Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-26 @ 11:08 AM
Ignite Modification Date:
2025-12-26 @ 11:08 AM
NCT ID:
NCT07273812
Brief Summary:
The goal of this clinical trial is to learn if an Arabic-language mobile application that uses artificial intelligence (AI) can help women with breast cancer during chemotherapy. The app is designed to give personalized support by reminding participants about their medications, teaching them how to manage treatment side effects, and alerting their healthcare team about serious symptoms.
The main questions this study aims to answer are:
1. Does the AI-based mobile app provide accurate and safe recommendations for the patients?
2. Does using the AI-based mobile app help lower treatment-related symptoms and side effects compared to usual care?
3. Does the app help participants take their medications more regularly?
4. Does it increase participants' understanding and satisfaction with the information they receive about their treatment?
Researchers will compare two groups:
Group 1: Participants who use the AI-based mobile app plus usual oncology care. Group 2: Participants who receive usual care only.
Participants will:
1. Use the mobile app daily for 12 weeks while receiving chemotherapy.
2. Complete short questionnaires about symptoms, medication use, and quality of life at the start and end of the study.
3. Report any problems or feedback about using the app. The AI app is for support and education only. It does not make treatment decisions. All information from the app will be reviewed by oncologists and pharmacists to ensure participant safety.
Detailed Description:
Despite advances in oncology care, breast cancer patients in Iraq face significant challenges regarding medication adherence and symptom management during the inter-cycle chemotherapy periods. This randomized controlled trial aims to bridge this gap by evaluating the efficacy, safety, and feasibility of a specialized, Arabic-language Artificial Intelligence (AI) mobile application.
Current standard care in the local setting often relies on episodic clinic visits, leaving patients without real-time support for side effects experienced at home. This study hypothesizes that a continuous, AI-driven digital intervention can reduce symptom burden and improve adherence to chemotherapy and supportive care medications (e.g., antiemetics) compared to standard care alone. The application utilizes Natural Language Processing (NLP) to provide conversational support tailored specifically to the cultural and linguistic context of Iraqi patients.
The intervention integrates a "Human-in-the-loop" safety model to ensure clinical accuracy. The AI algorithms are trained on clinical practice guidelines adapted for the local formulary.
Symptom Triage Logic: The app utilizes an algorithm based on the CTCAE grading system. Low-grade symptoms trigger self-care advice (e.g., hydration, dietary changes), while high-grade symptoms trigger immediate alerts to the patient to seek care and a notification to the study investigators.
Adherence Algorithms: Unlike static alarms, the notification system adapts to the specific chemotherapy cycle (e.g., AC or Taxane-based regimens) to remind patients of specific supportive medications required on specific days.
Control Group Specification (Standard of Care) Participants randomized to the control arm will receive the institutional standard of care. This includes routine oncologist consultations, standard written or verbal discharge instructions regarding chemotherapy side effects, and pharmacy dispensing counseling. They will not have access to the interactive AI features but will undergo the same schedule of outcome assessments to ensure rigorous comparison.
This study represents the first empirical effort to integrate AI-driven digital health tools into the public oncology sector in Iraq. It aims to validate whether automated, algorithmic triage is a feasible addition to the healthcare infrastructure in low-resource settings.
Study:
NCT07273812
Study Brief:
Protocol Section:
NCT07273812