The Use of Artificial Intelligence Chatbots by Newly Diagnosed Cancer Patients: A Descriptive Phenomenological Study

After a cancer diagnosis, patients experience intense uncertainty and increasingly resort to artificial intelligence chatbots in this process; however, how this use is experienced by patients has not been adequately studied. This study aimed to explore the experiences of adult patients diagnosed with cancer in the last six months using artificial intelligence (AI) chatbots. Using a descriptive phenomenological approach, face-to-face semi-structured interviews were carried out with 20 adults sampled by a criterion-based purposive sampling method in an oncology clinic in Türkiye. The data were analyzed according to Colaizzi’s method, and the following four main themes were revealed: artificial intelligence usage purposes, the experience of interacting with AI, the effect of the patient’s reflection on the AI experience on the relationship with the treatment team, and evaluation of the use of AI. The participants used chatbots as an intermediate resource during the period of uncertainty between the examination result and clinical explanation; some learned their diagnosis for the first time in this way. However, the relief provided by chatbots was temporary, and trust in chatbot results was constantly tested by the statements of the healthcare professional. As a result, some participants hid their use of chatbots for fear of being judged. AI chatbots served as a complementary source of information, but they did not replace the relationship with the healthcare professional. These findings suggest that healthcare professionals should ask patients about chatbot use without judgment, and that support provided during the post-examination waiting process could be strengthened.

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Publication Details

Journal
Current Oncology
Published
2026-09-17
DOI
https://doi.org/10.3390/curroncol33090563
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
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article

The Use of Artificial Intelligence Chatbots by Newly Diagnosed Cancer Patients: A Descriptive Phenomenological Study

İlhan Günbayı, Yağmur Çolak Yılmazer, Mustafa Serkan Alemdar, Hasan Mutlu
Current Oncology
AI in Service Interactions
article

The Use of Artificial Intelligence Chatbots by Newly Diagnosed Cancer Patients: A Descriptive Phenomenological Study

İlhan Günbayı, Yağmur Çolak Yılmazer, Mustafa Serkan Alemdar, Hasan Mutlu
article en

Abstract

After a cancer diagnosis, patients experience intense uncertainty and increasingly resort to artificial intelligence chatbots in this process; however, how this use is experienced by patients has not been adequately studied. This study aimed to explore the experiences of adult patients diagnosed with cancer in the last six months using artificial intelligence (AI) chatbots. Using a descriptive phenomenological approach, face-to-face semi-structured interviews were carried out with 20 adults sampled by a criterion-based purposive sampling method in an oncology clinic in Türkiye. The data were analyzed according to Colaizzi’s method, and the following four main themes were revealed: artificial intelligence usage purposes, the experience of interacting with AI, the effect of the patient’s reflection on the AI experience on the relationship with the treatment team, and evaluation of the use of AI. The participants used chatbots as an intermediate resource during the period of uncertainty between the examination result and clinical explanation; some learned their diagnosis for the first time in this way. However, the relief provided by chatbots was temporary, and trust in chatbot results was constantly tested by the statements of the healthcare professional. As a result, some participants hid their use of chatbots for fear of being judged. AI chatbots served as a complementary source of information, but they did not replace the relationship with the healthcare professional. These findings suggest that healthcare professionals should ask patients about chatbot use without judgment, and that support provided during the post-examination waiting process could be strengthened.

Current OncologyVol. 33(9)
Akdeniz University (TR), Istinye University (TR), Antalya IVF (TR)
Openalex Percentile: Top 8%
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