Generative AI

Behavioral health and generative AI: a perspective on future of therapies and patient care


  • Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. AI in health and medicine. Nat. Med. 28, 31–38 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Nazarian, S., Glover, B., Ashrafian, H., Darzi, A. & Teare, J. Diagnostic accuracy of artificial intelligence and computer-aided diagnosis for the detection and characterization of colorectal polyps: systematic review and meta-analysis. J. Med. Internet Res. 23, e27370 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Aggarwal, R. et al. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digit. Med. 4, 65 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, C., Zhu, X., Hong, J. C. & Zheng, D. Artificial intelligence in radiotherapy treatment planning: present and future. Technol. Cancer Res. Treat. 18, 1533033819873922 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhou, Q., Chen, Z.-H., Cao, Y.-H. & Peng, S. Clinical impact and quality of randomized controlled trials involving interventions evaluating artificial intelligence prediction tools: a systematic review. NPJ Digit. Med. 4, 154 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ho, D. et al. Enabling technologies for personalized and precision medicine. Trends Biotechnol. 38, 497–518 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bae, S. W. et al. Leveraging mobile phone sensors, machine learning, and explainable artificial intelligence to predict imminent same-day binge-drinking events to support just-in-time adaptive interventions: algorithm development and validation study. JMIR Form. Res 7, e39862 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Paul, D. et al. Artificial intelligence in drug discovery and development. Drug Discov. Today 26, 80–93 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kuziemsky, C. et al. Role of Artificial Intelligence within the Telehealth Domain. Yearb. Med. Inform. 28, 35–40 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sujith, A. V. L. N., Sajja, G. S., Mahalakshmi, V., Nuhmani, S. & Prasanalakshmi, B. Systematic review of smart health monitoring using deep learning and artificial intelligence. Neurosci. Inform. 2, 100028 (2022).

    Article 

    Google Scholar
     

  • Ho, J., Jain, A. & Abbeel, P. Denoising diffusion probabilistic models. Adv. Neural Inf. Process. Syst. 33, 6840–6851 (2020).


    Google Scholar
     

  • Goodfellow, I. et al. Generative adversarial networks. Commun. ACM 63, 139–144 (2020).

    Article 

    Google Scholar
     

  • Vaswani, A. et al. Attention is all you need. In: Advances in neural information processing systems 5998–6008 (2017).

  • Thirunavukarasu, A. J. et al. Large language models in medicine. Nat. Med. 29, 1930–1940 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Huang, K. et al. Artificial intelligence foundation for therapeutic science. Nat. Chem. Biol. 18, 1033–1036 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • D’Amico, S. et al. Synthetic data generation by artificial intelligence to accelerate research and precision medicine in hematology. JCO Clin. Cancer Inf. 7, e2300021 (2023).

    Article 

    Google Scholar
     

  • Zhao, J., Hou, X., Pan, M. & Zhang, H. Attention-based generative adversarial network in medical imaging: a narrative review. Comput. Biol. Med. 149, 105948 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Liu, Y. et al. CBCT-based synthetic CT generation using deep-attention cycleGAN for pancreatic adaptive radiotherapy. Med. Phys. 47, 2472–2483 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Koohi-Moghadam, M. & Bae, K. T. Generative AI in medical imaging: applications, challenges, and ethics. J. Med. Syst. 47, 94 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Acosta, J. N., Falcone, G. J., Rajpurkar, P. & Topol, E. J. Multimodal biomedical AI. Nat. Med. 28, 1773–1784 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bommasani, R. et al. On the opportunities and risks of foundation models. arXiv https://arxiv.org/abs/2108.07258 (2021).

  • Bond-Taylor, S., Leach, A., Long, Y. & Willcocks, C. G. Deep generative modelling: a comparative review of VAEs, GANs, normalizing flows, energy-based and autoregressive models. IEEE Trans. Pattern Anal. Mach. Intell. 44, 7327–7347 (2022).

    Article 
    PubMed 

    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
     

  • van Heerden, A. C., Pozuelo, J. R. & Kohrt, B. A. Global mental health services and the impact of artificial intelligence-powered large language models. JAMA Psychiatry 80, 662–664 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Pandey, S. & Sharma, S. A comparative study of retrieval-based and generative-based chatbots using deep learning and machine learning. Healthc. Analytics 3, 100198 (2023).

    Article 

    Google Scholar
     

  • Bird, J. J. & Lotfi, A. Generative transformer chatbots for mental health support: a study on depression and anxiety. In: Proceedings of the 16th international conference on pervasive technologies related to assistive environments, 475–479 (Association for Computing Machinery, 2023). https://doi.org/10.1145/3594806.3596520.

  • Sezgin, E., Chekeni, F., Lee, J. & Keim, S. Clinical accuracy of large language models and google search responses to postpartum depression questions: cross-sectional study. J. Med. Internet Res. 25, e49240 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schueller, S. M. & Morris, R. R. Clinical science and practice in the age of large language models and generative artificial intelligence. J. Consult. Clin. Psychol. 91, 559–561 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Pataranutaporn, P. et al. AI-generated characters for supporting personalized learning and well-being. Nat. Mach. Intell. 3, 1013–1022 (2021).

    Article 

    Google Scholar
     

  • Nye, A., Delgadillo, J. & Barkham, M. Efficacy of personalized psychological interventions: a systematic review and meta-analysis. J. Consult. Clin. Psychol. 91, 389–397 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Cronbach, L. J. & Snow, R. E. Aptitudes and instructional methods: a handbook for research on interactions. 574, (1977).

  • Zhong, Y. et al. The issue of evidence-based medicine and artificial intelligence. Asian J. Psychiatr. 85, 103627 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J. & Fernández-Leal, Á. Human-in-the-loop machine learning: a state of the art. Artif. Intell. Rev. 56, 3005–3054 (2023).

    Article 

    Google Scholar
     

  • Park, S. Y. et al. Identifying challenges and opportunities in human-AI collaboration in healthcare. In: Conference companion publication of the 2019 on computer supported cooperative work and social computing, 506–510 (Association for Computing Machinery, 2019). https://doi.org/10.1145/3311957.3359433.

  • Sezgin, E. Artificial intelligence in healthcare: complementing, not replacing, doctors and healthcare providers. Digit Health 9, 20552076231186520 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhong, Y. et al. The Artificial intelligence large language models and neuropsychiatry practice and research ethic. Asian J. Psychiatr. 84, 103577 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Holmes, E. A., Arntz, A. & Smucker, M. R. Imagery rescripting in cognitive behaviour therapy: images, treatment techniques and outcomes. J. Behav. Ther. Exp. Psychiatry 38, 297–305 (2007).

    Article 
    PubMed 

    Google Scholar
     

  • Lang, P. J. Imagery in therapy: an information processing analysis of fear. Behav. Ther. 8, 862–886 (1977).

    Article 

    Google Scholar
     

  • Hafner, C., Schneider, J., Schindler, M. & Braillard, O. Visual aids in ambulatory clinical practice: experiences, perceptions and needs of patients and healthcare professionals. PLoS One 17, e0263041 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Boland, L. et al. An experimental investigation of the effects of perspective-taking on emotional discomfort, cognitive fusion and self-compassion. J. Context. Behav. Sci. 20, 27–34 (2021).

    Article 

    Google Scholar
     

  • Beck, A. T. Thinking and depression. II. Theory and therapy. Arch. Gen. Psychiatry 10, 561–571 (1964).

  • Brasfield, C. Cognitive-behavioral treatment of borderline personality disorder. Behav. Res. Ther. 32, 899 (1994).

    Article 

    Google Scholar
     

  • Hayes, S. C., Strosahl, K. D. & Wilson, K. G. Acceptance and commitment therapy: an experiential approach to behavior change. (Guilford Publications, 1999).

  • Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Christens, B. D., Collura, J. J. & Tahir, F. Critical hopefulness: a person-centered analysis of the intersection of cognitive and emotional empowerment. Am. J. Community Psychol. 52, 170–184 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • Kotsou, I., Mikolajczak, M., Heeren, A., Grégoire, J. & Leys, C. Improving emotional intelligence: a systematic review of existing work and future challenges. Emot. Rev. 11, 151–165 (2019).

    Article 

    Google Scholar
     

  • Lewis, G. J., Lefevre, C. E. & Young, A. W. Functional architecture of visual emotion recognition ability: a latent variable approach. J. Exp. Psychol. Gen. 145, 589–602 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Brackett, M. A., Rivers, S. E. & Salovey, P. Emotional intelligence: implications for personal, social, academic, and workplace success. Soc. Personal. Psychol. Compass 5, 88–103 (2011).

    Article 

    Google Scholar
     

  • Morie, K. P., Crowley, M. J., Mayes, L. C. & Potenza, M. N. The process of emotion identification: considerations for psychiatric disorders. J. Psychiatr. Res. 148, 264–274 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bobek, E. & Tversky, B. Creating visual explanations improves learning. Cogn. Res Princ. Implic. 1, 27 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cheng, V. W. S., Davenport, T., Johnson, D., Vella, K. & Hickie, I. B. Gamification in apps and technologies for improving mental health and well-being: systematic review. JMIR Ment. Health 6, e13717 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xie, H. A scoping review of gamification for mental health in children: uncovering its key features and impact. Arch. Psychiatr. Nurs. 41, 132–143 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Nicolaidou, I., Aristeidis, L. & Lambrinos, L. A gamified app for supporting undergraduate students’ mental health: a feasibility and usability study. Digit Health 8, 20552076221109059 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • stabilityai/stable-diffusion-2-1 · Hugging face. https://huggingface.co/stabilityai/stable-diffusion-2-1.

  • Stanton, A. L. & Low, C. A. Expressing emotions in stressful contexts: benefits, moderators, and mechanisms. Curr. Dir. Psychol. Sci. 21, 124–128 (2012).

    Article 

    Google Scholar
     

  • CSEFEL: center on the social and emotional foundations for early learning. https://csefel.vanderbilt.edu/resources/family.html.

  • Gross, J. J. The emerging field of emotion regulation: an integrative review. Rev. Gen. Psychol. 2, 271–299 (1998).

    Article 

    Google Scholar
     

  • Moltrecht, B., Deighton, J., Patalay, P. & Edbrooke-Childs, J. Effectiveness of current psychological interventions to improve emotion regulation in youth: a meta-analysis. Eur. Child Adolesc. Psychiatry 30, 829–848 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Tang, W. & Kreindler, D. Supporting homework compliance in cognitive behavioural therapy: essential features of mobile apps. JMIR Ment. Health 4, e20 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Azuaje, G. et al. Exploring the use of AI text-to-image generation to downregulate negative emotions in an expressive writing application. R. Soc. Open Sci. 10, 220238 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vial, T. & Almon, A. Artificial intelligence in mental health therapy for children and adolescents. JAMA Pediatr. 177, 1251–1252 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Harvey, W. & Wood, F. Visual chain-of-thought diffusion models. arXiv https://arxiv.org/abs/2303.16187 (2023).

  • Weisz, J. D. et al. Design principles for generative AI applications. arXiv https://arxiv.org/html/2401.14484v1 (2024).

  • Cachat-Rosset, G. & Klarsfeld, A. Diversity, equity, and inclusion in artificial intelligence: an evaluation of guidelines. Appl. Artif. Intell. 37, 2176618 (2023).

    Article 

    Google Scholar
     

  • Lee, E. E. et al. Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 6, 856–864 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sezgin, E., Sirrianni, J. & Linwood, S. L. Operationalizing and implementing pretrained, large artificial intelligence linguistic models in the US health care system: outlook of generative pretrained transformer 3 (GPT-3) as a service model. JMIR Med. Inf. 10, e32875 (2022).

    Article 

    Google Scholar
     

  • Timmons, A. C. et al. A call to action on assessing and mitigating bias in artificial intelligence applications for mental health. Perspect. Psychol. Sci. 18, 1062–1096 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Boch, S., Sezgin, E. & Lin Linwood, S. Ethical artificial intelligence in paediatrics. Lancet Child Adolesc. Health 6, 833–835 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Interim guidance on government use of public generative AI tools – November 2023. https://architecture.digital.gov.au/guidance-generative-ai.

  • European Parliament-SpokespersonGuillot, J. D. EU AI Act: first regulation on artificial intelligence. https://www.europarl.europa.eu/pdfs/news/expert/2023/6/story/20230601STO93804/20230601STO93804_en.pdf (2023).

  • International community must urgently confront new reality of generative, artificial intelligence, speakers stress as Security Council debates risks, rewards. https://press.un.org/en/2023/sc15359.doc.htm (2023).

  • The White House. Executive order on the safe, secure, and trustworthy development and use of artificial intelligence. The White House https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/ (2023).

  • Gomez Rossi, J., Rojas-Perilla, N., Krois, J. & Schwendicke, F. Cost-effectiveness of artificial intelligence as a decision-support system applied to the detection and grading of melanoma, dental caries, and diabetic retinopathy. JAMA Netw. Open 5, e220269 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hendrix, N., Veenstra, D. L., Cheng, M., Anderson, N. C. & Verguet, S. Assessing the economic value of clinical artificial intelligence: challenges and opportunities. Value Health 25, 331–339 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Allen, M. R. et al. Navigating the doctor-patient-AI relationship – a mixed-methods study of physician attitudes toward artificial intelligence in primary care. BMC Prim. Care 25, 42 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mirsky, Y. & Lee, W. The creation and detection of deepfakes: a survey. ACM Comput. Surv. 54, 1–41 (2021).

    Article 

    Google Scholar
     

  • Brundage, M. et al. The malicious use of artificial intelligence: forecasting, prevention, and mitigation. arXiv https://arxiv.org/ftp/arxiv/papers/1802/1802.07228.pdf (2018).

  • Janssen, M., Brous, P., Estevez, E., Barbosa, L. S. & Janowski, T. Data governance: organizing data for trustworthy artificial intelligence. Gov. Inf. Q. 37, 101493 (2020).

    Article 

    Google Scholar
     

  • Tanner, B. A. Validity of global physical and emotional SUDS. Appl. Psychophysiol. Biof. 37, 31–34 (2012).

    Article 

    Google Scholar
     



  • Source

    Related Articles

    Back to top button