Artificial Intelligence and Decision-Making in Oncology: A Review of Ethical, Legal, and Informed Consent Challenges
Open accessAbstract
Purpose of Review: Artificial Intelligence (AI) integration in oncology is transforming therapeutic decision-making by providing clinical decision support. AI may improve treatment precision, but it raises ethical, legal, and informed consent issues. This review examines these paramount AI implementation issues in cancer care. This systematic review followed the PRISMA 2020 guidelines and was prospectively registered in the PROSPERO (CRD420251046482) database. A comprehensive literature search was conducted in PubMed, Embase, and the Cochrane CENTRAL Library to identify studies published between January 2015 and May 2025. AI-supported oncology therapeutic decision-making studies with ethical, legal, or informed consent implications were eligible. Recent Findings: Fifteen studies met the inclusion criteria. AI applications were found to support treatment recommendations, personalize drug dosing, and improve adherence and patient management. Despite these benefits, the review highlighted key concerns, including algorithmic transparency, unclear accountability in AI-guided decisions, data privacy, and gaps in patient understanding of AI’s role in their care. Summary: AI has the potential to enhance oncological care, but ethical and legal issues must be addressed for safe and equitable implementation. Emphasis should be placed on developing robust informed consent models, mitigating algorithmic bias, and establishing clear legal accountability. Future research must establish ethical frameworks and regulatory mechanisms to protect patient autonomy and responsibly integrate AI into oncology.
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AI tools may enhance risk stratification by integrating clinical, molecular, and imaging data to generate individualized estimates of recurrence, treatment response, and survival.
“AI tools may enhance risk stratification by integrating clinical, molecular, and imaging data to generate individualized estimates of recurrence, treatment response, and survival.14”
From Artificial Intelligence in Oncology: Practical Applications Across Clinical Care, Scholarship, and Translation. (2026) · cites this paper as [14]
The Discussion (page unavailable) says AI may analyze genomic, imaging, and clinical data to personalize oncology decisions. The Results describe cancer-risk classification, and the Methods table reports mortality prediction. Our copy does not establish that combining those data produces individualized estimates of recurrence, treatment response, and survival as claimed. (LLM, conf=0.86)
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Cites
Revolutionizing healthcare: the role of artificial intelligence in clinical practice
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., et al. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04698-z · Cited by 3,146 · see citing works
Ethical and legal challenges of artificial intelligence-driven healthcare
Gerke, S., Minssen, T., & Cohen, G. (2020) · Artificial Intelligence in Healthcare · doi:10.1016/b978-0-12-818438-7.00012-5 · Cited by 1,170 · see citing works
Computer knows best? The need for value-flexibility in medical AI
McDougall, R. J. (2019) · Journal of Medical Ethics · doi:10.1136/medethics-2018-105118 · Cited by 262 · see citing works
PROSPERO at one year: an evaluation of its utility
Booth, A., Clarke, M. J., Dooley, G., Ghersi, D., Moher, D., Petticrew, M. P., & Stewart, L. A. (2013) · Systematic Reviews · doi:10.1186/2046-4053-2-4 · Cited by 187 · see citing works
Artificial intelligence in oncology: path to implementation
Chua, I. S., Gaziel‐Yablowitz, M., Korach, Z. T., Kehl, K. L., Levitan, N. A., Arriaga, Y. E., Jackson, G. P., Bates, D. W., & Hassett, M. (2021) · Cancer Medicine · doi:10.1002/cam4.3935 · Cited by 179 · see citing works
Informed Consent and Medical Artificial Intelligence: What to Tell the Patient?
Cohen, I. G. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3529576 · Cited by 165 · see citing works
Woodman, R. J., & Mangoni, A. A. (2023) · Aging Clinical and Experimental Research · doi:10.1007/s40520-023-02552-2 · Cited by 142 · see citing works
Artificial intelligence in clinical decision support systems for oncology
Wang, L., Chen, X., Zhang, L., Li, L., Huang, Y., Sun, Y., & Yuan, X. (2022) · International Journal of Medical Sciences · doi:10.7150/ijms.77205 · Cited by 115 · see citing works
The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review
Istasy, P., Lee, W. S., Iansavitchene, A., Upshur, R. E. G., Gyawali, B., Burkell, J., Sadiković, B., Lazo‐Langner, A., & Chin-Yee, B. H. (2022) · Journal of Medical Internet Research · doi:10.2196/39748 · Cited by 85 · see citing works
Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions
Huhulea, E. N., Huang, L., Eng, S., Sumawi, B., Huang, A., Aifuwa, E., Hirani, R., Tiwari, R. K., & Etienne, M. (2025) · Biomedicines · doi:10.3390/biomedicines13040951 · Cited by 74 · see citing works
Hantel, A., Walsh, T. P., Marron, J. M., Kehl, K. L., Sharp, R. R., Allen, E. M. V., & Abel, G. A. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.4077 · Cited by 58 · see citing works
Verlingue, L., Boyer, C., Olgiati, L., Brutti-Mairesse, C., Morel, D., & Blay, J. (2024) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2024.101064 · Cited by 50 · see citing works
Far, B. F. (2023) · Exploration of Targeted Anti-tumor Therapy · doi:10.37349/etat.2023.00160 · Cited by 42 · see citing works
Tan, B. K. J., Teo, C. B., Tadeo, X., Peng, S., Soh, H. P. L., Du, S. D. X., Luo, V. W. Y., Bandla, A., Sundar, R., Ho, D., et al. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.635524 · Cited by 38 · see citing works
Parikh, R. B., Manz, C. R., Nelson, M. N., Evans, C. N., Regli, S. H., O’Connor, N. R., Schuchter, L. M., Shulman, L. N., Patel, M. S., Paladino, J., et al. (2022) · Supportive Care in Cancer · doi:10.1007/s00520-021-06774-w · Cited by 37 · see citing works
ChatGPT's Gastrointestinal Tumor Board Tango: A limping dance partner?
Aghamaliyev, U., Karimbayli, J., Gießen-Jung, C., Ilmer, M., Unger, K., Andrade, D., Hofmann, F. O., Weniger, M., Angele, M. K., Westphalen, C. B., et al. (2024) · European Journal of Cancer · doi:10.1016/j.ejca.2024.114100 · Cited by 34 · see citing works
Cervical cancer risk prediction with robust ensemble and explainable black boxes method
Curia, F. (2021) · Health and Technology · doi:10.1007/s12553-021-00554-6 · Cited by 31 · see citing works
Duwe, G., Mercier, D., Wiesmann, C., Kauth, V., Moench, K., Junker, M., Neumann, C., Haferkamp, A., Dengel, A., & Höfner, T. (2024) · Cancer Medicine · doi:10.1002/cam4.7398 · Cited by 31 · see citing works
The Use of Artificial Intelligence in Clinical Care: A Values-Based Guide for Shared Decision Making
Macri, R., & Roberts, S. L. (2023) · Current Oncology · doi:10.3390/curroncol30020168 · Cited by 29 · see citing works
Lococo, F., Boldrini, L., Charles-Davies, D., Evangelista, J., Nero, C., Flamini, S., Minucci, A., Paolis, E. D., Vita, E., Cesario, A., et al. (2023) · BMC Cancer · doi:10.1186/s12885-023-10997-x · Cited by 29 · see citing works
Artificial intelligence supporting cancer patients across Europe—The ASCAPE project
Tzelves, L. I., Manolitsis, I. S., VARKARAKIS, I. M., Ivanović, M. D., Kokkonidis, M., Useros, C. S., Kosmidis, T., Muñoz-Mateu, M., Grau, I., Athanatos, M., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0265127 · Cited by 27 · see citing works
Quality of ChatGPT-Generated Therapy Recommendations for Breast Cancer Treatment in Gynecology
Stalp, J. L., Denecke, A., Jentschke, M., Hillemanns, P., & Klapdor, R. (2024) · Current Oncology · doi:10.3390/curroncol31070284 · Cited by 26 · see citing works
Novel method for predicting nonvisible symptoms using machine learning in cancer palliative care
Shimada, K., & Tsuneto, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-39119-0 · Cited by 18 · see citing works
Masiero, M. A., Spada, G. E., Sanchini, V., Munzone, E., Pietrobon, R. S., Teixeira, L., Valencia, M., Machiavelli, A., Fragale, E., Pezzolato, M., et al. (2023) · JMIR Research Protocols · doi:10.2196/48852 · Cited by 17 · see citing works
Janbain, A., Farolfi, A., Arnoux, A., Romengas, L., Scharl, S., Fanti, S., Serani, F., Peeken, J. C., Katsahian, S., Strouthos, I., et al. (2024) · JMIR Cancer · doi:10.2196/60323 · Cited by 16 · see citing works
Li, M., Xiong, X., Xu, B., & Dickson, C. (2024) · JMIR Formative Research · doi:10.2196/53918 · Cited by 15 · see citing works
AI-Generated Content in Cancer Symptom Management: A Comparative Analysis Between ChatGPT and NCCN
Lazris, D., Schenker, Y., & Thomas, T. H. (2024) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2024.06.019 · Cited by 14 · see citing works
Artificial Intelligence in Cancer Care: Legal and Regulatory Dimensions
Gowda, V., Kwaramba, T., Hanemann, C. W., Garcia, J. A., & Barata, P. C. (2021) · The Oncologist · doi:10.1002/onco.13862 · Cited by 13 · see citing works
Albalawi, A. F., Yassen, M. H., Almuraydhi, K. M., Althobaiti, A. D., Alzahrani, H. H., & Alqahtani, K. M. (2024) · Journal of Healthcare Sciences · doi:10.52533/johs.2024.41241 · Cited by 11 · see citing works
Hesjedal, M. B., Lysø, E. H., Solbjør, M., & Skolbekken, J. (2024) · Sociology of Health & Illness · doi:10.1111/1467-9566.13818 · Cited by 10 · see citing works
Rinderknecht, E., von Winning, D., Kravchuk, A. P., Schäfer, C., Schnabel, M. J., Siepmann, S., Mayr, R., Grassinger, J., Goßler, C., Pöhl, F., et al. (2024) · Current Oncology · doi:10.3390/curroncol31110520 · Cited by 10 · see citing works
Ng, S. S. T., Oehring, R., Ramasetti, N., Roller, R., Thomas, P. E., Chen, Y., Moosburner, S., Winter, A., Maurer, M. M., Auer, T. A., et al. (2023) · Trials · doi:10.1186/s13063-023-07610-8 · Cited by 9 · see citing works
Informed Consent for the Use of AI in the Process of Providing Medical Services
Wałdoch, K. (2024) · Review of European and Comparative Law · doi:10.31743/recl.17239 · Cited by 7 · see citing works
Awuah, W. A., Aderinto, N., Poornaselvan, J., Tan, J. K., Shah, M. H., Ashinze, P. T., Pujari, A. G., Bharadwaj, H. R., Abdul‐Rahman, T., & Atallah, O. (2024) · Health Science Reports · doi:10.1002/hsr2.2268 · Cited by 7 · see citing works
Hantel, A., Marron, J. M., Kehl, K. L., Sharp, R. R., Walsh, T. P., Allen, E. M. V., & Abel, G. A. (2023) · Blood · doi:10.1182/blood-2023-175030 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Cao, Y., Deng, L., Liu, X., Feng, Z., & Gao, Y. (2025) · BMC Medical Ethics · doi:10.1186/s12910-025-01308-z · Cited by 27 · see citing works
Martinho, D., Sobreiro, P., Domingues, A., Martinho, F. I. F., & Nogueira, N. F. R. (2026) · Healthcare · doi:10.3390/healthcare14030287 · Cited by 14 · see citing works
Chamouni, G., Lococo, F., Sassorossi, C., Atuhaire, N. C., Ádány, R., & Varga, O. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1663298 · Cited by 13 · see citing works
Review on artificial intelligence supported breast cancer diagnosis for effective treatment planning
Narayanan, M., Prabhu, N., & Rajinikanth, V. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07920-3 · Cited by 6 · see citing works
Alavi, S. E., Sharma, L. A., Sharma, A. K., & Shahmabadi, H. E. (2025) · Clinical & Translational Oncology · doi:10.1007/s12094-025-04120-8 · Cited by 3 · see citing works
CAR‐T Cells: Current Status, Challenges, and Future Prospects
Adly, A. S., Cartron, G., Adly, A. S., Egea, J., Dutilleul, P. C., Adly, M. S., & Villalba, M. (2026) · MedComm · doi:10.1002/mco2.70606 · Cited by 4 · see citing works
Froicu, E.-M., Onicescu, O.-M., Creangă-Murariu, I., Dascalu, C., Gafton, B., Afrăsânie, V.-A., Alexa‐Stratulat, T., Marinca, M. V., Pușcașu, D.-M., Miron, L., et al. (2025) · Medicina · doi:10.3390/medicina61101741 · Cited by 3 · see citing works
Self-citationImpact of Artificial Intelligence on the Care of Terminally Ill Patients
Gonçalves, F., Gaudêncio, M., Nunes, S. B., Rêgo, F. M. P. D. S., & Nunes, R. (2026) · Healthcare · doi:10.3390/healthcare14050602 · Cited by 2 · see citing works
Adapa, K., & Jayakumar, N. (2026) · Asia-Pacific Journal of Oncology Nursing · doi:10.1016/j.apjon.2026.100964 · Cited by 2 · see citing works
Translational gaps in immuno-AI: From algorithmic accuracy to clinical trust
Oisakede, E. O., Daniel, R. I. A., Olawuyi, O. F., Alabi, J. O., Analikwu, C. C., & Olawade, D. B. (2026) · Human Immunology · doi:10.1016/j.humimm.2026.111774 · Cited by 2 · see citing works
Amoozgar, B., Bangolo, A., Thor, D. C., Rajanna, S., Abdelwahab, S., Mohamed, A. S., Mansour, C., & Ehsanullah, S. U. M. (2026) · Cancers · doi:10.3390/cancers18162598 · Cited by 2 · see citing works
Hundal, J., Veettil, A. A. V., Chung, C., & Hosseini, M. (2026) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-26-517280 · Cited by 1 · see citing works
Fatima, S., Faisal, S., Batool, S. V., Faisal, M., Aamir, T., Shahid, Z. U. A., & Mahato, R. K. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1673586 · Cited by 2 · see citing works
Decoding Uncertainty Quantification for Oncology—An Illustration Using Radiomics
van Daalen, F., Sasidharan, B. K., Praveenraj, C., Varghese, A. J., Dekker, A. L. A. J., Wee, L., Fijten, R., Irodi, A., & Thomas, H. M. T. (2026) · Diagnostics · doi:10.3390/diagnostics16050700 · Cited by 1 · see citing works
Budin‐Ljøsne, I., Boni, A., Boussemart, L., Fattal, A.-K., Frantzi, M., Gemenick, L., Koinis, F., Kotsakis, A. P., Östman, F., Roret, T., et al. (2026) · Journal of Cancer Policy · doi:10.1016/j.jcpo.2026.100803 · Cited by 1 · see citing works
Ethical challenges to patient autonomy in the era of artificial intelligence: a systematic review
Mohammadnejad, S., Raiesifar, A., Bazmi, S., & Ghonodi, F. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03614-x · Cited by 1 · see citing works
Wang, D., & Li, Y. (2026) · Seminars in Oncology · doi:10.1016/j.seminoncol.2026.152516 · Cited by 1 · see citing works
Khoury, G. E., Zacharias, L., & Nabhani‐Gebara, S. (2026) · Ethics Medicine and Public Health · doi:10.1016/j.jemep.2026.101247
The Role of Molecular Profiling in Achieving Guideline-Concordant Care—From Sequencing to Survival
Makhlin, I. (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2026.5596
Masood, F. (2026) · Journal of Psychosocial Oncology · doi:10.1080/07347332.2026.2649035
Senguttuvan, K., Kumar, K. K. S., Parameswaran, P., & Kumar, C. S. C. S. (2026) · Journal of Maxillofacial and Oral Surgery · doi:10.1007/s12663-026-02974-1
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Goyal, L., Chung, C. D., Mori, Y., Hall, W. A., Kehl, K. L., & Feng, M. (2026) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-26-515754
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Duranti, S., Iacobelli, V., Trozzi, R., Camarda, F., Panfili, A., Fagotti, A., Fanfani, F., Marchetti, C., & Nero, C. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1811964
Zarbá, M., O’Sullivan, D., Maj, D., Cheung, W. Y., Ludwig, L., Wells, C., Ferrier, E., Chehade, R. E. H., Donskov, F. N., Eid, M., et al. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1827686
Revolutionizing Cancer Care: Next-Gen Engineering and Policy Synergy for a Cure-Driven Future
Mohamed, H. M., Khalifa, M., Toumi, A., & Ali, M. (2025) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa68193.2025.11498858
Velasco, Y. L. C., Masaquiza, M. M. G., & GARCÍA, C. A. G. (2026) · Esprint Investigación · doi:10.61347/ei.v5i1.275
García, A. M., & Rodríguez, S. F. (2026) · European Journal of Hospital Pharmacy · doi:10.1136/ejhpharm-2026-005154
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Farooq, F., Qadri, R. A., Ji, A., Qayoom, H., & Mir, M. F. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-49109-2.00008-0
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Ignarska, J., `Mzabi, S. K., Piech, B., Judzińska, A., Sobczyńska, K., Terbosh, M., Trojanowska, E., Dubaj, M., Kwolek, M., & Osipowska, J. (2026) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.2(50).2026.5599
Divekar, G., & Bhosale, B. (2026) · Indian Journal of Medical and Paediatric Oncology · doi:10.1055/s-0046-1824589
Jung, M. (2026) · Journal of Preventive Medicine and Public Health · doi:10.3961/jpmph.26.094
Hannarici, Z., & Turhan, A. (2026) · Anatolian Current Medical Journal · doi:10.38053/acmj.1943411
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Thakkar, A., Bhattu, B. K., & Desai, C. (2026) · Cureus · doi:10.7759/cureus.113774
AI adoption among French oncology pharmacists: A national survey
Wakil, A., Chevrier, R., Tournamille, J., & Pourroy, B. (2026) · Journal of Oncology Pharmacy Practice · doi:10.1177/10781552261485826 · Cited by 1 · see citing works
Koliou, N., Kourtidis, L., Lykoudis, P. M., Karavolias, I., Charalampous, S., Ayiomamitis, G. D., Kouki, P., & Iordanou, C. (2026) · Healthcare · doi:10.3390/healthcare14193331
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Farhadzadeh, F., Borji, A., Aram, C., Alishvandi, A., Kamali, M. J., & Rahimi, M. (2026) · iScience · doi:10.1016/j.isci.2026.117255
Hey, G. E., Sesto, M., Still, M., Patel, G., Crlenjak, N., Dagra, A., Wu, Y., Guo, Y., & Ghiaseddin, A. P. (2026) · Journal of Neuro-Oncology · doi:10.1007/s11060-026-05774-2
López-Gómez, M., Corrales-García, E.-M., Gómez-Raposo, C., Ruiz‐Casado, A., Casado-Sáenz, E., & Delgado-López, P.-D. (2026) · Clinical & Translational Oncology · doi:10.1007/s12094-026-04591-3
- Authors
- Froicu, E.-M., Creangă-Murariu, I., Afrăsânie, V.-A., Gafton, B., Alexa-Stratulat, T., Miron, L., Pușcașu, D. M., Poroch, V., Bacoanu, G., Radu, I., & Marinca, M.-V.
- Affiliations
- Grigore T. Popa University of Medicine and Pharmacy (RO)
- Semmelweis University (HU)
- Corresponding author
- Creangă-Murariu, Ioana
- ORCID
- Venue
- Current Oncology Reports
- Published
- 2025-06-17
- Volume
- 27
- Issue
- 8
- Pages
- 1002-1012
- ISSN
- 1523-3790 (ISSN-L)
- 1534-6269
- PMC
- PMC12423120
- OpenAlex
- W4411392123
- Cited by
- 38
- References
- 35
- Citations by year
- 2026: 31 · 2025: 7
- FWCI
- 2.25
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Current Oncology Reports (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- artificial intelligence in oncology, clinical decision support, treatment decision-making, informed consent, algorithmic transparency, legal accountability, data privacy, algorithmic bias, individualized dosing, medication adherence, patient autonomy, AI ethics, regulatory framework
- MeSH terms
- Artificial Intelligence, Decision Making, Humans, Informed Consent, Medical Oncology, Neoplasms
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