhu, W., Xie, L., Han, J. & Guo, X. The application of deep learning in cancer prognosis prediction. Cancers 12, 603 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. Zhang, H. et al. Application of deep learning in cancer prognosis prediction model. Technol. Cancer Res. Treat. 22, 15330338231199287 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  182. Wang, M., Zhang, Q., Lam, S., Cai, J. & Yang, R. A review on application of deep learning algorithms in external beam radiotherapy automated treatment planning. Front. Oncol. 10, 580919 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  183. Kawamura, M. et al. Revolutionizing radiation therapy: the role of AI in clinical practice. J. Radiat. Res. 65, 1–9 (2024).

    Article  PubMed  Google Scholar 

  184. Yang, C.-Y., Shiranthika, C., Wang, C.-Y., Chen, K.-W. & Sumathipala, S. Reinforcement learning strategies in cancer chemotherapy treatments: a review. Comput. Methods Programs Biomed. 229, 107280 (2023).

    Article  PubMed  Google Scholar 

  185. Harrer, S., Shah, P., Antony, B. & Hu, J. Artificial intelligence for clinical trial design. Trends Pharmacol. Sci. 40, 577–591 (2019).

    Article  CAS  PubMed  Google Scholar 

  186. Hutson, M. How AI is being used to accelerate clinical trials. Nature 627, S2–S5 (2024).

    Article  CAS  PubMed  Google Scholar 

  187. Askin, S., Burkhalter, D., Calado, G. & El Dakrouni, S. Artificial intelligence applied to clinical trials: opportunities and challenges. Health Technol. 13, 203–213 (2023).

    Article  Google Scholar 

  188. Prelaj, A. et al. Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review. Ann. Oncol. 35, 29–65 (2024).

    Article  CAS  PubMed  Google Scholar 

  189. Bera, K., Braman, N., Gupta, A., Velcheti, V. & Madabhushi, A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat. Rev. Clin. Oncol. 19, 132–146 (2022).

    Article  CAS  PubMed  Google Scholar 

  190. Adam, G. et al. Machine learning approaches to drug response prediction: challenges and recent progress. NPJ Precis. Oncol. 4, 19 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  191. Tsigelny, I. F. Artificial intelligence in drug combination therapy. Brief. Bioinform. 20, 1434–1448 (2019).

    Article  CAS  PubMed  Google Scholar 

  192. Baptista, D., Ferreira, P. G. & Rocha, M. A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer. PLoS Comput. Biol. 19, e1010200 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  193. Wang, Y., Wang, J. & Liu, Y. Deep learning for predicting synergistic drug combinations: state-of-the-arts and future directions. Clin. Transl. Discov. 4, e317 (2024).

    Article  Google Scholar 

  194. Tanoli, Z., Vähä-Koskela, M. & Aittokallio, T. Artificial intelligence, machine learning, and drug repurposing in cancer. Expert Opin. Drug Discov. 16, 977–989 (2021).

    Article  CAS  PubMed  Google Scholar 

  195. Issa, N. T., Stathias, V., Schürer, S. & Dakshanamurthy, S. Machine and deep learning approaches for cancer drug repurposing. Semin. Cancer Biol. 68, 132–142 (2021).

    Article  CAS  PubMed  Google Scholar 

  196. You, Y. et al. Artificial intelligence in cancer target identification and drug discovery. Signal. Transduct. Target. Ther. 7, 156 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  197. Tan, P., Chen, X., Zhang, H., Wei, Q. & Luo, K. Artificial intelligence aids in development of nanomedicines for cancer management. Semin. Cancer Biol. 89, 61–75 (2023).

    Article  CAS  PubMed  Google Scholar 

  198. Askr, H. et al. Deep learning in drug discovery: an integrative review and future challenges. Artif. Intell. Rev. 56, 5975–6037 (2023).

    Article  PubMed  Google Scholar 

  199. Mak, K.-K., Wong, Y.-H. & Pichika, M. R. Artificial intelligence in drug discovery and development. In Drug Discovery and Evaluation: Safety and Pharmacokinetic Assays (eds Hock, F. J. & Pugsley, M. K.) 1461–1498 (Springer Open, 2023).

  200. Mullowney, M. W. et al. Artificial intelligence for natural product drug discovery. Nat. Rev. Drug Discov. 22, 895–916 (2023).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This research was supported by the US National Institutes of Health (NIH) Intramural Research Program, National Cancer Institute. This work used the computational resources of the NIH HPC Biowulf cluster (https://hpc.nih.gov). We thank S. Rajagopal and Z. Ronai for their thoughtful input in reviewing earlier versions of the manuscript.

Author information

Authors and Affiliations

  1. Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Tian-Gen Chang, Seongyong Park, Alejandro A. Schäffer, Peng Jiang & Eytan Ruppin

Authors
  1. Tian-Gen Chang
    View author publications

    Search author on:PubMed Google Scholar

  2. Seongyong Park
    View author publications

    Search author on:PubMed Google Scholar

  3. Alejandro A. Schäffer
    View author publications

    Search author on:PubMed Google Scholar

  4. Peng Jiang
    View author publications

    Search author on:PubMed Google Scholar

  5. Eytan Ruppin
    View author publications

    Search author on:PubMed Google Scholar

Corresponding authors

Correspondence to Tian-Gen Chang or Eytan Ruppin.

Ethics declarations

Competing interests

E.R. is a cofounder of MedAware, Metabomed and Pangea Biomed (divested) and is an unpaid member of Pangea Biomed’s and GSK Oncology’s scientific advisory boards. The other authors declare no competing interests.

Peer review

Peer review information

Nature Cancer thanks Hamid Ali and Federica Eduati for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Methods

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, TG., Park, S., Schäffer, A.A. et al. Hallmarks of artificial intelligence contributions to precision oncology. Nat Cancer 6, 417–431 (2025). https://doi.org/10.1038/s43018-025-00917-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s43018-025-00917-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer