Jul 2026· 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS)· pp. 1-6· 0 citations· 26 references
Abstract
This study presents a bias-aware systematic review of artificial intelligence (AI)-based mental health detection using social media data from 2015 to 2025. Guided by PRISMA 2020 and PROBAST(+AI), records from IEEE Xplore, Scopus, and Web of Science were screened from 3,861 initial records to 308 included studies. Results show a strong shift toward transformer and hybrid models, depression-focused tasks, and Twitter/X and Reddit datasets. However, the corpus also shows major methodological gaps, including absent external validation, limited reporting of class imbalance handling, low explainable AI adoption, and underreported platform sources. The review argues that future systems require transparent data provenance, bias-aware validation, explainable decision support, and privacy-preserving crossplatform evaluation before clinical or public-health deployment.
AI-generated health information significantly shapes medical consultation behaviour, necessitating risk-stratified deployment strategies, clinician guidance frameworks, and interventions to ensure equitable access and preserve the physician-patient relationship.
Abeer Mohammed Khamis· Journal of Intelligent Decis...· 0 citations
Abstract Background A growing body of literature leverages large language models (LLMs) to make mental health predictions. However, these models are prone to bias, and studies to validate their clinical utility are lacking. Objective This scoping review aims to uncover bias and clinical utility limitations stemming fro...
Clémentine Bleuze, Karen Fort, Vincent P. Martin et al.· JMIR AI· 0 citations
A comparative analysis of user sentiment on X regarding public opinion on stunting using three widely utilized text classification algorithms: K-Nearest Neighbour, Naïve Bayes, and Decision Tree demonstrates the effectiveness of the Naïve Bayes algorithm for sentiment analysis in public health contexts.
Vivian Collahua Rupaylla, Richard Santiago Quivio Cuno, Sandra Guadalupe et al.· Journal of Intelligent Decis...· 0 citations
It is concluded that LLM-based decision-support tools hold substantial promise as complementary — rather than autonomous — decision-support systems capable of transforming medication safety and pharmacy practice.
K. K. Kumar, Koyya Gowtham Reddy, K. Reddy· International Scientific Jou...· 0 citations
It was identified that AI continues to significantly outperform humans in terms of accuracy, efficiency, and early intervention for mental health detection.
U. Nazir, Nur Shazwani Kamarudin, Mazlina Abdul Majid· Journal of Communication, La...· 0 citations
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