Analysis
Critical analysis of AI and mental health news. Our goal: expose biases, bring nuance, and reopen the debate.
When AI makes headlines in mental health, we analyze, question and open up reflection.
Doctolib reuses health data for AI research by default. What the email promises the patient, what the contract promises the practitioner, and mental health's blind spot.
More than 170 AI ethics frameworks published in ten years, zero demonstrated effect on patients. Yet the five-principles framework (Floridi-Cowls) remains the practitioner's best starting point — provided you know what it can do, what it cannot, and how to convert it into an actionable evaluation grid. Founding article of our dossier on the Ethics of AI in Mental Health.
The study conducted by Luc Garczynski at Université de Montréal interviewed four Quebec CBT psychologists about their actual uses of ChatGPT in their practice. We jointly present what he found: a spontaneous three-step protocol, a systematically peripheral use, an invisible prerequisite — and two phenomena that no one had anticipated.
Elizabeth Stade and her team (Stanford, Penn, Johns Hopkins) propose a framework for thinking about the integration of LLMs in psychotherapy, articulated around three tiers of autonomy (assistive, collaborative, autonomous). Luc Garczynski (UdeM, 2026) formalises its applications into five axes and adds two unprecedented empirical categories.
The HAS-CNIL guide on AI in healthcare (February 2026) mentions neither psychotherapy nor mental health. Three critical blind spots for psychologists: consent, professional secrecy, situation awareness. Analysis and practical tools.
Published in the BMJ in March 2025, PROBAST+AI is the first quality assessment tool for clinical prediction models that holds classical statistical and artificial intelligence approaches to the same standards of rigour. Its starting finding is damning: most published models are of poor quality, their performance is overestimated and their biases go unnoticed. Sixth instalment of our series on AI evaluation frameworks in healthcare.
In April 2025, CONSORT and SPIRIT published their first coordinated update since 2010 and 2013. Major advance: Open Science is now integrated into the base standards. But artificial intelligence? Absent. Despite CONSORT-AI existing since 2020, the new guidelines mention neither AI, nor existing extensions, nor algorithmic transparency issues.
Major AI health studies — published in Nature, JAMA, The Lancet — rely on Prolific workers, text-based vignettes, and inadequate comparators. Five layers of distance separate these protocols from clinical reality. A detailed analysis with data and a practical reading framework for the clinician.
Out of 137 studies published in the year following ChatGPT's launch, fewer than 40% report key elements of their prompting strategy. An international consortium of 531 experts proposes 12 criteria to fix this — and change how we read these studies.
An AI that achieves 95% accuracy on standardized cases can fail in real clinical practice. A human factors researcher at West Virginia University explains why — and offers a three-level framework every clinician should know before trusting an AI health study.
Published in 2020, CONSORT-AI mandates 14 transparency criteria for clinical trials testing AI interventions. Five years on, adherence is declining and most journals ignore these standards. What this reveals — and how it changes the way we critically read studies.
Out of 160 studies reviewed, LLMs account for 77% of bench tests but only 16% of clinical trials. A Harvard team proposes a three-tier framework to clarify what studies actually prove — and what they don't.
France's health authority (HAS) has published its first recommendations on AI in healthcare and launched an ambitious mental health program. But these two efforts advance in parallel, without ever crossing. Mapping an institutional blind spot that directly concerns psychologists.
A Munich-based team proposes integrating ethicists directly into medical AI development teams. The idea is appealing—but is it enough? Analysis of an approach directly relevant to future AI-assisted psychotherapy tools.
A prominent psychiatrist publishes a warning about therapeutic chatbots in the British Journal of Psychiatry. His analysis is lucid on certain risks, but falls into cognitive traps that every clinician should know how to spot — in authors and in themselves.
When a study mentions a 'therapeutic chatbot', are we talking about a scripted decision tree or a fine-tuned GPT-4? This terminological blur is far from trivial: it makes studies incomparable and public debate unintelligible.
Explore our practical resources to integrate AI into your clinical practice ethically.
