Article Text

Download PDFPDF
Original research
Conversational AI facilitates mental health assessments and is associated with improved recovery rates
  1. Max Rollwage1,
  2. Johanna Habicht1,
  3. Keno Juchems1,
  4. Ben Carrington1,
  5. Tobias U Hauser1,2,3,4,5,
  6. Ross Harper1
  1. 1 AI & Research, Limbic, London, UK
  2. 2 Department of Psychiatry and Psychotherapy, Medical School and University Hospital, Eberhard Karls University of Tübingen, Tübingen, Germany
  3. 3 German Center for Mental Health (DZPG), Tübingen, Germany
  4. 4 Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
  5. 5 Wellcome Centre for Human Neuroimaging, University College London, London, UK
  1. Correspondence to Dr Max Rollwage, AI & Research, Limbic, London, EC1V 2NX, UK; max{at}


Mental health services across the globe are overburdened due to increased patient need for psychological therapies and a shortage of qualified mental health practitioners. This is unlikely to change in the short-to-medium term. Digital support is urgently needed to facilitate access to mental healthcare while creating efficiencies in service delivery. In this paper, we evaluate the use of a conversational artificial intelligence (AI) solution (Limbic Access) to assist both patients and mental health practitioners with referral, triage, and clinical assessment of mild-to-moderate adult mental illness. Assessing this solution in the context of England’s National Health Service (NHS) Talking Therapies services, we demonstrate in a cohort study design that deploying such an AI solution is associated with improved recovery rates. We find that those NHS Talking Therapies services that introduced the conversational AI solution improved their recovery rates, while comparable NHS Talking Therapies services across the country reported deteriorating recovery rates during the same time period. Further, we provide an economic analysis indicating that the usage of this AI solution can be highly cost-effective relative to other methods of improving recovery rates. Together, these results highlight the potential of AI solutions to support mental health services in the delivery of quality care in the context of worsening workforce supply and system overburdening. For transparency, the authors of this paper declare our conflict of interest as employees and shareholders of Limbic Access, the AI solution referred to in this paper.

  • Costs and Cost Analysis
  • Health Care Quality, Access, and Evaluation
  • Mental Health Recovery
  • Psychology, Medical

Data availability statement

Data available at a dedicated GitHub repository. Code and data supporting this study are available at a dedicated GitHub repository.

Statistics from

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Data availability statement

Data available at a dedicated GitHub repository. Code and data supporting this study are available at a dedicated GitHub repository.

View Full Text


  • X @M_Rollwage

  • Contributors MR and BC conceived and planned the study with input from RH. MR and JH analysed the data with input from TUH, KJ and RH. MR wrote the first draft of the manuscript with edits from all other authors. JH revised the initial draft of the manuscript. MR is responsible for the overall content as guarantor.

  • Funding This work was supported by Limbic Limited.

  • Competing interests MR, KJ, JH, BC and RH are employed by Limbic Limited and hold shares in the company. TUH is working as a paid consultant for Limbic Limited.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.