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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: June 2026
Volume 11 | Issue 6
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Impact Factor : 8.14
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| Paper Title: | TrialMitra: Federated AI For Patient-Friendly Clinical Trials |
| Authors Name: | Ananya S , Chinmayi K , Nikhita R , Niriksha K , Piyush Kumar Pareek |
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IJRTI_212285
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| Published Paper Id: | IJRTI2605004 |
| Published In: | Volume 11 Issue 5, May-2026 |
| DOI: | |
| Abstract: | The challenge of recognizing and predicting patient suitability for clinical trials remains a significant impediment in clinical research with high chances of enrollment delays in most clinical trials. Traditional techniques involve the centralization of sensitive patient health information and thus raise several security concerns, such as compliance with frameworks such as HIPAA and GDPR. In this paper, we present TrialMitra, a secure and privacy-aware platform for clinical trial recruiting. In TrialMitra, Federated Learning (FL) is used to carry out decentralized prediction of eligibility across multiple hospitals, thereby preserving raw patient data. The system uses a Federated Averaging (FedAvg) framework based on logistic regression optimized using Stochastic Gradient Descent. In addition, we ensure local privacy using Local Differential Privacy (LDP) by adding Gaussian noise with (σ = 0.01). We conduct our experiment on a simulated scenario involving a four-hospital network having highly heterogeneous (non-IID) datasets, generated through the use of Dirichlet process distribution (α = 0.1). Our experimental results, using 100,000+ records from ten different diseases, show that after 15 communication rounds, TrialMitra achieves an accuracy rate of 82.92%, precision of 84.78%, recall of 84.99%, and F1-Score of 84.88%. The platform is additionally enriched with an AI-powered multilingual chatbot supporting four Indian languages, along with a real-time trial recommendation feature linked with the ClinicalTrials.gov database. It is worth mentioning here that the study results reveal that federated learning systems have the potential to provide reliable predictions just like centralized systems. |
| Keywords: | Federated Learning, Clinical Trial Recruitment, Privacy-Preserving Machine Learning, Differential Privacy, Healthcare AI, FedAvg, Patient Eligibility Prediction |
| Cite Article: | "TrialMitra: Federated AI For Patient-Friendly Clinical Trials", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.a37-a42, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605004.pdf |
| Downloads: | 000205553 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2605004
Registration ID:212285
Published In: Volume 11 Issue 5, May-2026
DOI (Digital Object Identifier):
Page No: a37-a42 Country: Bengaluru, Karnataka, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2605004 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2605004 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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