Potential Use of AI Chatbots by Patients With Bowel Tumors: Opportunities and Implications for Low- and Middle-Income Countries.

Authors

Jehad F AlSamhori, Abdel Rahman F AlSamhori, Rama M Amourah, Hazim Aldweik, Ahmad F AlSamhori, Leen M Holy, Rihane Zakraoui, Sawsan H Alomari, Awni D Shahait

Published in

Cureus. Volume 18. Issue 9. Pages e116502. Epub Sep 18, 2026.

Abstract

Bowel tumors and colorectal cancer (CRC), in particular, have become a major concern to the global health community and have led to high morbidity and mortality levels, especially in lower- and middle-income countries (LMICs). This narrative review examines the potential roles of patient-facing AI chatbots and large language model (LLM)-based conversational tools across bowel tumor care, with particular attention to their potential applicability, limitations, and implementation requirements in LMICs. The available evidence spans small prospective and feasibility studies of patient-facing symptom monitoring or education as well as studies evaluating LLM performance in simulated clinical scenarios and clinician-facing decision support. Among the 16 studies, three were conducted in middle-income countries, and none in low-income countries; most evidence therefore arose from high-income settings. Patient-facing interventions generally support feasibility, acceptability, symptom reporting, and communication, whereas accuracy and concordance for screening, diagnosis, or treatment recommendations vary by task and are less reliable in complex cases. These findings support chatbots primarily as adjuncts to structured clinical care rather than autonomous clinical decision-making tools. AI chatbots may complement bowel tumor care through education, symptom monitoring, communication, and selected decision-support functions, but current evidence does not establish broad clinical effectiveness or safety, particularly in LMICs. Prospective validation, human oversight, privacy safeguards, local-language and cultural adaptation, digital-access considerations, and context-specific implementation studies are needed before wider clinical adoption.

PMID:
42894742
Bibliographic data and abstract were imported from PubMed on 19 Oct 2026.

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