Evaluating an AI-Based Mobile Application for Chemotherapy Support in Breast Cancer Patients (AI-ChemoApp)

April 20, 2026 updated by: Dena h. Al-Tameemi

Evaluating an AI-Based Mobile Application for Chemotherapy Support in Breast Cancer Patients: A Randomized Controlled Trial

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

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 Type

Interventional

Enrollment (Estimated)

130

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • Iraq
      • Baghdad, Iraq
        • Oncology Teaching Hospital-Medical City- Baghdad
        • Contact:
          • Rasha Saer Abbood, F.I.B.M.S med.oncology
          • Phone Number: 009647813281259
          • Email: Drrasha85@yahoo.com

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • 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.
  • Possession of a smartphone (Android or iOS) and functional digital literacy, defined as the ability to independently navigate mobile applications, read on-screen text in Arabic, and input daily health data. (for the intervention group).
  • Willingness to comply with study procedures and follow-up schedules.
  • Ability to communicate in Arabic, as the mobile application and chatbot will be developed in Arabic.

Exclusion Criteria:

  • Patients with Stage IV (Metastatic) breast cancer.
  • Patients receiving concurrent hormonal therapy during the chemotherapy phase, to isolate chemotherapy-induced adverse events.

Patients with cognitive impairment or severe psychiatric disorders that would preclude effective interaction with the mobile application or questionnaire completion.

  • 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.
  • Patients with severe comorbidities that could significantly impact their ability to participate or bias outcome measures.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Supportive Care
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: AI-Based Mobile Application Plus Usual Care

Participants in this group will receive the AI-based mobile application in addition to usual oncology care.

The Arabic-language mobile app uses artificial intelligence (AI) to provide personalized chemotherapy support, including symptom monitoring, medication adherence reminders, and educational guidance.

Participants will use the app daily for 12 weeks during their chemotherapy cycles.

All AI-generated advice is reviewed by oncologists and pharmacists to ensure clinical safety.

They will also receive standard oncology care provided by the hospital team, including chemotherapy administration, routine follow-up, and patient education according to local protocols.
Behavioral: AI-Based Mobile Application for Personalized Chemotherapy Support

The 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.

Other Names:
  • AI Breast Cancer Support App
  • AI-ChemoApp
Other: Usual Care
Standard oncology care provided by the hospital team, including chemotherapy administration, routine follow-up, and patient education according to local protocols.
Other Names:
  • standard care
  • ordinary care
Active Comparator: Usual Care Only

Participants in this group will receive standard oncology care provided by the hospital team, including chemotherapy administration, routine follow-up, and patient education according to local protocols.

They will not have access to the AI-based mobile application.
Other: Usual Care
Standard oncology care provided by the hospital team, including chemotherapy administration, routine follow-up, and patient education according to local protocols.
Other Names:
  • standard care
  • ordinary care

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Change in Symptom Burden and Chemotherapy-Related Toxicities (Arabic PRO-CTCAE)
Time Frame: Up to 12 weeks
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.
Up to 12 weeks
Medication Adherence Score (Arabic MMAS-8
Time Frame: Baseline and at weeks 6,12.
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.
Baseline and at weeks 6,12.
Change in Patient Knowledge and Information Satisfaction (EORTC QLQ-INFO25)
Time Frame: Baseline and at weeks 6,12.
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.
Baseline and at weeks 6,12.
Change in General Quality of Life (EORTC QLQ-C30)
Time Frame: Baseline and at weeks 6,12.

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.
Baseline and at weeks 6,12.
Change in Breast Cancer-Specific Quality of Life (EORTC QLQ-BR23)
Time Frame: Baseline and at weeks 6,12.

The Arabic version of EORTC QLQ-BR23 will be used to assess the change in Breast Cancer-Specific Quality of Life.The EORTC QLQ-BR23 is a supplementary module specifically for breast cancer patients. It consists of 23 items covering functional scales (body image, sexual functioning, future perspective) and symptom scales (side effects of systemic therapy, breast symptoms, arm symptoms, hair loss). Raw scores are transformed to a linear scale ranging from 0 to 100.

Functional Scales: Higher scores represent better functioning. Symptom Scales: Higher scores represent worse symptoms.
Baseline and at weeks 6,12.

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy of AI-Generated Advice Compared with Oncologist Assessment
Time Frame: Through study completion, an average of 12 weeks.
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.
Through study completion, an average of 12 weeks.
User Satisfaction and App Usability Score
Time Frame: At 12 weeks (end of intervention)

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
At 12 weeks (end of intervention)
App Usage Frequency
Time Frame: Continuously during the 12-week intervention.
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
Continuously during the 12-week intervention.
Identification of Technical, Ethical, and Implementation Barriers
Time Frame: After 12 weeks of intervention use and upon study completion.
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.
After 12 weeks of intervention use and upon study completion.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Dena h. Al-Tameemi

Collaborators

Baghdad Medical City

Investigators

  • Principal Investigator: Samer Imad Mohammed, Assistant Prof, University of Baghdad-College of Pharmacy

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

May 1, 2026

Primary Completion (Estimated)

July 1, 2026

Study Completion (Estimated)

October 1, 2026

Study Registration Dates

First Submitted

November 17, 2025

First Submitted That Met QC Criteria

December 5, 2025

First Posted (Actual)

December 10, 2025

Study Record Updates

Last Update Posted (Actual)

April 21, 2026

Last Update Submitted That Met QC Criteria

April 20, 2026

Last Verified

November 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • RECAUBCP2192506C

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

Deidentified individual participant data (IPD) that underlie the results reported in published articles will be made available to qualified academic researchers upon reasonable request. Data sharing will occur after publication of the main results and subject to approval by the Research Ethics Committee, College of Pharmacy, University of Baghdad. Data will include coded variables without any identifiers to ensure participant confidentiality.

IPD Sharing Time Frame

Deidentified individual participant data and related documents will be available beginning 6 months after publication of the main study results and for up to 3 years thereafter. Requests beyond this period will be considered on a case-by-case basis depending on data availability and ethical approval.

IPD Sharing Access Criteria

Access to the deidentified data will be granted to qualified investigators for non-commercial academic research. Requests must include a clear research proposal and data-use agreement ensuring data protection. Data will be shared electronically through secure university-approved transfer methods after approval by the principal investigator and the Research Ethics Committee, College of Pharmacy, University of Baghdad.

IPD Sharing Supporting Information Type

  • STUDY_PROTOCOL
  • SAP
  • ICF

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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