Artificial intelligence (AI) is a transformative technology that is capturing popular imagination and can revolutionize biomedicine. AI and machine learning (ML) algorithms have the potential to break through existing barriers in oncology research and practice such as automating workflow processes, personalizing care, and reducing healthcare disparities. Emerging applications of AI/ML in the literature include screening and early detection of cancer, disease diagnosis, response prediction, prognosis, and accelerated drug discovery. Despite this excitement, only few AI/ML models have been properly validated and fewer have become regulated products for routine clinical use. In this review, we highlight the main challenges impeding AI/ML clinical translation. We present different clinical use cases from the domains of radiology, radiation oncology, immunotherapy, and drug discovery in oncology. We dissect the unique challenges and opportunities associated with each of these cases. Finally, we summarize the general requirements for successful AI/ML implementation in the clinic, highlighting specific examples and points of emphasis including the importance of multidisciplinary collaboration of stakeholders, role of domain experts in AI augmentation, transparency of AI/ML models, and the establishment of a comprehensive quality assurance program to mitigate risks of training bias and data drifts, all culminating toward safer and more beneficial AI/ML applications in oncology labs and clinics.
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Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380:1347–58.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56.
Elemento O, Leslie C, Lundin J, Tourassi G. Artificial intelligence in cancer research, diagnosis and therapy. Nat Rev Cancer. 2021;21:747–52.
Meropol NJ, Donegan J, Rich AS. Progress in the application of machine learning algorithms to cancer research and care. JAMA Netw Open. 2021;4:e2116063–e2116063.
El Naqa I, Haider MA, Giger ML, Ten, Haken RK. Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century. Br J Radiol. 2020;93:20190855.
Kann BH, Hosny A, Aerts H. Artificial intelligence for clinical oncology. Cancer Cell. 2021;39:916–27.
Gillies RJ, Schabath MB. Radiomics improves cancer screening and early detection. Cancer Epidemiol Biomark Prev. 2020;29:2556–67.
Kenner B, Chari ST, Kelsen D, Klimstra DS, Pandol SJ, Rosenthal M, et al. Artificial intelligence and early detection of pancreatic cancer: 2020 summative review. Pancreas. 2021;50:251–79.
Larsen M, Aglen CF, Lee CI, Hoff SR, Lund-Hanssen H, Lång K, et al. Artificial intelligence evaluation of 122 969 mammography examinations from a population-based screening program. Radiology. 2022;303:502–11.
Iqbal MJ, Javed Z, Sadia H, Qureshi IA, Irshad A, Ahmed R, et al. Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future. Cancer Cell Int. 2021;21:270.
Hunter B, Hindocha S, Lee RW. The role of artificial intelligence in early cancer diagnosis. Cancers. 2022;14:1524.
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115–8.
Chen RJ, Lu MY, Williamson DFK, Chen TY, Lipkova J, Noor Z, et al. Pan-cancer integrative histology-genomic analysis via multimodal deep learning. Cancer Cell. 2022;40:865–878.e866.
Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8–17.
Walter W, Haferlach C, Nadarajah N, Schmidts I, Kühn C, Kern W, et al. How artificial intelligence might disrupt diagnostics in hematology in the near future. Oncogene. 2021;40:4271–80.
El Naqa I, Li R, Murphy MJ, editors. Machine learning in radiation oncology: theory and application. Springer International Publishing: Switzerland; 2015.
Huynh E, Hosny A, Guthier C, Bitterman DS, Petit SF, Haas-Kogan DA, et al. Artificial intelligence in radiation oncology. Nat Rev Clin Oncol. 2020;17:771–81.
Feng M, Valdes G, Dixit N, Solberg TD. Machine learning in radiation oncology: opportunities, requirements, and needs. Front Oncol. 2018;8:110.
Malta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L, Weinstein JN, et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell. 2018;173:338–354.e315.
Li Q, Ren Z, Cao K, Li MM, Wang K, Zhou Y. CancerVar: an artificial intelligence–empowered platform for clinical interpretation of somatic mutations in cancer. Sci Adv. 2022;8:eabj1624.
Biswas N, Chakrabarti S. Artificial Intelligence (AI)-based systems biology approaches in multi-omics data analysis of cancer. Front Oncol. 2020; 10:588221.
Cui S, Ten Haken RK, El Naqa I. Integrating multiomics information in deep learning architectures for joint actuarial outcome prediction in non-small cell lung cancer patients after radiation therapy. Int J Radiat Oncol Biol Phys. 2021;110:893–904.
Luo R, Sun L, Xia Y, Qin T, Zhang S, Poon H et al. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief Bioinform. 2022;23:bbac409.
Yu VL, Fagan LM, Wraith SM, Clancey WJ, Scott AC, Hannigan J, et al. Antimicrobial selection by a computer: a blinded evaluation by infectious diseases experts. JAMA. 1979;242:1279–82.
Schwartz WB. Medicine and the computer. N Engl J Med. 1970;283:1257–64.
Chen JH, Asch SM. Machine learning and prediction in medicine - beyond the peak of inflated expectations. N Engl J Med. 2017;376:2507–9.
Matheny ME, Whicher D, Thadaney, Israni S. Artificial intelligence in health care: a report from the national academy of medicine. JAMA. 2020;323:509–10.
Cui S, Hope A, Dilling TJ, Dawson LA, Ten Haken R, El, et al. Artificial intelligence for outcome modeling in radiotherapy. Semin Radiat Oncol. 2022;32:351–64.
Niraula D, Cui S, Pakela J, Wei L, Luo Y, Ten Haken RK, et al. Current status and future developments in predicting outcomes in radiation oncology. Br J Radio. 2022;95:20220239.
Liu M, Fang S, Dong H, Xu C. Review of digital twin about concepts, technologies, and industrial applications. J Manuf Syst. 2021;58:346–61.
Venkatesh KP, Raza MM, Kvedar JC. Health digital twins as tools for precision medicine: considerations for computation, implementation, and regulation. NPJ Digit Med. 2022;5:150.
El Naqa I. Murphy MJ (eds). Machine and deep learning in oncology, medical physics and radiology. 2nd edn. Switzerland: Springer Nature; 2022.
Denny JC, Collins FS. Precision medicine in 2030-seven ways to transform healthcare. Cell. 2021;184:1415–9.
Carter LD, Liu D, Cantrell C. Exploring the intersection of the digital divide and artificial intelligence: a hermeneutic literature review. AIS Trans Hum-Comput Interact. 2020;12:253–75.
Reddy H, Joshi S, Joshi A, Wagh V. A critical review of global digital divide and the role of technology in healthcare. Cureus. 2022;14:e29739.
Masic I, Miokovic M, Muhamedagic B. Evidence based medicine—new approaches and challenges. Acta Inform Med. 2008;16:219–25.
Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med. 2015;13:1.
El Naqa I, Li H, Fuhrman J, Hu Q, Gorre N, Chen W, et al. Lessons learned in transitioning to AI in the medical imaging of COVID-19. J Med Imaging. 2021;8:010902–010902.
Collins GS, Dhiman P, Andaur Navarro CL, Ma J, Hooft L, Reitsma JB, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021;11:e048008.
Mongan J, Moy L, Kahn CE. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell. 2020;2:e200029.
Norgeot B, Quer G, Beaulieu-Jones BK, Torkamani A, Dias R, Gianfrancesco M, et al. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat Med. 2020;26:1320–4.
Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, et al. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ. 2022;377:e070904.
El Naqa I, Boone JM, Benedict SH, Goodsitt MM, Chan HP, Drukker K, et al. AI in medical physics: guidelines for publication. Med Phys. 2021;48:4711–4.
Cruz Rivera S, Liu X, Chan A-W, Denniston AK, Calvert MJ, Ashrafian H, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Lancet Digit Health. 2020;2:e549–e560.
McIntosh C, Conroy L, Tjong MC, Craig T, Bayley A, Catton C, et al. Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer. Nat Med. 2021;27:999–1005.
El Naqa I. Prospective clinical deployment of machine learning in radiation oncology. Nat Rev Clin Oncol. 2021;18:605–6.
Gama F, Tyskbo D, Nygren J, Barlow J, Reed J, Svedberg P. Implementation frameworks for artificial intelligence translation into health care practice: scoping review. J Med Internet Res. 2022;24:e32215.
Madai VI, Higgins DC. Artificial Intelligence in healthcare: lost in translation? 2021.
Mechelli A, Vieira S. From models to tools: clinical translation of machine learning studies in psychosis. NPJ Schizophr. 2020;6:4.
El Naqa I. Perspectives on making big data analytics work for oncology. Methods. 2016;111:32–44.
Lin D, Lin H. Translating artificial intelligence into clinical practice. Ann Transl Med. 2020;8:715.
Sendak MP, D’Arcy J, Kashyap S, Gao M, Nichols M, Corey KM et al. A path for translation of machine learning products into healthcare delivery. EMJ Innov. 2020;10:19–00172.
Strickland E. IBM Watson, heal thyself: how IBM overpromised and underdelivered on AI health care. IEEE Spectr. 2019;56:24–31.
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:195.
Tadavarthi Y, Makeeva V, Wagstaff W, Zhan H, Podlasek A, Bhatia N, et al. Overview of noninterpretive artificial intelligence models for safety, quality, workflow, and education applications in radiology practice. Radiol Artif Intell. 2022;4:e210114.
Prevedello LM, Erdal BS, Ryu JL, Little KJ, Demirer M, Qian S, et al. Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology. 2017;285:923–31.
Leal JP, Rowe SP, Stearns V, Connolly RM, Vaklavas C, Liu MC, et al. Automated lesion detection of breast cancer in [(18)F] FDG PET/CT using a novel AI-Based workflow. Front Oncol. 2022;12:1007874.
Do HM, Spear LG, Nikpanah M, Mirmomen SM, Machado LB, Toscano AP, et al. Augmented radiologist workflow improves report value and saves time: a potential model for implementation of artificial intelligence. Acad Radio. 2020;27:96–105.
Folio LR, Choi MM, Solomon JM, Schaub NP. Automated registration, segmentation, and measurement of metastatic melanoma tumors in serial CT scans. Acad Radio. 2013;20:604–13.
Xu J, Greenspan H, Napel S, Rubin DL. Automated temporal tracking and segmentation of lymphoma on serial CT examinations. Med Phys. 2011;38:5879–86.
Ben-Cohen A, Klang E, Diamant I, Rozendorn N, Amitai MM, Greenspan H. Automated method for detection and segmentation of liver metastatic lesions in follow-up CT examinations. J Med Imaging. 2015;2:034502.
Cusumano D, Boldrini L, Dhont J, Fiorino C, Green O, Güngör G, et al. Artificial intelligence in magnetic resonance guided radiotherapy: medical and physical considerations on state of art and future perspectives. Phys Med. 2021;85:175–91.
Tarhini A, Kudchadkar RR. Predictive and on-treatment monitoring biomarkers in advanced melanoma: moving toward personalized medicine. Cancer Treat Rev. 2018;71:8–18.
Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, et al. IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Investig. 2017;127:2930–40.
Fehrenbacher L, Spira A, Ballinger M, Kowanetz M, Vansteenkiste J, Mazieres J, et al. Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): a multicentre, open-label, phase 2 randomised controlled trial. Lancet. 2016;387:1837–46.
Bolen CR, McCord R, Huet S, Frampton GM, Bourgon R, Jardin F, et al. Mutation load and an effector T-cell gene signature may distinguish immunologically distinct and clinically relevant lymphoma subsets. Blood Adv. 2017;1:1884–90.
Coppola D, Nebozhyn M, Khalil F, Dai H, Yeatman T, Loboda A, et al. Unique ectopic lymph node-like structures present in human primary colorectal carcinoma are identified by immune gene array profiling. Am J Pathol. 2011;179:37–45.
Messina JL, Fenstermacher DA, Eschrich S, Qu X, Berglund AE, Lloyd MC, et al. 12-Chemokine gene signature identifies lymph node-like structures in melanoma: potential for patient selection for immunotherapy? Sci Rep. 2012;2:765.
Liu D, Lin JR, Robitschek EJ, Kasumova GG, Heyde A, Shi A, et al. Evolution of delayed resistance to immunotherapy in a melanoma responder. Nat Med. 2021;27:985–92.
Yarchoan M, Hopkins A, Jaffee EM. Tumor mutational burden and response rate to PD-1 inhibition. N Engl J Med. 2017;377:2500–1.
Davoli T, Uno H, Wooten EC, Elledge SJ. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science. 2017;355:eaaf8399.
McGranahan N, Furness AJ, Rosenthal R, Ramskov S, Lyngaa R, Saini SK, et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science. 2016;351:1463–9.
Iafolla MAJ, Yang C, Chandran V, Pintilie M, Li Q, Bedard PL, et al. Predicting toxicity and response to pembrolizumab through germline genomic HLA Class 1 analysis. JNCI Cancer Spectr. 2021;5:pkaa115.
Postow MA, Manuel M, Wong P, Yuan J, Dong Z, Liu C, et al. Peripheral T cell receptor diversity is associated with clinical outcomes following ipilimumab treatment in metastatic melanoma. J Immunother Cancer. 2015;3:23.
Sayaman RW, Saad M, Thorsson V, Hu D, Hendrickx W, Roelands J, et al. Germline genetic contribution to the immune landscape of cancer. Immunity. 2021;54:367–86.e368.
Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021;26:80–93.
Park Y, Jackson GP, Foreman MA, Gruen D, Hu J, Das AK. Evaluating artificial intelligence in medicine: phases of clinical research. JAMIA Open. 2020;3:326–31.
Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18:463–77.
Sun D, Gao W, Hu H, Zhou S. Why 90% of clinical drug development fails and how to improve it? Acta Pharm Sin B. 2022;12:3049–62.
Dowden H, Munro J. Trends in clinical success rates and therapeutic focus. Nat Rev Drug Discov. 2019;18:495–6.
Özçelik R, van Tilborg D, Jiménez-Luna J, Grisoni F. Structure-Based Drug Discovery with Deep Learning. Chembiochem. 2023;24:e202200776. https://doi.org/10.1002/cbic.202200776.
Jayatunga MKP, Xie W, Ruder L, Schulze U, Meier C. AI in small-molecule drug discovery: a coming wave? Nat Rev Drug Discov. 2022;21:175–6.
Noé F, Tkatchenko A, Müller K-R, Clementi C. Machine learning for molecular simulation. Annu Rev Phys Chem. 2020;71:361–90.
Costello JC, Heiser LM, Georgii E, Gönen M, Menden MP, Wang NJ, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol. 2014;32:1202–12.
Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A’Court C, et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res. 2017;19:e367.
Liu Y, Ling Z, Huo B, Wang B, Chen T, Mouine E. Building a platform for machine learning operations from open source frameworks. IFAC-PapersOnLine. 2020;53:704–9.
Giordano C, Brennan M, Mohamed B, Rashidi P, Modave F, Tighe P. Accessing artificial intelligence for clinical decision-making. Front Digit Health. 2021;3:645232.
Marcus G. Deep learning: a critical appraisal. 2018. https://doi.org/10.48550/arXiv.1801.00631.
Luo Y, Cuneo KC, Lawrence TS, Matuszak MM, Dawson LA, Niraula D, et al. A human-in-the-loop based Bayesian network approach to improve imbalanced radiation outcomes prediction for hepatocellular cancer patients with stereotactic body radiotherapy. Front Oncol. 2022;12:1061024. https://doi.org/10.3389/fonc.2022.1061024.
El Naqa I, Kosorok MR, Jin J, Mierzwa M, Ten Haken RK. Prospects and challenges for clinical decision support in the era of big data. JCO Clin Cancer Inf. 2018;2:CCI.18.00002.
Lambin P, Roelofs E, Reymen B, Velazquez ER, Buijsen J, Zegers CML, et al. ‘Rapid Learning health care in oncology’—an approach towards decision support systems enabling customised radiotherapy’. Radiother Oncol. 2013;109:159–64.
Lambin P, Zindler J, Vanneste B, van de Voorde L, Jacobs M, Eekers D, et al. Modern clinical research: how rapid learning health care and cohort multiple randomised clinical trials complement traditional evidence based medicine. Acta Oncol. 2015;54:1289–1300.
Rieke N, Hancox J, Li W, Milletarì F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3:119.
Zerka F, Barakat S, Walsh S, Bogowicz M, Leijenaar RTH, Jochems A, et al. Systematic review of privacy-preserving distributed machine learning from federated databases in health care. JCO Clin Cancer Inform. 2020;4:184–200.
Jochems A, Deist TM, El Naqa I, Kessler M, Mayo C, Reeves J, et al. Developing and validating a survival prediction model for NSCLC patients through distributed learning across 3 countries. Int J Radiat Oncol Biol Phys. 2017;99:344–52.
Jochems A, El-Naqa I, Kessler M, Mayo CS, Jolly S, Matuszak M, et al. A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy. Acta Oncol. 2018;57:226–30.
Luo Y, Tseng HH, Cui S, Wei L, Ten Haken RK, El Naqa I. Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling. BJR Open. 2019;1:20190021.
Fuhrman JD, Gorre N, Hu Q, Li H, El Naqa I, Giger ML. A review of explainable and interpretable AI with applications in COVID-19 imaging. Med Phys. 2022;49:1–14.
(FDA) FaDA. Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)- based software as a medical device (SaMD). Food and Drug Administration, 2019.
FDA. Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD) - discussion paper and request for feedback, 2021.
The National AI Initiative Act 2020.
Bleher H, Braun M. Diffused responsibility: attributions of responsibility in the use of AI-driven clinical decision support systems. AI Ethics. 2022;2:747–61.
Holzinger A, Biemann C, Pattichis CS, Kell DB. What do we need to build explainable AI systems for the medical domain? 2017. https://doi.org/10.48550/arXiv.1712.09923.
Keane PA, Topol EJ. With an eye to AI and autonomous diagnosis. NPJ Digit Med. 2018;1:40.
Wang D, Khosla A, Gargeya R, Irshad H, Beck AH. Deep learning for identifying metastatic breast cancer. 2016. https://doi.org/10.48550/arXiv.1606.05718.
Wu X, Xiao L, Sun Y, Zhang J, Ma T, He L. A survey of human-in-the-loop for machine learning. Future Gener Comput Syst. 2022;135:364–81.
Ghai B, Mueller K. D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias. IEEE Trans Vis Comput Graph. 2023;29:473–82.
Vasey B, Ursprung S, Beddoe B, Taylor EH, Marlow N, Bilbro N, et al. Association of clinician diagnostic performance with machine learning–based decision support systems: a systematic review. JAMA Netw Open. 2021;4:e211276–e211276.
Freeman K, Geppert J, Stinton C, Todkill D, Johnson S, Clarke A, et al. Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy. BMJ. 2021;374:n1872.
Brocklehurst P, Field D, Greene K, Juszczak E, Keith R, Kenyon S, et al. Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. Lancet. 2017;389:1719–29.
Dalkey N, Helmer O. An experimental application of the DELPHI method to the use of experts. Manag Sci. 1963;9:458–67.
Rowe G, Wright G. Expert opinions in forecasting: the role of the Delphi technique. In: Armstrong JS (ed). Principles of forecasting: a handbook for researchers and practitioners. US: Boston, MA: Springer, 2001, p. 125–44.
Biswas S, Corti L, Buijsman S, Yang J. CHIME: Causal Human-in-the-Loop Model Explanations. Proc AAAI Conf Hum Comput Crowdsourcing. 2022;10:27–39.
Smilowitz JB, Das IJ, Feygelman V, Fraass BA, Kry SF, Marshall IR, et al. AAPM medical physics practice guideline 5.a.: commissioning and qa of treatment planning dose calculations—megavoltage photon and electron beams. J Appl Clin Med Phys. 2015;16:14–34. https://doi.org/10.1120/jacmp.v16i5.5768.
El Naqa I, Moran, Jean M, Ten Haken RK. Machine learning in radiation oncology: what have we learned so far? In: van Dyk J (ed). The modern technology of radiation oncology (Volume 4) Madison, WI, USA: Medical Physics Publishing, 2020.
This work was partly supported by National Institute of Health (NIH) grant R01-CA233487 and its supplement (CA233487-05S1). IEN and KP would like to acknowledge support from the Center for Advanced Studies at Ludwig-Maximilians-Universität München (CAS LMU fellowship).
IEN is on the scientific advisory of Endectra, LLC., act as deputy editor for the journal of Medical Physics and receives funding from NIH and DoD. DER is on the Board of Directors for NanoString technologies, Inc. LRF Research agreement: Philips Healthcare (ended Aug 2021). Patents (no royalties since NIH and military owned). Author royalties, Springer. AAT reports contracted research grants with institution from Bristol Myers Squib, Genentech-Roche, Regeneron, Sanofi-Genzyme, Nektar, Clinigen, Merck, Acrotech, Pfizer, Checkmate, OncoSec; personal consultant/advisory board fees from Bristol Myers Squibb, Merck, Easai, Instil Bio Clinigin, Regeneron, Sanofi-Genzyme, Novartis, Partner Therapeutics, Genentech/Roche, BioNTech, Concert AI, AstraZeneca outside the submitted work.
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El Naqa, I., Karolak, A., Luo, Y. et al. Translation of AI into oncology clinical practice. Oncogene 42, 3089–3097 (2023). https://doi.org/10.1038/s41388-023-02826-z