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.
Confirmed diagnosis of breast cancer stages I, II, or III.
Patients must be currently scheduled to initiate their first-ever cycle of chemotherapy.
Age 18 years or older.
Ability to understand and provide informed consent.
Patients with Stage IV (Metastatic) breast cancer.
Patients receiving concurrent hormonal therapy during the chemotherapy phase, to isolate chemotherapy-induced adverse events.
Patients receiving palliative care where symptom management is the sole focus and active chemotherapy is not being administered with curative or life prolonging intent.
Patients participating in other interventional clinical trials that might confound the outcomes of this study.
Parallel
AI-Based Mobile Application for Personalized Chemotherapy Support
BehavioralThe intervention is an Arabic-language mobile application powered by artificial intelligence (AI) designed to provide personalized chemotherapy support for women with breast cancer. The app assists participants by monitoring symptoms, sending medication adherence reminders, and offering educational content on managing side effects and improving treatment understanding. It uses a conversational interface based on natural language processing (NLP) to communicate with users. Participants are asked to use the app daily for 12 weeks while receiving chemotherapy. A human-in-the-loop system ensures oncologists and pharmacists review AI-generated advice for accuracy and safety.
Usual Care
Other interventionStandard oncology care provided by the hospital team, including chemotherapy administration, routine follow-up, and patient education according to local protocols.
Change in Symptom Burden and Chemotherapy-Related Toxicities (Arabic PRO-CTCAE)
Symptom burden and treatment-related toxicities will be assessed using the validated Arabic version of the Patient-Reported Outcomes-Common Terminology Criteria for Adverse Events (PRO-CTCAE). Participants will report frequency, severity, and interference of common chemotherapy-related symptoms such as fatigue, nausea, vomiting, pain, and neuropathy. Mean score changes between baseline and 12 weeks will be compared between the AI app group and the usual care group to evaluate whether the intervention lowers symptom burden and improves self-management.Data will be collected at Baseline and weekly thereafter.
Medication Adherence Score (Arabic MMAS-8
Medication adherence will be measured using the 8-item Morisky Medication Adherence Scale (MMAS-8) in its validated Arabic version. Participants' self-reported responses generate a score from 0-8, with higher scores indicating better adherence. The mean change in adherence scores from baseline to 12 weeks will be compared between groups to determine the app's effectiveness in promoting medication adherence during chemotherapy.
Change in Patient Knowledge and Information Satisfaction (EORTC QLQ-INFO25)
The Arabic version of EORTC QLQ-INFO25 questionnaire will assess participants' perception of the adequacy, clarity, and usefulness of information provided about their disease, treatment, and care. The total score ranges from 0 to 100, with higher scores reflecting greater satisfaction with information. The AI-based mobile app is expected to increase knowledge and satisfaction through personalized, accessible education.
Change in General Quality of Life (EORTC QLQ-C30)
Quality of life (QoL) will be measured using the validated Arabic version of the EORTC QLQ-C30. The EORTC QLQ-C30 is a 30-item questionnaire used to assess the quality of life of cancer patients. It incorporates five functional scales (physical, role, cognitive, emotional, and social), three symptom scales (fatigue, pain, and nausea/vomiting), and a global health status/QoL scale. Raw scores are transformed to a linear scale ranging from 0 to 100. Functional Scales \& Global Health Status: Higher scores represent a higher/better level of functioning and quality of life. Symptom Scales: Higher scores represent a higher/worse level of symptomatology.
Accuracy of AI-Generated Advice Compared with Oncologist Assessment
The accuracy and clinical appropriateness of the AI-generated recommendations will be evaluated by comparing the app's advice logs to oncologist judgments on the same patient-reported scenarios. Agreement will be assessed using Cohen's kappa coefficient to determine the reliability of the AI-based symptom triage.
User Satisfaction and App Usability Score
Participants will evaluate the application using a structured questionnaire assessing multiple domains including ease of use, perceived usefulness, information clarity, and trust in AI recommendations. Responses are measured on a 5-point Likert scale: 1 = Strongly Disagree 2 = Disagree 3 = Neutral 4 = Agree 5 = Strongly Agree The final outcome is reported as an aggregate average score across all items. Scale Range: 1 to 5. Interpretation: Higher scores indicate higher user satisfaction
App Usage Frequency
Engagement is assessed by monitoring the backend usage logs to determine the average number of distinct application sessions (log-ins) per participant. Unit of Measure: Sessions per week
Identification of Technical, Ethical, and Implementation Barriers
Qualitative data will be collected through semi-structured interviews to identify challenges related to app usability, privacy concerns, data reliability, and clinical integration. Thematic analysis will be conducted to inform strategies for safe and effective implementation of AI-based supportive care tools in Iraqi oncology settings.
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Dena h. Al-Tameemi
Lead sponsor
University of Baghdad
Sponsor institution
Baghdad Medical City
Collaborator