Technology and mental health: The role of artificial intelligence

Lovejoy, Christopher A.

doi:10.1016/j.eurpsy.2018.08.004

Cited by 85 publications

(47 citation statements)

References 9 publications

(10 reference statements)

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“…AI can record patients' mood diaries, sleeping patterns and some other physiological conditions, even outside the clinical setting. Adequate monitoring benefits (potential) patients a lot, especially in the early detection and prevention of relapse, which have significant impacts on outcomes 17 . Sensors can collect multiple types of data such as distance travelled, variation of voice, speaking rate and voice quality, which are good predictors of symptoms of depression and post-traumatic stress disorder (PTSD) 18 .…”

Section: Ai In Mental Healthmentioning

confidence: 99%

“…Generally speaking, AI in mental healthcare can drastically reduce the burden of clinicians and therapists, especially in complicated and monotonous chores. After the re-balance of workload, clinicians can pay more attention to the interactions with patients, which improve the quality of treatments 17 . The future prospect of AI in mental healthcare will be promising, since the number of people who pursue mental health increases swiftly these years.…”

Section: Ai In Mental Healthmentioning

confidence: 99%

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Insight of Artificial Intelligence Application in Healthcare

Kuang¹

2019

ijSciences

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Ever since "Deep Blue" defeated the human champion chess player in 1997, artificial intelligence has caused extensive attention and discussion. With the development of computing and data storage technologies, artificial intelligence has been made great progress and applied in a wide range of fields. Here, we focus on several creative and novel applications of artificial intelligence in the healthcare field and also discuss their future trends.

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Section: Ai In Mental Healthmentioning

confidence: 99%

Section: Ai In Mental Healthmentioning

confidence: 99%

Insight of Artificial Intelligence Application in Healthcare

Kuang¹

2019

ijSciences

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“…Different from the diagnosis of other chronic conditions that rely on laboratory tests and measurements, mental illnesses are typically diagnosed based on an individual's self-report to specific questionnaires designed for the detection of specific patterns of feelings or social interactions 3 . Due to the increasing availability of data pertaining to an individual's mental health status, artificial intelligence (AI) and machine learning (ML) technologies are being applied to improve our understanding of mental health conditions and have been engaged to assist mental health providers for improved clinical decision-making [4][5][6] . As one of the latest advances in AI and ML, deep learning (DL), which transforms the data through layers of nonlinear computational processing units, provides a new paradigm to effectively gain knowledge from complex data 7 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning in mental health outcome research: a scoping review

et al. 2020

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Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients' historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.

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“…Cultural and social aspects could play an important role in how patients will respond to AI and therefore how effective it can prove in practice [ 57 ]. Hence, it is important to know on which basis one may define the target population that can benefit from it [ 58 ]. In this regard, the question of social acceptability (acceptable risk and public confidence) also needs to be considered, which goes beyond the simple question of the effectiveness and usability of AI [ 59 ].…”

Section: Introductionmentioning

confidence: 99%

“…For example, some AI applications can reidentify an individual from only three different data sources [ 25 , 38 , 72 ]. In the same vein, the issue of consent is becoming more complex, as patients will be asked to authorize the use of increasingly large and diversified amounts of data about them: medical records, audio, videos, and socioeconomic data [ 58 ]. Problems could arise if the patient only consents to sharing parts of his or her data.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity

Alami¹,

Lehoux²,

Auclair³

et al. 2020

J Med Internet Res

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Artificial intelligence (AI) is seen as a strategic lever to improve access, quality, and efficiency of care and services and to build learning and value-based health systems. Many studies have examined the technical performance of AI within an experimental context. These studies provide limited insights into the issues that its use in a real-world context of care and services raises. To help decision makers address these issues in a systemic and holistic manner, this viewpoint paper relies on the health technology assessment core model to contrast the expectations of the health sector toward the use of AI with the risks that should be mitigated for its responsible deployment. The analysis adopts the perspective of payers (ie, health system organizations and agencies) because of their central role in regulating, financing, and reimbursing novel technologies. This paper suggests that AI-based systems should be seen as a health system transformation lever, rather than a discrete set of technological devices. Their use could bring significant changes and impacts at several levels: technological, clinical, human and cognitive (patient and clinician), professional and organizational, economic, legal, and ethical. The assessment of AI’s value proposition should thus go beyond technical performance and cost logic by performing a holistic analysis of its value in a real-world context of care and services. To guide AI development, generate knowledge, and draw lessons that can be translated into action, the right political, regulatory, organizational, clinical, and technological conditions for innovation should be created as a first step.

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Technology and mental health: The role of artificial intelligence

Cited by 85 publications

References 9 publications

Insight of Artificial Intelligence Application in Healthcare

Insight of Artificial Intelligence Application in Healthcare

Deep learning in mental health outcome research: a scoping review

Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity

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