Towards personalised predictive psychiatry in clinical practice: an ethical perspective

Lane, Natalie M; Broome, Matthew R.

doi:10.1192/bjp.2022.37

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…10 Given the desire to promote the well-being of their patients, some clinicians may perceive an epistemic obligation to align their clinical judgements with the algorithmic outputs in the interest of high-quality evidence-based decision-making. 11 12 The hope is that AI and digital technologies will help promote improved access to treatment and quality of care. 13 Early work has focused on tools like conversational AI (ie, chatbots) to provide cognitive behavioural therapy and more integrated digital care delivery systems, both of which remain in their infancy and have been met with challenges with uptake and implementation.…”

Section: Introductionmentioning

confidence: 99%

Evidence, ethics and the promise of artificial intelligence in psychiatry

2022

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Researchers are studying how artificial intelligence (AI) can be used to better detect, prognosticate and subgroup diseases. The idea that AI might advance medicine’s understanding of biological categories of psychiatric disorders, as well as provide better treatments, is appealing given the historical challenges with prediction, diagnosis and treatment in psychiatry. Given the power of AI to analyse vast amounts of information, some clinicians may feel obligated to align their clinical judgements with the outputs of the AI system. However, a potential epistemic privileging of AI in clinical judgements may lead to unintended consequences that could negatively affect patient treatment, well-being and rights. The implications are also relevant to precision medicine, digital twin technologies and predictive analytics generally. We propose that a commitment to epistemic humility can help promote judicious clinical decision-making at the interface of big data and AI in psychiatry.

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Section: Introductionmentioning

confidence: 99%

Evidence, ethics and the promise of artificial intelligence in psychiatry

2022

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“…Previous studies have also demonstrated that using statistical modelling of patients' medical health records to improve the identification of certain mental health problems (most commonly using ‘deep learning’ – a form of artificial intelligence) has been effective in identifying mental health problems (Pham et al, 2017 ; Su et al, 2020 ). However, it is important to be aware of ethical implications of such prediction models as they can undermine patients' and clinicians' sense of agency, and shared decision making (Lane & Broome, 2022 ). Finally, previous research also identified systemic barriers related to the early identification of mental health problems in primary care, such as limited consultation time and long waiting times for specialist services (e.g., O'Brien et al, 2016 ), indicating the need for systemic changes in primary care.…”

Section: Resultsmentioning

confidence: 99%

Identifying individuals at risk of developing psychosis: A systematic review of the literature in primary care services

Radež

Izon

Johns

2023

Early Intervention Psych

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Aim: Psychosis and related disorders are a major public health issue. Early identification and prevention for those at high risk (at-risk-mental-state, ARMS) is important.General practitioners (GPs) are often the first point of contact for health services. In this review we aim to identify (1) the most common methods for identifying individuals with an ARMS in primary care, (2) the methods for improving identification of individuals with an ARMS in primary care, and (3) the most common barriers that prevent GPs from screening for individuals with an ARMS.Methods: We conducted a systematic review (PROSPERO 42021245095) of quantitative and qualitative studies with no date restriction. Searches were performed in September 2021. Studies' quality was appraised using Mixed Methods Appraisal tool (MMAT).Results: We identified 16 eligible studies, and all but one provided quantitative data.Nearly two-thirds of studies were classified as 'medium' quality. Employing narrative synthesis, we identified three themes relating to (1) improving GP knowledge and confidence in identifying individuals with an ARMS, (2) balancing the over-and underidentification of individuals with an ARMS in primary care, and (3) supporting GPs as significant stakeholders in early diagnosis and treatment of individuals with an ARMS.Conclusions: Improved identification of individuals with an ARMS is needed. We identified various strategies, including development and implementation of identification methods (e.g., screening measures), educational interventions for GPs (e.g., workshops), and systemic interventions (e.g., simplifying referrals to secondary care, developing integrated services). When implemented successfully, these interventions may help facilitate the access to appropriate care for individuals with an ARMS.

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“…However, obstacles, including issues of accuracy, practical feasibility and ethical acceptability, remain. 32…”

Section: Future Directionsmentioning

confidence: 99%

Bipolar disorder: Diagnosis, treatment and future directions

Lane,

Smith

2023

Journal of the Royal College of Physicians of Edinburgh

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Bipolar disorder is a relatively common mental illness, characterised by recurrent episodes of mania (or hypomania) and major depression, and associated with a significant burden of morbidity and premature mortality. Physicians across all specialities are likely to encounter individuals with the condition within their clinical practice. This short review provides an up-to-date overview of the clinical features, epidemiology, pathophysiology, evidence-based management, prognosis and future directions for treatment and research in bipolar disorder. Aspects of cross-specialty relevance are highlighted, including the physical health burden associated with the condition, and the side effects and safety considerations of medication regimes used in bipolar disorder.

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Towards personalised predictive psychiatry in clinical practice: an ethical perspective

Cited by 8 publications

References 5 publications

Evidence, ethics and the promise of artificial intelligence in psychiatry

Evidence, ethics and the promise of artificial intelligence in psychiatry

Identifying individuals at risk of developing psychosis: A systematic review of the literature in primary care services

Bipolar disorder: Diagnosis, treatment and future directions

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