Potential Use of AI Chatbots by Patients With Bowel Tumors: Opportunities and Implications for Low- and Middle-Income Countries.
- 1School of Medicine, The University of Jordan, Amman, JOR.
- 2Department of Surgery, Southern Illinois University School of Medicine, Springfield, USA.
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.
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BibTeX
@article{alsamhori2026potential,
title = {{Potential Use of AI Chatbots by Patients With Bowel Tumors: Opportunities and Implications for Low- and Middle-Income Countries.}},
author = {Jehad F AlSamhori and Abdel Rahman F AlSamhori and Rama M Amourah and Hazim Aldweik and Ahmad F AlSamhori and Leen M Holy and Rihane Zakraoui and Sawsan H Alomari and Awni D Shahait},
journal = {Cureus},
year = {2026},
month = sep,
doi = {10.7759/cureus.116502},
url = {https://europepmc.org/article/MED/42894742},
note = {CC BY},
}
RIS
TY - JOUR
TI - Potential Use of AI Chatbots by Patients With Bowel Tumors: Opportunities and Implications for Low- and Middle-Income Countries.
AU - Jehad F AlSamhori
AU - Abdel Rahman F AlSamhori
AU - Rama M Amourah
AU - Hazim Aldweik
AU - Ahmad F AlSamhori
AU - Leen M Holy
AU - Rihane Zakraoui
AU - Sawsan H Alomari
AU - Awni D Shahait
PY - 2026
DA - 2026/09/18
JO - Cureus
DO - 10.7759/cureus.116502
UR - https://europepmc.org/article/MED/42894742
AB - 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.
SN - 2168-8184
ER -