{
  "id": 1993,
  "url": "https://link.springer.com/article/10.1007/s10462-026-11587-6",
  "title": "Supervised machine learning classifiers for schizophrenia and bipolar disorder using speech and language: a systematic review, meta-analysis, and novel quality assessment framework",
  "summary": "This paper presents a systematic review and meta-analysis of 62 studies that developed speech- and language-based AI for severe mental illnesses (SMI) (i.e., characterized by substantial communication problems affecting speech production and language). We employed a random-effects meta-analysis using Restricted Maximum Likelihood (REML). We evaluated these studies using our proposed rigorous 16-item quality assessment framework, grouped into three domains: Study Design, Fairness and Explainabili",
  "authors": null,
  "category": "research",
  "topics": "bias-fairness,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-29T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-artificial-intelligence-review",
  "source_name": "Artificial Intelligence Review",
  "source_homepage": "https://link.springer.com/journal/10462",
  "ethics_ai_record_url": "https://ethics.ai/record/1993",
  "original_url": "https://link.springer.com/article/10.1007/s10462-026-11587-6",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}