The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Are medical oncologists ready for the artificial intelligence revolution? Evaluation of the opinions, knowledge, and experiences of medical oncologists about artificial intelligence technologies
Abstract
The use of artificial intelligence technologies (AIT) in medicine is increasing worldwide. In this study, it was aimed to evaluate the experiences, opinions, and future expectations of medical oncologists on artificial intelligence (AI). After the reliability and validity analyses were carried out by a pilot study, the main online questionnaire was sent to the members of the "Turkish Society of Medical Oncology" mail group by an invitation e-mail. The anonymized responses of the participants were analyzed. The median age of the 156 participants was 36 (34-43) years and half (51%) were male. Most (45%) were fellows. Forty-six percent were working in university hospitals, 56% were visiting 20-40 patients a day. Medical oncologists' view of AIT was mostly positive (78%). However, some (especially women) had doubts about the reliability of AI (44%) and the establishment of its ethical/legal basis (49%). Sixty-five percent of the participants had no/superficial knowledge about AI. More than half (55%) had never used AI-based applications in their academic or clinical work. However, unlike now, 80% of the participants believed that they would use AIT frequently in their practice in the future and it would be beneficial. The most anticipated (81%) benefit was real-time information processing and real-time access to big data. Sixty-two percent believed that information about AI should be in the education curriculum. The vast majority of respondents (79%) thought that AI would not completely replace medical oncologists in the future. Some differences were found in the perception and experience of oncologists according to age, gender, title, and the number of patients examined per day. About AI, the general opinion of medical oncologists was positive, but their level of knowledge and use was low. However, they thought they would use it frequently in future and needed training.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Furthermore, the professionals believe that AI would increase their efficiency in managing medical information and knowledge, by being granted quick access to vast amounts of knowledge with the ability to scan and summarize relevant information effectively with the help of AI [29, 30, 46–49] or being able to handle big-data records in real-time [50, 51].”
Citations
Cites
Artificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817
Artificial intelligence: A powerful paradigm for scientific research
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726
The rise of artificial intelligence in healthcare applications
Bohr, A., & Memarzadeh, K. (2020) · Artificial Intelligence in Healthcare · doi:10.1016/b978-0-12-818438-7.00002-2 · Cited by 1,636
Toward understanding the impact of artificial intelligence on labor
Frank, M. R., Autor, D., Bessen, J. E., Brynjolfsson, E., Cebrian, M., Deming, D. J., Feldman, M., Groh, M., Lobo, J., Moro, E., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900949116 · Cited by 713
Deep Learning—A Technology With the Potential to Transform Health Care
Hinton, G. (2018) · JAMA · doi:10.1001/jama.2018.11100 · Cited by 667
Mariani, M. M., Perez‐Vega, R., & Wirtz, J. (2022) · Psychology & Marketing · doi:10.1002/mar.21619 · Cited by 631 · cited 1×
“All these tools and platforms are powered by AI-assisted algorithms [2, 5, 14].”
MethodArtificial intelligence in oncology
Shimizu, H., & Nakayama, K. I. (2020) · Cancer Science · doi:10.1111/cas.14377 · Cited by 388
Physician Confidence in Artificial Intelligence: An Online Mobile Survey
Oh, S., Kim, J. H., Choi, S.-W., Lee, H. J., Hong, J., & Kwon, S. H. (2019) · Journal of Medical Internet Research · doi:10.2196/12422 · Cited by 387
Blease, C., Kaptchuk, T. J., Bernstein, M. H., Mandl, K. D., Halamka, J. D., & DesRoches, C. M. (2019) · Journal of Medical Internet Research · doi:10.2196/12802 · Cited by 346
Gong, B., Nugent, J. P., Guest, W., Parker, W., Chang, P. J., Khosa, F., & Nicolaou, S. (2019) · Academic Radiology · doi:10.1016/j.acra.2018.10.007 · Cited by 325
Chen, Z., Lin, L., Wu, C., Li, C., Xu, R., & Sun, Y. H. (2021) · 癌症：英文版 · doi:10.1002/cac2.12215 · Cited by 298
Physician perspectives on integration of artificial intelligence into diagnostic pathology
Sarwar, S., Dent, A., Faust, K., Richer, M., Djuric, U., Van Ommeren, R., & Diamandis, P. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0106-0 · Cited by 297
Scheetz, J., Rothschild, P., McGuinness, M. B., Hadoux, X., Soyer, H. P., Janda, M., Condon, J. J. J., Oakden‐Rayner, L., Palmer, L. J., Keel, S., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-84698-5 · Cited by 292
Artificial intelligence and the future of psychiatry: Insights from a global physician survey
Doraiswamy, P. M., Blease, C., & Bodner, K. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101753 · Cited by 253
Translating Artificial Intelligence Into Clinical Care
Beam, A. L., & Kohane, I. S. (2016) · JAMA · doi:10.1001/jama.2016.17217 · Cited by 238
European Society of Radiology (ESR) (2019) · Insights into Imaging · doi:10.1186/s13244-019-0798-3 · Cited by 207
Collado‐Mesa, F., Alvarez, E., & Arheart, K. L. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.12.021 · Cited by 141
Designing a Chatbot for a Brief Motivational Interview on Stress Management: Qualitative Case Study
Park, S., Choi, J., Lee, S., Oh, C., Kim, C., La, S., Lee, J., & Suh, B. (2019) · Journal of Medical Internet Research · doi:10.2196/12231 · Cited by 137
van Hoek, J., Huber, A. T., Leichtle, A. B., Härmä, K., Hilt, D., von Tengg‐Kobligk, H., Heverhagen, J. T., & Poellinger, A. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.108742 · Cited by 124
The fate of medicine in the time of AI
Coiera, E. W. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31925-1 · Cited by 96
Shaikh, A. A., Kumar, A., Jani, K., Mitra, S., García-Tadeo, D. A., & Devarajan, A. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.09.368 · Cited by 78
A Glimpse of the Next 100 Years in Medicine
Kohane, I. S., Drazen, J. M., & Campion, E. W. (2012) · New England Journal of Medicine · doi:10.1056/nejme1213371 · Cited by 74
Zheng, B. R., Wu, M., Zhu, S., Zhou, H., Hao, X., Fei, F., Jia, Y., Wu, J., Yang, W., & Pan, X.-B. (2021) · BMC Health Services Research · doi:10.1186/s12913-021-07044-5 · Cited by 54
The use and future perspective of Artificial Intelligence—A survey among German surgeons
Pecqueux, M., Riediger, C., Distler, M., Oehme, F., Bork, U., Kolbinger, F. R., Schöffski, O., van Wijngaarden, P., Weitz, J., Schweipert, J., et al. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.982335 · Cited by 41
Oncologist Burnout Syndrome in Eastern Europe: Results of the Multinational Survey
Kust, D., Murgić, J., Vuković, P., Kruljac, I., Prpić, M., Zilić, A., Lengyel, C. G., Wdowiak, K., Simaškaitė, L., Günaydın, U. M., et al. (2020) · JCO Oncology Practice · doi:10.1200/jop.19.00470 · Cited by 21
Radiation therapist perceptions on how artificial intelligence may affect their role and practice
O'Shaughnessey, J., & Collins, M. (2022) · Journal of Medical Radiation Sciences · doi:10.1002/jmrs.638 · Cited by 16
Cited by
Sorted by citations
Henzler, D., Schmidt, S., Koçar, A., Herdegen, S., Lindinger, G. L., Maris, M. T., Bak, M. A. R., Willems, D. L., Tan, H. L., Lauerer, M., et al. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-12664-2 · Cited by 54
“…c k a cc e ss , s u mm a r i z i n g i n f o r m a t i o n ) [ 29 , 30 , 46 – 49 ] • H a n d l i n g o f b i g d a t a i n r e a l - t i m e [ 50 , 51 ] W o r k i n g w i t h A I • D e c r e a s e t i m e o n r e p e t i t i v e t a s k s ( m e d i c a l , n o n - m e d i c a l ) [ 30 , 40 , 46 ,…”
Al-Ani, A., Rayyan, A., Maswadeh, A., Sultan, H., Alhammouri, A., Asfour, H., Alrawajih, T., Al Sharie, S., Al Karmi, F., Al-Azzam, A. M., et al. (2024) · BMC Medical Ethics · doi:10.1186/s12910-024-01008-0 · Cited by 21
Pedersen, M. R. V., Kusk, M. W., Lysdahlgaard, S., Mork-Knudsen, H., Malamateniou, C., & Jensen, J. (2024) · Radiography · doi:10.1016/j.radi.2024.04.020 · Cited by 21
Serin, O., Yıldırım, B. F., Duman, F., Ercorumlu, K., Yavas, R., Taşar, M. A., & Celik, M. S. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106003 · Cited by 7
Erdat, E. C., & Kavak, E. E. (2025) · BMC Cancer · doi:10.1186/s12885-025-13596-0 · Cited by 6
Metadata
- Authors
- Sahin, E.
- ORCID
- Sahin, Elif
- Venue
- Medical Oncology
- Published
- 2023-10-09
- Volume
- 40
- Issue
- 11
- Pages
- 327-327
- ISSN
- 1357-0560 (ISSN-L)
- 1559-131X
- OpenAlex
- W4387439772
- Cited by
- 12
- References
- 26
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5
- FWCI
- 0.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Medical Oncology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- artificial intelligence, medical oncologists, oncology practice, clinical implementation of AI, artificial intelligence in medical education
- MeSH terms
- Oncologists, Adult, Artificial Intelligence, Female, Humans, Male, Medical Oncology, Pilot Projects, Reproducibility of Results
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
