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Author

G. Sivaramakrishnan

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Open access Sep 2026

LLM-Generated Visual Summaries of Cochrane Plain-Language Summaries: A Pilot Expert-Rated Study and Implications for Research Communication

Background: Plain-language summaries (PLSs) improve accessibility of medical research for patients but remain predominantly text-based. Large language models (LLMs) can now generate images from text. We carried out the present exploratory study to evaluate whether LLMs can generate images that demonstrate technical acc...

K. Sridharan, G. Sivaramakrishnan, Juliet Rebecca Joseph et al. · 0 citations
Open access Sep 2026

Comparative Analysis of Large Language Models in Interpreting Publication Research Ethics: Insights from COPE Forum Case Studies.

BackgroundCommittee on Publication Ethics (COPE) serves as one of the key resources for journal editors navigating ethical challenges. This study evaluated two large language models (LLMs) in interpreting real-world publication ethics cases compared to COPE member responses.MethodsEleven anonymized COPE cases were sele...

K. Sridharan, G. Sivaramakrishnan · 0 citations
Aug 2026

Machine learning-integrated explainable artificial intelligence for survival prediction in urothelial carcinoma with enfortumab vedotin: an exploratory real-world analysis.

BACKGROUND Enfortumab vedotin (EV) has transformed treatment for advanced urothelial carcinoma (aUC), but outcomes vary. Machine learning (ML) with explainable artificial intelligence (XAI) may improve survival prediction. METHODS Data from 544 aUC patients receiving EV after platinum chemotherapy and immunotherapy (...

K. Sridharan, O. Fiala, G. Sivaramakrishnan et al. · 0 citations
#explainable ai Sep 2026

Machine learning integrated explainable artificial intelligence in predicting toxicities of enfortumab vedotin in urothelial carcinoma: an exploratory study.

Preliminary, hypothesis-generating findings suggest ML may predict EV-related toxicities, but single train-test split, small event counts, and lack of external validation preclude clinical use.

K. Sridharan, Mattia Alberto Di Civita, G. Sivaramakrishnan et al. · 0 citations
#explainable ai Open access Sep 2026

Machine Learning and Explainable AI for Predicting Survival and Mortality in People Living With HIV on Antiretroviral Therapy: A Secondary Analysis of the ACTG-175 Trial Dataset.

Machine learning models, particularly Elastic Net and Cox Proportional Hazards, effectively predict mortality and survival in PLWH, with immunological response, treatment-related factors, and clinical presentation emerging as the dominant predictors.

K. Sridharan, G. Sivaramakrishnan · 0 citations

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