Bubeck, S. et al. Sparks of artificial general intelligence: Early experiments with GPT-4. Preprint at http://arxiv.org/abs/2303.12712 (2023).

Broderick, R. People are using AI for therapy, whether the tech is ready for it or not. Fast Company (2023).

Weizenbaum, J. ELIZA—a computer program for the study of natural language communication between man and machine. Commun. ACM 9, 36–45 (1966).

Article 

Google Scholar 

Bantilan, N., Malgaroli, M., Ray, B. & Hull, T. D. Just in time crisis response: Suicide alert system for telemedicine psychotherapy settings. Psychother. Res. 31, 289–299 (2021).

Article 

Google Scholar 

Peretz, G., Taylor, C. B., Ruzek, J. I., Jefroykin, S. & Sadeh-Sharvit, S. Machine learning model to predict assignment of therapy homework in behavioral treatments: Algorithm development and validation. JMIR Form. Res. 7, e45156 (2023).

Article 
PubMed 
PubMed Central 

Google Scholar 

Tanana, M. J. et al. How do you feel? Using natural language processing to automatically rate emotion in psychotherapy. Behav. Res. Methods 53, 2069–2082 (2021).

Article 
PubMed 
PubMed Central 

Google Scholar 

Sharma, A., Lin, I. W., Miner, A. S., Atkins, D. C. & Althoff, T. Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support. Nat. Mach. Intell. 5, 46–57 (2023).

Article 

Google Scholar 

Chen, Z., Flemotomos, N., Imel, Z. E., Atkins, D. C. & Narayanan, S. Leveraging open data and task augmentation to automated behavioral coding of psychotherapy conversations in low-resource scenarios. Preprint at https://doi.org/10.48550/arXiv.2210.14254 (2022).

Shah, R. S. et al. Modeling motivational interviewing strategies on an online peer-to-peer counseling platform. Proc. ACM Hum.-Comput. Interact 6, 1–24 (2022).

Article 

Google Scholar 

Chan, W. W. et al. The challenges in designing a prevention chatbot for eating disorders: Observational study. JMIR Form. Res. 6, e28003 (2022).

Article 
PubMed 
PubMed Central 

Google Scholar 

Darcy, A. Why generative AI Is not yet ready for mental healthcare. Woebot Health https://woebothealth.com/why-generative-ai-is-not-yet-ready-for-mental-healthcare/ (2023).

Abd-Alrazaq, A. A. et al. An overview of the features of chatbots in mental health: A scoping review. Int. J. Med. Inf. 132, 103978 (2019).

Article 

Google Scholar 

Lim, S. M., Shiau, C. W. C., Cheng, L. J. & Lau, Y. Chatbot-delivered psychotherapy for adults with depressive and anxiety symptoms: A systematic review and meta-regression. Behav. Ther. 53, 334–347 (2022).

Article 
PubMed 

Google Scholar 

Baumel, A., Muench, F., Edan, S. & Kane, J. M. Objective user engagement with mental health apps: Systematic search and panel-based usage analysis. J. Med. Internet Res. 21, e14567 (2019).

Article 
PubMed 
PubMed Central 

Google Scholar 

Torous, J., Nicholas, J., Larsen, M. E., Firth, J. & Christensen, H. Clinical review of user engagement with mental health smartphone apps: Evidence, theory and improvements. Evid. Based Ment. Health 21, 116–119 (2018b).

Article 
PubMed 
PubMed Central 

Google Scholar 

Das, A. et al. Conversational bots for psychotherapy: A study of generative transformer models using domain-specific dialogues. in Proceedings of the 21st Workshop on Biomedical Language Processing 285–297 (Association for Computational Linguistics, 2022). https://doi.org/10.18653/v1/2022.bionlp-1.27.

Liu, H. Towards automated psychotherapy via language modeling. Preprint at http://arxiv.org/abs/2104.10661 (2021).

Hamilton, J. Why generative AI (LLM) is ready for mental healthcare. LinkedIn https://www.linkedin.com/pulse/why-generative-ai-chatgpt-ready-mental-healthcare-jose-hamilton-md/ (2023).

Shariff, A., Bonnefon, J.-F. & Rahwan, I. Psychological roadblocks to the adoption of self-driving vehicles. Nat. Hum. Behav. 1, 694–696 (2017).

Article 
PubMed 

Google Scholar 

Markov, A. A. Essai d’une recherche statistique sur le texte du roman “Eugene Onegin” illustrant la liaison des epreuve en chain (‘Example of a statistical investigation of the text of “Eugene Onegin” illustrating the dependence between samples in chain’). Izvistia Imperatorskoi Akad. Nauk Bull. L’Academie Imp. Sci. St-Petersbourg 7, 153–162 (1913).

Google Scholar 

Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).

Article 
MathSciNet 

Google Scholar 

Baker, J. K. Stochastic modeling for automatic speech understanding. in Speech recognition: invited papers presented at the 1974 IEEE symposium (ed. Reddy, D. R.) (Academic Press, 1975).

Jelinek, F. Continuous speech recognition by statistical methods. Proc. IEEE 64, 532–556 (1976).

Article 

Google Scholar 

Jurafsky, D. & Martin, J. H. N-gram language models. in Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition (Pearson Prentice Hall, 2009).

Vaswani, A. et al. Attention is all you need. 31st Conf. Neural Inf. Process. Syst. (2017).

Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at http://arxiv.org/abs/2108.07258 (2022).

Gao, L. et al. The Pile: An 800GB dataset of diverse text for language modeling. Preprint at http://arxiv.org/abs/2101.00027 (2020).

Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: Pre-training of deep bidirectional transformers for language understanding. Preprint at http://arxiv.org/abs/1810.04805 (2019).

Kojima, T., Gu, S. S., Reid, M., Matsuo, Y. & Iwasawa, Y. Large language models are zero-shot reasoners. Preprint at http://arxiv.org/abs/2205.11916 (2023).

Fairburn, C. G. & Patel, V. The impact of digital technology on psychological treatments and their dissemination. Behav. Res. Ther. 88, 19–25 (2017).

Article 
PubMed 
PubMed Central 

Google Scholar 

Fisher, A. J. et al. Open trial of a personalized modular treatment for mood and anxiety. Behav. Res. Ther. 116, 69–79 (2019).

Article 
PubMed 

Google Scholar 

Fan, X. et al. Utilization of self-diagnosis health chatbots in real-world settings: Case study. J. Med. Internet Res. 23, e19928 (2021).

Article 
PubMed 
PubMed Central 

Google Scholar 

Coghlan, S. et al. To chat or bot to chat: Ethical issues with using chatbots in mental health. Digit. Health 9, 1–11 (2023).

Google Scholar 

Beatty, C., Malik, T., Meheli, S. & Sinha, C. Evaluating the therapeutic alliance with a free-text CBT conversational agent (Wysa): A mixed-methods study. Front. Digit. Health 4, 847991 (2022).

Article 
PubMed 
PubMed Central 

Google Scholar 

Lin, B., Bouneffouf, D., Cecchi, G. & Varshney, K. R. Towards healthy AI: Large language models need therapists too. Preprint at http://arxiv.org/abs/2304.00416 (2023).

Weidinger, L. et al. Ethical and social risks of harm from language models. Preprint at http://arxiv.org/abs/2112.04359 (2021).

Bender, E. M., Gebru, T., McMillan-Major, A. & Shmitchell, S. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency 610–623 (ACM, 2021). https://doi.org/10.1145/3442188.3445922.

Chamberlain, J. The risk-based approach of the European Union’s proposed artificial intelligence regulation: Some comments from a tort law perspective. Eur. J. Risk Regul. 14, 1–13 (2023).

Article 

Google Scholar 

Norden, J. G. & Shah, N. R. What AI in health care can learn from the long road to autonomous vehicles. NEJM Catal. Innov. Care Deliv. https://doi.org/10.1056/CAT.21.0458 (2022).

Sedlakova, J. & Trachsel, M. Conversational artificial intelligence in psychotherapy: A new therapeutic tool or agent? Am. J. Bioeth. 23, 4–13 (2023).

Article 
PubMed 

Google Scholar 

Gearing, R. E. et al. Major ingredients of fidelity: A review and scientific guide to improving quality of intervention research implementation. Clin. Psychol. Rev. 31, 79–88 (2011).

Article 
PubMed 

Google Scholar 

Wiltsey Stirman, S. Implementing evidence-based mental-health treatments: Attending to training, fidelity, adaptation, and context. Curr. Dir. Psychol. Sci. 31, 436–442 (2022).

Article 

Google Scholar 

Waller, G. Evidence-based treatment and therapist drift. Behav. Res. Ther. 47, 119–127 (2009).

Article 
PubMed 

Google Scholar 

Flemotomos, N. et al. “Am I a good therapist?” Automated evaluation of psychotherapy skills using speech and language technologies. CoRR, Abs, 2102 (10.3758) (2021).

Zhang, X. et al. You never know what you are going to get: Large-scale assessment of therapists’ supportive counseling skill use. Psychotherapy https://doi.org/10.1037/pst0000460 (2022).

Goldberg, S. B. et al. Machine learning and natural language processing in psychotherapy research: Alliance as example use case. J. Couns. Psychol. 67, 438–448 (2020).

Article 
PubMed 
PubMed Central 

Google Scholar 

Wiltsey Stirman, S. et al. A novel approach to the assessment of fidelity to a cognitive behavioral therapy for PTSD using clinical worksheets: A proof of concept with cognitive processing therapy. Behav. Ther. 52, 656–672 (2021).

Article 

Google Scholar 

Raviola, G., Naslund, J. A., Smith, S. L. & Patel, V. Innovative models in mental health delivery systems: Task sharing care with non-specialist providers to close the mental health treatment gap. Curr. Psychiatry Rep. 21, 44 (2019).

Article 
PubMed 

Google Scholar 

American Psychological Association. Guidelines for clinical supervision in health service psychology. Am. Psychol. 70, 33–46 (2015).

Article 

Google Scholar 

Cook, S. C., Schwartz, A. C. & Kaslow, N. J. Evidence-based psychotherapy: Advantages and challenges. Neurotherapeutics 14, 537–545 (2017).

Article 
PubMed 
PubMed Central 

Google Scholar 

Leichsenring, F., Steinert, C., Rabung, S. & Ioannidis, J. P. A. The efficacy of psychotherapies and pharmacotherapies for mental disorders in adults: An umbrella review and meta‐analytic evaluation of recent meta‐analyses. World Psych. 21, 133–145 (2022).

Article 

Google Scholar 

Cuijpers, P., van Straten, A., Andersson, G. & van Oppen, P. Psychotherapy for depression in adults: A meta-analysis of comparative outcome studies. J. Consult. Clin. Psychol. 76, 909–922 (2008).

Article 
PubMed 

Google Scholar 

Morris, Z. S., Wooding, S. & Grant, J. The answer is 17 years, what is the question: Understanding time lags in translational research. J. R. Soc. Med. 104, 510–520 (2011).

Article 
PubMed 
PubMed Central 

Google Scholar 

Chekroud, A. M. et al. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psych. 20, 154–170 (2021).

Article 

Google Scholar 

Kazdin, A. E. Mediators and mechanisms of change in psychotherapy research. Annu. Rev. Clin. Psychol. 3, 1–27 (2007).

Article 
PubMed 

Google Scholar 

Angelov, P. P., Soares, E. A., Jiang, R., Arnold, N. I. & Atkinson, P. M. Explainable artificial intelligence: An analytical review. WIREs Data Min. Knowl. Discov. 11, (2021).

Kelley, T. L. Interpretation of Educational Measurements. (World Book, 1927).

van Bronswijk, S. C. et al. Precision medicine for long-term depression outcomes using the Personalized Advantage Index approach: Cognitive therapy or interpersonal psychotherapy? Psychol. Med. 51, 279–289 (2021).

Article 
PubMed 

Google Scholar 

Scala, J. J., Ganz, A. B. & Snyder, M. P. Precision medicine approaches to mental health care. Physiology 38, 82–98 (2023).

Article 
CAS 

Google Scholar 

Chorpita, B. F., Daleiden, E. L. & Weisz, J. R. Identifying and selecting the common elements of evidence based interventions: A distillation and matching model. Ment. Health Serv. Res. 7, 5–20 (2005).

Article 
PubMed 

Google Scholar 

Chambless, D. L. & Hollon, S. D. Defining empirically supported therapies. J. Consult. Clin. Psychol. 66, 7–18 (1998).

Article 
CAS 
PubMed 

Google Scholar 

Tolin, D. F., McKay, D., Forman, E. M., Klonsky, E. D. & Thombs, B. D. Empirically supported treatment: Recommendations for a new model. Clin. Psychol. Sci. Pract. 22, 317–338 (2015).

Google Scholar 

Lilienfeld, S. O. Psychological treatments that cause harm. Perspect. Psychol. Sci. 2, 53–70 (2007).

Article 
PubMed 

Google Scholar 

Wasil, A. R., Venturo-Conerly, K. E., Shingleton, R. M. & Weisz, J. R. A review of popular smartphone apps for depression and anxiety: Assessing the inclusion of evidence-based content. Behav. Res. Ther. 123, 103498 (2019).

Article 
PubMed 

Google Scholar 

Torous, J. B. et al. A hierarchical framework for evaluation and informed decision making regarding smartphone apps for clinical care. Psychiatr. Serv. 69, 498–500 (2018).

Article 
PubMed 

Google Scholar 

Gunasekar, S. et al. Textbooks are all you need. Preprint at http://arxiv.org/abs/2306.11644 (2023).

Wilhelm, E. et al. Measuring the burden of infodemics: Summary of the methods and results of the Fifth WHO Infodemic Management Conference. JMIR Infodemiology 3, e44207 (2023).

Article 
PubMed 
PubMed Central 

Google Scholar 

Creed, T. A. et al. Knowledge and attitudes toward an artificial intelligence-based fidelity measurement in community cognitive behavioral therapy supervision. Adm. Policy Ment. Health Ment. Health Serv. Res. 49, 343–356 (2022).

Article 

Google Scholar 

Aktan, M. E., Turhan, Z. & Dolu, İ. Attitudes and perspectives towards the preferences for artificial intelligence in psychotherapy. Comput. Hum. Behav. 133, 107273 (2022).

Article 

Google Scholar 

Prescott, J. & Hanley, T. Therapists’ attitudes towards the use of AI in therapeutic practice: considering the therapeutic alliance. Ment. Health Soc. Incl. 27, 177–185 (2023).

Article 

Google Scholar 

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. (2013).

Yogatama, D., De Masson d’Autume, C. & Kong, L. Adaptive semiparametric language models. Trans. Assoc. Comput. Linguist 9, 362–373 (2021).

Article 

Google Scholar 

Stanley, B. & Brown, G. K. Safety planning intervention: A brief intervention to mitigate suicide risk. Cogn. Behav. Pract. 19, 256–264 (2012).

Article 

Google Scholar 

Behzadan, V., Munir, A. & Yampolskiy, R. V. A psychopathological approach to safety engineering in AI and AGI. Preprint at http://arxiv.org/abs/1805.08915 (2018).

Lambert, M. J. & Harmon, K. L. The merits of implementing routine outcome monitoring in clinical practice. Clin. Psychol. Sci. Pract. 25, (2018).

Kjell, O. N. E., Kjell, K. & Schwartz, H. A. AI-based large language models are ready to transform psychological health assessment. Preprint at https://doi.org/10.31234/osf.io/yfd8g (2023).

First, M. B., Williams, J. B. W., Karg, R. S. & Spitzer, R. L. SCID-5-CV: Structured Clinical Interview for DSM-5 Disorders: Clinician Version. (American Psychiatric Association Publishing, 2016).

Shah, D. S., Schwartz, H. A. & Hovy, D. Predictive biases in natural language processing models: A conceptual framework and overview. in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics 5248–5264 (Association for Computational Linguistics, 2020). https://doi.org/10.18653/v1/2020.acl-main.468.

Adams, L. M. & Miller, A. B. Mechanisms of mental-health disparities among minoritized groups: How well are the top journals in clinical psychology representing this work? Clin. Psychol. Sci. 10, 387–416 (2022).

Google Scholar 

Viswanath, H. & Zhang, T. FairPy: A toolkit for evaluation of social biases and their mitigation in large language models. Preprint at http://arxiv.org/abs/2302.05508 (2023).

von Zitzewitz, J., Boesch, P. M., Wolf, P. & Riener, R. Quantifying the human likeness of a humanoid robot. Int. J. Soc. Robot. 5, 263–276 (2013).

Article 

Google Scholar 

White House Office of Science and Technology Policy. Blueprint for an AI bill of rights. (2022).

Parry, G., Castonguay, L. G., Borkovec, T. D. & Wolf, A. W. Practice research networks and psychological services research in the UK and USA. in Developing and Delivering Practice-Based Evidence (eds. Barkham, M., Hardy, G. E. & Mellor-Clark, J.) 311–325 (Wiley-Blackwell, 2010). https://doi.org/10.1002/9780470687994.ch12.

Craske, M. G., Treanor, M., Conway, C. C., Zbozinek, T. & Vervliet, B. Maximizing exposure therapy: An inhibitory learning approach. Behav. Res. Ther. 58, 10–23 (2014).

Article 
PubMed 
PubMed Central 

Google Scholar 

Delgadillo, J. et al. Stratified care vs stepped care for depression: A cluster randomized clinical trial. JAMA Psychiatry 79, 101 (2022).

Article 
PubMed 

Google Scholar 

Furukawa, T. A. et al. Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression: A systematic review and component network meta-analysis using individual participant data. Lancet Psychiatry 8, 500–511 (2021).

Article 
PubMed 
PubMed Central 

Google Scholar 

Leave A Reply