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Critical analysis of AI and mental health news. Our goal: expose biases, bring nuance, and reopen the debate.

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When AI makes headlines in mental health, we analyze, question and open up reflection.

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Five principles to judge AI in mental health: what the AIEF framework is worth, and what it lacks
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Five principles to judge AI in mental health: what the AIEF framework is worth, and what it lacks

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.

ethics ethical principles AIEF chatbot regulation professional ethics evidence-based ethics pillar page

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What four CBT psychologists really do with ChatGPT

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.

LLMs in psychotherapy CBT and AI clinical uses responsible integration Stade 2024 framework AI ethics private practice Quebec
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The Stade 2024 framework for integrating LLMs in psychotherapy — and Garczynski's matrix formalisation
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The Stade 2024 framework for integrating LLMs in psychotherapy — and Garczynski's matrix formalisation

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.

Stade 2024 framework LLMs in psychotherapy responsible integration CBT and AI digital mental health machine in the loop human in the loop three tiers of autonomy Garczynski five axes AI ethics
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PROBAST+AI: 34 questions that most AI prediction models in healthcare don't survive

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.

reporting guideline prediction model risk of bias AI evaluation evidence-based medicine PROBAST algorithmic fairness
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CONSORT 2025 and SPIRIT 2025: Open Science yes, Artificial Intelligence no

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.

reporting guideline clinical trial open science methodological transparency normative fragmentation
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From Lab to Practice: Why AI Health Studies Don't Measure What They Claim

From Lab to Practice: Why AI Health Studies Don't Measure What They Claim

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.

ecological validity methodology critical appraisal Bean et al. Nature Medicine JAMA mental health double standards AI clinical trials
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Fewer than 40% of health chatbot studies report their prompting strategy: the CHART Statement

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.

reporting guideline chatbot methodological transparency reproducibility AI evaluation
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Why an AI That 'Outperforms Doctors' in the Lab Can Fail in the Clinic: Choudhury's Framework
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Why an AI That 'Outperforms Doctors' in the Lab Can Fail in the Clinic: Choudhury's Framework

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.

ecological validity human factors AI evaluation trust clinical adoption accountability
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Only 3 out of 52 journals require transparency for AI clinical trials: the CONSORT-AI case

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.

reporting guideline clinical trial methodological transparency reproducibility AI evaluation
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77% of LLM studies in mental health never get past the bench test stage: the Hua framework

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.

ecological validity AI evaluation chatbot mental health LLM methodology
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What France's HAS says (and doesn't say) about AI in mental health

What France's HAS says (and doesn't say) about AI in mental health

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.

HAS regulation mental health digital therapeutics ethics France A.V.E.C.
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Embedded ethics: What if ethicists joined the teams developing your AI tools?
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Embedded ethics: What if ethicists joined the teams developing your AI tools?

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.

ethics embedded ethics AI development interdisciplinarity medical AI bioethics
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AI, chatbot, LLM, app: why we need to stop conflating everything

AI, chatbot, LLM, app: why we need to stop conflating everything

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.

terminology chatbot LLM mental health apps epistemology research
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The APA Model: A Framework for Evaluating Mental Health Apps

Over 10,000 mental health apps on the stores, but only 15% with clinical evidence. The APA model offers a 5-level framework to help clinicians navigate the landscape.

APA mental health apps evaluation ethics practical guide
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APA Ethical Guide: What Psychologists Should Know

The APA publishes an ethical guide for AI use in psychological practice. 71% of American psychologists have never used AI: what lessons can we draw?

APA ethics practical guide regulation AI in practice
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ChatGPT Health: When AI Enters Your Medical Records

OpenAI launches a dedicated version of ChatGPT for healthcare. Between promises of patient empowerment and unprecedented concentration of sensitive data, this announcement deserves critical examination.

OpenAI ChatGPT digital health health data ethics

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