Importance of consent in mental health trials

Date 24 November 2025
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Importance of consent in mental health trials

Magdalena Kogut Czarkowska, Vaisakh Shaji (Timelex)

Artificial Intelligence is rapidly transforming mental health research. From predictive algorithms for depression to chatbots offering cognitive-behavioral support, AI-driven mental health studies promise earlier detection and personalized interventions. However, obtaining participants’ informed consent remains the cornerstone of trust and legitimacy in mental health studies – whether conducted in traditional clinical settings or through digital tools.

Consent under the GDPR: legal requirements

Consent is one of the legal grounds for lawful processing of personal data or special categories of personal data under the General Data Protection Regulation (GDPR).

  • Article 4(11) – defines consent as a freely given, specific, informed and unambiguous expression of a data subject’s wishes;
  • Article 6(1)(a) – provides consent as one of the lawful basis for processing personal data, while Article 9(2)(a) requires explicit consent for processing special category data, including health and mental health information;
  • Article 7 – mandates that consent must be demonstrable, and data subjects must be able to withdraw it at any time, easily and without penalty.

In mental health trials, where participants may experience fluctuating capacity or vulnerabilities, these principles take on deeper meaning. Consent should not be treated as a mere bureaucratic exercise of obtaining a signature; rather, it requires that the individual is genuinely able to comprehend their declaration of will and is in a position to express it voluntarily.

Broad consent in scientific research

Recital 33 of the GDPR clarifies that individuals can give consent to certain areas of scientific research when it is not possible to fully identify purposes in advance. This flexibility – known as broad consent – is essential in mental health research, where data may later support new studies, AI training, or meta-analysis.

The European Data Protection Board (EDPB) clarifies that broad consent is not license for open-ended data use. It must be accompanied by:

  • Transparency and ethics oversight;
  • Safeguards under Article 89(1) – data minimization, pseudonymisation, access controls;
  • Continuous engagement with participants

In essence, the concept of “broad consent” enables scientific research purposes but demands structured governance, particularly when AI tools repurpose data beyond the original trial context.

Ethical consent vs. consent as a legal basis for personal data processing

Ethical consent requirement requires a participant’s voluntary decision to take part in a study. As clarified by the EDPB, in the context of a clinical trial, this consent requirement addresses fundamental rights issues of the participant. The importance of ethical consent is underlined in such important international instruments like Declaration of Helsinki and Oviedo Convention.

GDPR consent pertains to individual’s agreement to the processing of their personal data for a specific purpose, and requires specific formality and explicitness. However, GDPR consent is not required, if there is another legal basis available that justifies data processing. For example, the doctor requires the patient’s data (including sensitive information) to make a diagnosis and plan a treatment, but does need to ask the patient for GDPR consent, because this data is linked to providing medical care to the patient.

On-going challenges:

Obtaining valid consent in health research faces several ongoing challenges

  • Ensuring participants have a genuine possibility to refuse consent, despite potential power imbalances.
  • Addressing fluctuating decision-making capacity of mental health patients, which can make it difficult to confirm understanding at any given moment. Researchers must monitor understanding and ensure re-consent or proxy consent where appropriate;
  • Communicating complex information about data handling, including storage, sharing, and secondary research uses, or even algorithmic processes and data-sharing risks in plain, non-technical language is difficult, but essential;
  • Managing consent fatigue, especially when participants are asked to sign multiple forms or participate in repeated procedures.
  • Explaining potential future uses of data, such as contributing to AI training, may be uncertain or difficult for participants to fully grasp. Moreover, – future AI model training or cross-study may require renewed consent or reliance on lawful alternatives;
  • Alignment with other emerging legal frameworks – Emerging regimes such as the European Health Data Space shifts focus on right to opt-out than relying on consent for processing data for secondary use.

Conclusion

AI’s expanding role in mental health intensifies both the promise and the risks associated with data use. In this evolving landscape, well-implemented consent remains the central ethical and legal safeguard—anchoring autonomy, supporting transparency, and sustaining trust.

References for further reading:

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