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Turkish medical oncologists' perspectives on integrating artificial intelligence: knowledge, attitudes, and ethical
Efe Cem Erdat1, Filiz Çay Şenler2
1Faculty of Medicine, Department of Medical Oncology, Ankara University, Balkiraz Mh. Tip Fakultesi Cad. No 1 Mamak, Ankara, Türkiye. cemerdat@gmail.com.
Background:
Integrating artificial intelligence (AI), especially large language models (LLM) into oncology has potential benefits, yet medical oncologists' knowledge, attitudes, and ethical concerns remain unclear. Understanding these perspectives is particularly relevant in Türkiye, which has approximately 1340 practicing oncologists.
Methods:
A cross-sectional, online survey was distributed via the Turkish Society of Medical Oncology's channels from October 16 to November 27, 2024. Data on demographics, AI usage, self-assessed knowledge, attitudes, ethical/regulatory perceptions, and educational needs were collected. Quantitative analyses were performed using descriptive statistics and graphics were generated using R v.4.3.1, and qualitative analysis of open-ended responses was conducted manually.
Results:
Of 147 respondents (representing about 11% of Turkish oncologists), 77.5% reported prior AI use, mainly LLMs, yet only 9.5% had formal AI education. While most supported integrating AI into prognosis estimation, research, and decision support, concerns persisted regarding patient-physician relationships and social perception. Ethical reservations centered on patient management, scholarly writing, and research design. Over 79% deemed current regulations inadequate and advocated ethical audits, legal frameworks, and patient consent. Nearly all were willing to receive AI training, reflecting a substantial educational gap.
Conclusions:
Turkish medical oncologists exhibit cautious optimism toward AI but highlight critical gaps in training, clear regulations, and ethical safeguards. Addressing these needs could guide responsible AI integration. Limitations include a single-country perspective. Further research is warranted to generalize findings and assess evolving attitudes as AI advances.
Trial Registration:
Not applicable due to cross-sectional survey design.
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