Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
61
result(s) for
"Whybrow, Peter C"
Sort by:
Artificial intelligence and increasing misinformation
With the recent advances in artificial intelligence (AI), patients are increasingly exposed to misleading medical information. Generative AI models, including large language models such as ChatGPT, create and modify text, images, audio and video information based on training data. Commercial use of generative AI is expanding rapidly and the public will routinely receive messages created by generative AI. However, generative AI models may be unreliable, routinely make errors and widely spread misinformation. Misinformation created by generative AI about mental illness may include factual errors, nonsense, fabricated sources and dangerous advice. Psychiatrists need to recognise that patients may receive misinformation online, including about medicine and psychiatry.
Journal Article
Reconceptualizing anhedonia: novel perspectives on balancing the pleasure networks in the human brain
Anhedonia, the lack of pleasure, has been shown to be a critical feature of a range of psychiatric disorders. Yet, it is currently measured primarily through subjective self-reports and as such has been difficult to submit to rigorous scientific analysis. New insights from affective neuroscience hold considerable promise in improving our understanding of anhedonia and for providing useful objective behavioral measures to complement traditional self-report measures, potentially leading to better diagnoses and novel treatments. Here, we review the state-of-the-art of hedonia research and specifically the established mechanisms of wanting, liking, and learning. Based on this framework we propose to conceptualize anhedonia as impairments in some or all of these processes, thereby departing from the longstanding view of anhedonia as solely reduced subjective experience of pleasure. We discuss how deficits in each of the reward components can lead to different expressions, or subtypes, of anhedonia affording novel ways of measurement. Specifically, we review evidence suggesting that patients suffering from depression and schizophrenia show impairments in wanting and learning, while some aspects of conscious liking seem surprisingly intact. Furthermore, the evidence suggests that anhedonia is heterogeneous across psychiatric disorders, depending on which parts of the pleasure networks are most affected. This in turn has implications for diagnosis and treatment of anhedonia.
Journal Article
Smartphones in mental health: a critical review of background issues, current status and future concerns
There has been increasing interest in the use of smartphone applications (apps) and other consumer technology in mental health care for a number of years. However, the vision of data from apps seamlessly returned to, and integrated in, the electronic medical record (EMR) to assist both psychiatrists and patients has not been widely achieved, due in part to complex issues involved in the use of smartphone and other consumer technology in psychiatry. These issues include consumer technology usage, clinical utility, commercialization, and evolving consumer technology. Technological, legal and commercial issues, as well as medical issues, will determine the role of consumer technology in psychiatry. Recommendations for a more productive direction for the use of consumer technology in psychiatry are provided.
Journal Article
Dynamic coupling of whole-brain neuronal and neurotransmitter systems
Remarkable progress has come from whole-brain models linking anatomy and function. Paradoxically, it is not clear how a neuronal dynamical system running in the fixed human anatomical connectome can give rise to the rich changes in the functional repertoire associated with human brain function, which is impossible to explain through long-term plasticity. Neuromodulation evolved to allow for such flexibility by dynamically updating the effectivity of the fixed anatomical connectivity. Here, we introduce a theoretical framework modeling the dynamical mutual coupling between the neuronal and neurotransmitter systems. We demonstrate that this framework is crucial to advance our understanding of wholebrain dynamics by bidirectional coupling of the two systems through combining multimodal neuroimaging data (diffusion magnetic resonance imaging [dMRI], functional magnetic resonance imaging [fMRI], and positron electron tomography [PET]) to explain the functional effects of specific serotoninergic receptor (5-HT2AR) stimulation with psilocybin in healthy humans. This advance provides an understanding of why psilocybin is showing considerable promise as a therapeutic intervention for neuropsychiatric disorders including depression, anxiety, and addiction. Overall, these insights demonstrate that the whole-brain mutual coupling between the neuronal and the neurotransmission systems is essential for understanding the remarkable flexibility of human brain function despite having to rely on fixed anatomical connectivity.
Journal Article
Survey of psychiatrist use of digital technology in clinical practice
BackgroundPsychiatrists were surveyed to obtain an overview of how they currently use technology in clinical practice, with a focus on psychiatrists who treat patients with bipolar disorder.MethodsData were obtained using an online-only survey containing 46 questions, completed by a convenience sample of 209 psychiatrists in 19 countries. Descriptive statistics, and analyses of linear associations and to remove country heterogeneity were calculated.ResultsVirtually all psychiatrists seek information online with many benefits, but some experience information overload. 75.2% of psychiatrists use an EMR/EHR at work, and 64.6% communicate with patients using a new technology, primarily email (48.8%). 66.0% do not ask patients if they use the Internet in relation to bipolar disorder. 67.3% of psychiatrists feel it is too early to tell if patient online information seeking about bipolar disorder is improving the quality of care. 66.3% of psychiatrists think technology-based treatments will improve the quality of care for some or many patients. However, 60.0% of psychiatrists do not recommend technology-based treatments to patients, and those who recommend select a variety of treatments. Psychiatrists use technology more frequently when the patients live in urban rather than rural or suburban areas. Only 23.9% of psychiatrists have any formal training in technology.ConclusionsDigital technology is routinely used by psychiatrists in clinical practice. There is near unanimous agreement about the benefits of psychiatrist online information-seeking, but research on information overload is needed. There is less agreement about the appropriate use of other clinical technologies, especially those involving patients. It is too early to tell if technology-based treatments or patient Internet activities will improve the quality of care. The digital divide remains between use of technology for psychiatrists with patients living in urban and rural or suburban areas. Psychiatrists need more formal training in technology to understand risks, benefits and limitations of clinical products.
Journal Article
Expectations for Artificial Intelligence (AI) in Psychiatry
Purpose of Review
Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine.
Recent Findings
For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI.
Summary
The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.
Journal Article
Internet of things issues related to psychiatry
BackgroundInternet of Things (IoT) devices for remote monitoring, diagnosis, and treatment are widely viewed as an important future direction for medicine, including for bipolar disorder and other mental illness. The number of smart, connected devices is expanding rapidly. IoT devices are being introduced in all aspects of everyday life, including devices in the home and wearables on the body. IoT devices are increasingly used in psychiatric research, and in the future may help to detect emotional reactions, mood states, stress, and cognitive abilities. This narrative review discusses some of the important fundamental issues related to the rapid growth of IoT devices.Main bodyArticles were searched between December 2019 and February 2020. Topics discussed include background on the growth of IoT, the security, safety and privacy issues related to IoT devices, and the new roles in the IoT economy for manufacturers, patients, and healthcare organizations. ConclusionsThe use of IoT devices will increase throughout psychiatry. The scale, complexity and passive nature of data collection with IoT devices presents unique challenges related to security, privacy and personal safety. While the IoT offers many potential benefits, there are risks associated with IoT devices, and from the connectivity between patients, healthcare providers, and device makers. Security, privacy and personal safety issues related to IoT devices are changing the roles of manufacturers, patients, physicians and healthcare IT organizations. Effective and safe use of IoT devices in psychiatry requires an understanding of these changes.
Journal Article
Commercial Use of Emotion Artificial Intelligence (AI): Implications for Psychiatry
Purpose of Review
Emotion artificial intelligence (AI) is technology for emotion detection and recognition. Emotion AI is expanding rapidly in commercial and government settings outside of medicine, and will increasingly become a routine part of daily life. The goal of this narrative review is to increase awareness both of the widespread use of emotion AI, and of the concerns with commercial use of emotion AI in relation to people with mental illness.
Recent Findings
This paper discusses emotion AI fundamentals, a general overview of commercial emotion AI outside of medicine, and examples of the use of emotion AI in employee hiring and workplace monitoring.
Summary
The successful re-integration of patients with mental illness into society must recognize the increasing commercial use of emotion AI. There are concerns that commercial use of emotion AI will increase stigma and discrimination, and have negative consequences in daily life for people with mental illness. Commercial emotion AI algorithm predictions about mental illness should not be treated as medical fact.
Journal Article