Artificial Intelligence

doi:10.1017/9781009052023.006

Chapter 5 - Artificial Intelligence

Why We Need It and Why We Need to Be Cautious

Published online by Cambridge University Press: 23 November 2023

Lesa S. Wright

Edited by

Rob Waller ,

Omer S. Moghraby and

Mark Lovell

Show author details

Rob Waller: Affiliation:
NHS Lothian
Omer S. Moghraby: Affiliation:
South London & Maudsley NHS Foundation Trust
Mark Lovell: Affiliation:
Esk and Wear Valleys NHS Foundation Trust

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Summary

The potential for artificial intelligence (AI) still attracts polarising views. There are clear benefits and dangers. The key is being aware of what these are and understanding that it is possible to utilise AI in psychiatry safely. There has to be to a will to do so as it is far more expensive to develop AI applications safely than it is to develop them at all. As shown however, there are hidden costs that make this a false economy. Ultimately, transparency about the inner workings of AI and machine learning tools makes increasing their safety realistic. It reduces the sense of magic but that is a small price to pay.

Keywords

Artificial Intelligence AI NLP Natural Language Processing Machine Learning Ethics

Information

Type: Chapter
Information: Digital Mental Health
From Theory to Practice
, pp. 60 - 71

DOI: https://doi.org/10.1017/9781009052023.006 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2023

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References

Fantasia. IMDb. 1940. Available at: www.imdb.com/title/tt0032455/?ref_=fn_al_tt_1 (accessed 3 February 2022).Google Scholar

Artificial intelligence. Cambridge Dictionary. Available at: https://dictionary.cambridge.org/dictionary/english/artificial-intelligence (accessed 1 November 2021).Google Scholar

Machine learning. Cambridge Dictionary. Available at: https://dictionary.cambridge.org/dictionary/english/machine-learning (accessed 19 November 2022).Google Scholar

Department of Health & Social Care. Accelerating Artificial Intelligence in Health and Care: Results from a State of the Nation Survey. 2018. Available at: https://wessexahsn.org.uk/img/news/AHSN%20Network%20AI%20Report-1536078823.pdf (accessed 3 February 2022).Google Scholar

Shatte, A. B. R., Hutchinson, D. M., Teague, S. J. Machine learning in mental health: a scoping review of methods and applications. Psychol. Med. 2019;49(9): 1426–48.CrossRef Google Scholar PubMed

Rosenfeld, A., Benrimoh, D., Armstrong, C. et al. Big data analytics and AI in mental healthcare. 2019. Available at: https://arxiv.org/abs/1903.12071 (accessed 2 February 2022).Google Scholar

Jones, S. S., Heaton, P. S., Rudin, R. S., Schneider, E. C. Unraveling the IT productivity paradox: lessons for health care. N. Engl. J. Med. 2012;366(24): 2243–5.CrossRef Google Scholar PubMed

The King’s Fund. Mental Health under Pressure. 2015. Available at: www.kingsfund.org.uk/sites/default/files/field/field_publication_file/mental-health-under-pressure-nov15_0.pdf (accessed 10 July 2022).Google Scholar

Abbe, A., Grouin, C., Zweigenbaum, P., Falissard, B. Text mining applications in psychiatry: a systematic literature review. Int. J. Methods Psychiatr. Res. 2016;25(2): 86–100.CrossRef Google Scholar PubMed

GPT-3. A robot wrote this entire article. Are you scared yet, human? The Guardian, 8 September 2020. Available at: www.theguardian.com/commentisfree/2020/sep/08/robot-wrote-this-article-gpt–3 (accessed 21 July 2021).Google Scholar

Smith, A. Microsoft patent shows plans to revive dead loved ones as chatbots. Independent, 20 January 2021. Available at: www.independent.co.uk/life-style/gadgets-and-tech/microsoft-chatbot-patent-dead-b1789979.html (accessed 3 February 2022).Google Scholar

Fitzpatrick, K. K., Darcy, A., Vierhile, M. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR Ment. Health. 2017;4(2): e19.CrossRef Google Scholar PubMed

Inkster, B., Sarda, S., Subramanian, V. An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR mHealth and uHealth. 2018;6(11): e12106.CrossRef Google Scholar PubMed

Luxton, D. D. Artificial Intelligence in Behavioral and Mental Health Care. 1st ed. Cambridge, MA: Academic Press. 2015.Google Scholar

Mehltretter, J., Fratila, R., Benrimoh, D. A. et al. Differential treatment benefit prediction for treatment selection in depression: a deep learning analysis of STAR*D and CO-MED data. Comput. Psychiatry. 2020;4(0): 61.CrossRef Google Scholar

Walsh, C. G., Ribeiro, J. D., Franklin, J. C. Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning. J. Child Psychol. Psychiatry. 2018;59(12): 1261–70.CrossRef Google Scholar PubMed

Rumshisky, A., Ghassemi, M., Naumann, T. et al. Predicting early psychiatric readmission with natural language processing of narrative discharge summaries. Transl. Psychiatry. 2016;6(10): e921.CrossRef Google Scholar PubMed

Banoula, M. Bias and variance in machine learning: an in depth explanation. Simplilearn. 2021. Available at: www.simplilearn.com/tutorials/machine-learning-tutorial/bias-and-variance (accessed 25 September 2021).Google Scholar

Jones, T. Machine learning and bias. IBM Developer. 2019. Available at: https://developer.ibm.com/articles/machine-learning-and-bias/ (accessed 25 September 2021).Google Scholar

Obermeyer, Z., Powers, B., Vogeli, C., Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464): 447–53.Google Scholar

Gov.UK. Detentions under the Mental Health Act. 2021. Available at: www.ethnicity-facts-figures.service.gov.uk/health/mental-health/detentions-under-the-mental-health-act/latest (accessed 3 February 2022).Google Scholar

Olbert, C. M., Nagendra, A., Buck, B. Meta-analysis of Black vs. White racial disparity in schizophrenia diagnosis in the United States: do structured assessments attenuate racial disparities? J. Abnorm. Psychol. 2018;127(1): 104–15.CrossRef Google Scholar

Sansone, R. A., Sansone, L. A. Gender patterns in borderline personality disorder. Innov. Clin. Neurosci. 2011;8(5): 16–20.Google Scholar PubMed

Dalgleish, T., Power, M. Handbook of Cognition and Emotion. 1st ed. Chichester: Wiley. 2007.Google Scholar

Chen, L., Ma, X., Zhu, N. et al. Facial expression recognition with machine learning and assessment of distress in patients with cancer. Oncol. Nurs. Forum. 2021;48(1): 81–93.CrossRef Google Scholar PubMed

Buolamwini, J., Gebru, T. Gender shades: intersectional accuracy disparities in commercial gender classification. In Sorelle, A., Friedler, C. W., eds, Proceedings of the 1st Conference on Fairness, Accountability and Transparency. New York: Proceedings of Machine Learning Research. 2018. pp. 77–91.Google Scholar

BBC News. IBM abandons ‘biased’ facial recognition tech. 9 June 2020. Available at: www.bbc.co.uk/news/technology-52978191 (accessed 23 November 2021).Google Scholar

Murgia, M. Microsoft quietly deletes largest public face recognition data set. Financial Times, 6 June 2019. Available at: www.ft.com/content/7d3e0d6a-87a0-11e9-a028-86cea8523dc2 (accessed 23 November 2022).Google Scholar

Jin, B., Qu, Y., Zhang, L., Gao, Z. Diagnosing Parkinson disease through facial expression recognition: video analysis. J. Med. Internet Res. 2020;22(7): e18697.CrossRef Google Scholar PubMed

Orme, J. Samaritans pulls ‘suicide watch’ Radar app over privacy concerns. The Guardian, 7 November 2014. Available at: www.theguardian.com/society/2014/nov/07/samaritans-radar-app-suicide-watch-privacy-twitter-users (accessed 14 July 2021).Google Scholar

Lee, D. Tay: Microsoft issues apology over racist chatbot fiasco. BBC News, 25 March 2016. Available at: www.bbc.co.uk/news/technology-35902104 (accessed 5 February 2022).Google Scholar

Mellor, D. What are the emerging risks of AI? Swiss Re, 3 December 2020. Available at: https://corporatesolutions.swissre.com/insights/knowledge/emerging-risks-of-AI.html (accessed 3 February 2022).Google Scholar

Cahn, A. F. A human wrote this article. You shouldn’t be scared of GPT-3. The Guardian, 12 September 2020. Available at: www.theguardian.com/commentisfree/2020/sep/12/human-wrote-this-article-gpt–3 (accessed 21 July 2021).Google Scholar

Strubell, E., Ganesh, A., McCallum, A. Energy and policy considerations for deep learning in NLP. 2019 Jun 5 arXiv preprint arXiv:1906.02243.CrossRef Google Scholar

Wynants, L., van Calster, B., Collins, G. S. et al. Prediction models for diagnosis and prognosis of Covid-19: systematic review and critical appraisal. BMJ. 2020;7: m1328.Google Scholar

Mongan, J., Moy, L., Kahn, C. E. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers. Radiol. Artif. Intell. 2020;2(2): e200029.CrossRef Google Scholar PubMed

Roberts, M., Driggs, D., Thorpe, M. et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat. Mach. Intell. 2021;3(3): 199–217.CrossRef Google Scholar

ICO. Guide to the UK General Data Protection Regulation (UK GDPR). n.d. Available at: https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/ (accessed 10 July 2021).Google Scholar

Health Insurance Portability and Accountability Act of 1996 (HIPAA). CDC. Available at: www.cdc.gov/phlp/publications/topic/hipaa.html (accessed 3 February 2022).Google Scholar

ICO. The Information Commissioner’s Office response to the Department for Digital, Culture, Media and Sport’s consultation on the National Data Strategy. n.d. Available at: https://ico.org.uk/media/about-the-ico/consultation-responses/2618963/ico-response-national-data-strategy-consultation.pdf (accessed 3 February 2022).Google Scholar

Moody, G. Detailed medical records of 61 million Italian citizens to be given to IBM for its ‘cognitive computing’ system Watson. LaptrinhX, 23 May 2017. Available at: https://laptrinhx.com/detailed-medical-records-of-61-million-italian-citizens-to-be-given-to-ibm-for-its-cognitive-computing-system-watson-3962904859/ (accessed 3 February 2022).Google Scholar

Carding, N. Regulator reveals ‘concerns’ over Babylon’s ‘chatbot’. HSJ. 2021. Available at: www.hsj.co.uk/technology-and-innovation/regulator-reveals-concerns-over-babylons-chatbot/7029598.article? (accessed 3 February 2023).Google Scholar

Iacobucci, G. Row over Babylon’s chatbot shows lack of regulation. BMJ. 2020;368: m815.CrossRef Google Scholar

Medicines and Healthcare products Regulatory Agency. Software and AI as a Medical Device Change Programme. 2021. Available at: www.gov.uk/government/publications/software-and-ai-as-a-medical-device-change-programme/software-and-ai-as-a-medical-device-change-programme (accessed 3 February 2022).Google Scholar

Grainger, D. Guidance: Medical device stand-alone software including apps (including IVDMDs) v1.08. Available at: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/999908/Software_flow_chart_Ed_1-08b-IVD.pdf (accessed 3 February 2022).Google Scholar

National Institute for Health and Care Excellence (NICE). Evidence standards framework for digital health technologies. Available at: www.nice.org.uk/about/what-we-do/our-programmes/evidence-standards-framework-for-digital-health-technologies (accessed 17 March 2022).Google Scholar

Cruz Rivera, S., Liu, X., Chan, A-W. et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Lancet Digit. Health. 2020;2(10): e549–60.Google Scholar

Liu, X., Rivera, S. C., Moher, D., Calvert, M. J., Denniston, A. K. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ. 2020;370: m3164.CrossRef Google Scholar PubMed

Collins, G. S., Reitsma, J. B., Altman, D. G., Moons, K. G. M. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD statement. Br. J. Surg. 2015;102(3): 148–58.CrossRef Google Scholar PubMed

Moons, K. G. M., Wolff, R. F., Riley, R. D. et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann. Intern. Med. 2019;170(1): W1.CrossRef Google Scholar PubMed

Whiting, P. F. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann. Intern. Med. 2011;155(8): 529.Google Scholar

Department of Health & Social Care. A Guide to Good Practice for Digital and Data-Driven Health Technologies. 2021. Available at: www.gov.uk/government/publications/code-of-conduct-for-data-driven-health-and-care-technology/initial-code-of-conduct-for-data-driven-health-and-care-technology (accessed 3 February 2022).Google Scholar

ICO. Explaining decisions made with AI. n.d. Available at: https://ico.org.uk/for-organisations/guide-to-data-protection/key-dp-themes/explaining-decisions-made-with-artificial-intelligence/ (accessed 3 February 2022).Google Scholar

PAIR-code/what-if-tool: source code/webpage/demos for the What-If Tool. Available at: https://github.com/pair-code/what-if-tool (accessed 3 February 2022).Google Scholar

Trusted-AI/AIF360: a comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models. Available at: https://github.com/Trusted-AI/AIF360 (accessed 3 February 2022).Google Scholar

marcotcr/lime: Lime: explaining the predictions of any machine learning classifier. Available at: https://github.com/marcotcr/lime (accessed 3 February 2022).Google Scholar

adebayoj/fairml. Available at: https://github.com/adebayoj/fairml (accessed 3 February 2022).Google Scholar

OpenAI. Safety best practices – OpenAI API. n.d. Available at: https://beta.openai.com/docs/safety-best-practices (accessed 21 July 2021).Google Scholar

Devlin, J., Chang, M-W., Lee, K., Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.Google Scholar

Li, Y., Rao, S., Solares, J. R. A. et al. BEHRT: transformer for electronic health records. Sci. Rep. 2020;10(1): 7155.CrossRef Google Scholar PubMed

Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., Tsaneva-Atanasova, K. Artificial intelligence, bias and clinical safety. BMJ Qual. Saf. 2019;28(3): 231–7.CrossRef Google Scholar PubMed

AI Incident Database. Welcome to the Artificial Intelligence Incident Database. n.d. Available at: https://incidentdatabase.ai/?lang=en (accessed 3 February 2022).Google Scholar

Rock Health. 2021 year-end digital health funding: seismic shifts beneath the surface. 10 January 2022. Available at: https://rockhealth.com/insights/2021-year-end-digital-health-funding-seismic-shifts-beneath-the-surface/ (accessed 3 February 2022).Google Scholar

AI for Good. n.d. Available at: www.aiforgood.co.uk/about-us (accessed 3 February 2022).Google Scholar

The DAIR Institute. Available at: www.dair-institute.org/ (accessed 3 February 2022).Google Scholar

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