Hasil untuk "Psychiatry"

A. Caspi, T. Moffitt

P. Shrout

B. Sadock, H. Kaplan, V. A. Sadock

Yang Ni, Fanli Jia

Artificial intelligence (AI)-enabled digital interventions, including Generative AI (GenAI) and Human-Centered AI (HCAI), are increasingly used to expand access to digital psychiatry and mental health care. This PRISMA-ScR scoping review maps the landscape of AI-driven mental health (mHealth) technologies across five critical phases: pre-treatment (screening/triage), treatment (therapeutic support), post-treatment (remote patient monitoring), clinical education, and population-level prevention. We synthesized 36 empirical studies implemented through early 2024, focusing on Large Language Models (LLMs), machine learning (ML) models, and autonomous conversational agents. Key use cases involve referral triage, empathic communication enhancement, and AI-assisted psychotherapy delivered via chatbots and voice agents. While benefits include reduced wait times and increased patient engagement, we address recurring challenges like algorithmic bias, data privacy, and human-AI collaboration barriers. By introducing a novel four-pillar framework, this review provides a comprehensive roadmap for AI-augmented mental health care, offering actionable insights for researchers, clinicians, and policymakers to develop safe, effective, and equitable digital health interventions.

Moiz Sadiq Awan, Maryam Raza

Prior authorization remains one of the most burdensome administrative processes in U.S. healthcare, consuming billions of dollars and thousands of physician hours each year. While large language models have shown promise across clinical text tasks, their ability to produce submission-ready prior authorization letters has received only limited attention, with existing work confined to single-case demonstrations rather than structured multi-scenario evaluation. We assessed three commercially available LLMs (GPT-4o, Claude Sonnet 4.5, and Gemini 2.5 Pro) across 45 physician-validated synthetic scenarios spanning rheumatology, psychiatry, oncology, cardiology, and orthopedics. All three models generated letters with strong clinical content: accurate diagnoses, well-structured medical necessity arguments, and thorough step therapy documentation. However, a secondary analysis of real-world administrative requirements revealed consistent gaps that clinical scoring alone did not capture, including absent billing codes, missing authorization duration requests, and inadequate follow-up plans. These findings reframe the question: the challenge for clinical deployment is not whether LLMs can write clinically adequate letters, but whether the systems built around them can supply the administrative precision that payer workflows require.

S. Khalsa, Rachel C Lapidus

Disrupted interoception is a prominent feature of the diagnostic classification of several psychiatric disorders. However, progress in understanding the interoceptive basis of these disorders has been incremental, and the application of interoception in clinical treatment is currently limited to panic disorder. To examine the degree to which the scientific community has recognized interoception as a construct of interest, we identified and individually screened all articles published in the English language on interoception and associated root terms in Pubmed, Psychinfo, and ISI Web of Knowledge. This search revealed that interoception is a multifaceted process that is being increasingly studied within the fields of psychiatry, psychology, neuroscience, and biomedical science. To illustrate the multifaceted nature of interoception, we provide a focused review of one of the most commonly studied interoceptive channels, the cardiovascular system, and give a detailed comparison of the most popular methods used to study cardiac interoception. We subsequently review evidence of interoceptive dysfunction in panic disorder, depression, somatic symptom disorders, anorexia nervosa, and bulimia nervosa. For each disorder, we suggest how interoceptive predictions constructed by the brain may erroneously bias individuals to express key symptoms and behaviors, and outline questions that are suitable for the development of neuroscience-based mental health interventions. We conclude that interoception represents a viable avenue for clinical and translational research in psychiatry, with a well-established conceptual framework, a neural basis, measurable biomarkers, interdisciplinary appeal, and transdiagnostic targets for understanding and improving mental health outcomes.

Liam G. McCoy, Fateme Nateghi Haredasht, Kanav Chopra et al.

This study evaluates the capacity of large language models (LLMs) to generate structured clinical consultation templates for electronic consultation. Using 145 expert-crafted templates developed and routinely used by Stanford's eConsult team, we assess frontier models -- including o3, GPT-4o, Kimi K2, Claude 4 Sonnet, Llama 3 70B, and Gemini 2.5 Pro -- for their ability to produce clinically coherent, concise, and prioritized clinical question schemas. Through a multi-agent pipeline combining prompt optimization, semantic autograding, and prioritization analysis, we show that while models like o3 achieve high comprehensiveness (up to 92.2\%), they consistently generate excessively long templates and fail to correctly prioritize the most clinically important questions under length constraints. Performance varies across specialties, with significant degradation in narrative-driven fields such as psychiatry and pain medicine. Our findings demonstrate that LLMs can enhance structured clinical information exchange between physicians, while highlighting the need for more robust evaluation methods that capture a model's ability to prioritize clinically salient information within the time constraints of real-world physician communication.

Junjie Luo, Rui Han, Arshana Welivita et al.

Understanding how patients perceive their physicians is essential to improving trust, communication, and satisfaction. We present a large language model (LLM)-based pipeline that infers Big Five personality traits and five patient-oriented subjective judgments. The analysis encompasses 4.1 million patient reviews of 226,999 U.S. physicians from an initial pool of one million. We validate the method through multi-model comparison and human expert benchmarking, achieving strong agreement between human and LLM assessments (correlation coefficients 0.72-0.89) and external validity through correlations with patient satisfaction (r = 0.41-0.81, all p<0.001). National-scale analysis reveals systematic patterns: male physicians receive higher ratings across all traits, with largest disparities in clinical competence perceptions; empathy-related traits predominate in pediatrics and psychiatry; and all traits positively predict overall satisfaction. Cluster analysis identifies four distinct physician archetypes, from "Well-Rounded Excellent" (33.8%, uniformly high traits) to "Underperforming" (22.6%, consistently low). These findings demonstrate that automated trait extraction from patient narratives can provide interpretable, validated metrics for understanding physician-patient relationships at scale, with implications for quality measurement, bias detection, and workforce development in healthcare.

Jinghao Wang, Ping Zhang, Carter Yagemann

Medical Large Language Models (LLMs) are increasingly deployed for clinical decision support across diverse specialties, yet systematic evaluation of their robustness to adversarial misuse and privacy leakage remains inaccessible to most researchers. Existing security benchmarks require GPU clusters, commercial API access, or protected health data -- barriers that limit community participation in this critical research area. We propose a practical, fully reproducible framework for evaluating medical AI security under realistic resource constraints. Our framework design covers multiple medical specialties stratified by clinical risk -- from high-risk domains such as emergency medicine and psychiatry to general practice -- addressing jailbreaking attacks (role-playing, authority impersonation, multi-turn manipulation) and privacy extraction attacks. All evaluation utilizes synthetic patient records requiring no IRB approval. The framework is designed to run entirely on consumer CPU hardware using freely available models, eliminating cost barriers. We present the framework specification including threat models, data generation methodology, evaluation protocols, and scoring rubrics. This proposal establishes a foundation for comparative security assessment of medical-specialist models and defense mechanisms, advancing the broader goal of ensuring safe and trustworthy medical AI systems.

Konstantin M. Evdokimov, Ekaterina O. Ivanova, Amayak G. Brutyan et al.

Introduction. Functional tremor (FT) is the most common phenotype of functional movement disorders. Electrophysiological assessment is included in the diagnostic criteria for tremor; however, there is currently no consensus criteria for the differential diagnosis of FT. The objective of this study was to evaluate the utility of tremor frequency characteristics derived from accelerometry for the differential diagnosis between FT and organic tremor (OT). Materials and methods. Nineteen patients with FT, 20 patients with essential tremor, and 20 patients with Parkinson's disease were enrolled in the study and underwent electrophysiological examination with a two-channel accelerometer and subsequent data processing. Results. The study results revealed the differences in the frequency peak widths in patients with FT and OT, predominantly while performing a cognitive load task. This criterion showed a high sensitivity (100%) and a high specificity (97.5%) for the diagnosis of FT in the study population. Conclusion. Tremor characteristics recorded during accelerometry combined with cognitive load task can serve as an additional testing aid for differential diagnosis between functional and organic tremor.

Rossella Rispoli, Barbara Cappelletto

Mike Thelwall, Abdallah Yaghi

Time spent by academics on research quality assessment might be reduced if automated approaches can help. Whilst citation-based indicators have been extensively developed and evaluated for this, they have substantial limitations and Large Language Models (LLMs) like ChatGPT provide an alternative approach. This article assesses whether ChatGPT 4o-mini can be used to estimate the quality of journal articles across academia. It samples up to 200 articles from all 34 Units of Assessment (UoAs) in the UK's Research Excellence Framework (REF) 2021, comparing ChatGPT scores with departmental average scores. There was an almost universally positive Spearman correlation between ChatGPT scores and departmental averages, varying between 0.08 (Philosophy) and 0.78 (Psychology, Psychiatry and Neuroscience), except for Clinical Medicine (rho=-0.12). Although other explanations are possible, especially because REF score profiles are public, the results suggest that LLMs can provide reasonable research quality estimates in most areas of science, and particularly the physical and health sciences and engineering, even before citation data is available. Nevertheless, ChatGPT assessments seem to be more positive for most health and physical sciences than for other fields, a concern for multidisciplinary assessments, and the ChatGPT scores are only based on titles and abstracts, so cannot be research evaluations.

Saige Rutherford, Thomas Wolfers, Charlotte Fraza et al.

Reference classes in healthcare establish healthy norms, such as pediatric growth charts of height and weight, and are used to chart deviations from these norms which represent potential clinical risk. How the demographics of the reference class influence clinical interpretation of deviations is unknown. Using normative modeling, a method for building reference classes, we evaluate the fairness (racial bias) in reference models of structural brain images that are widely used in psychiatry and neurology. We test whether including race in the model creates fairer models. We predict self-reported race using the deviation scores from three different reference class normative models, to better understand bias in an integrated, multivariate sense. Across all of these tasks, we uncover racial disparities that are not easily addressed with existing data or commonly used modeling techniques. Our work suggests that deviations from the norm could be due to demographic mismatch with the reference class, and assigning clinical meaning to these deviations should be done with caution. Our approach also suggests that acquiring more representative samples is an urgent research priority.

Kayvan Kousha, Mike Thelwall

Academics and departments are sometimes judged by how their research has benefitted society. For example, the UK Research Excellence Framework (REF) assesses Impact Case Studies (ICS), which are five-page evidence-based claims of societal impacts. This study investigates whether ChatGPT can evaluate societal impact claims and therefore potentially support expert human assessors. For this, various parts of 6,220 public ICS from REF2021 were fed to ChatGPT 4o-mini along with the REF2021 evaluation guidelines, comparing the results with published departmental average ICS scores. The results suggest that the optimal strategy for high correlations with expert scores is to input the title and summary of an ICS but not the remaining text, and to modify the original REF guidelines to encourage a stricter evaluation. The scores generated by this approach correlated positively with departmental average scores in all 34 Units of Assessment (UoAs), with values between 0.18 (Economics and Econometrics) and 0.56 (Psychology, Psychiatry and Neuroscience). At the departmental level, the corresponding correlations were higher, reaching 0.71 for Sport and Exercise Sciences, Leisure and Tourism. Thus, ChatGPT-based ICS evaluations are simple and viable to support or cross-check expert judgments, although their value varies substantially between fields.

Narendra Javadekar, Archana Javadekar, Deepa Thakur

Mental illnesses have a significant impact on the lives of people not only because of their morbidity but also because of their noticeable impact on economic wellbeing. Out-of-pocket expenditure for mental healthcare services is significant in India and may even lead to impoverishment of the families. The present paper states that Health Technology Assessment (HTA) is necessary for mental healthcare primarily because of its rising cost and competing interests in government decisions and prioritization. HTA does a systematic evaluation of the consequences of using health technology. HTA will provide information to decision makers to develop and implement safer, cost-effective, and efficient policies at the individual and government levels. Appropriate guidance regarding the cost-effectiveness of mental health interventions will help to serve the purpose of providing transparent reports in the context of limited budgets.

M. Garriga, I. Pacchiarotti, S. Kasper et al.

Stephen, C., Newman et al.

Yuhan Li, Hongtao Zhang, Keaven Anderson et al.

In the pharmaceutical industry, the use of artificial intelligence (AI) has seen consistent growth over the past decade. This rise is attributed to major advancements in statistical machine learning methodologies, computational capabilities and the increased availability of large datasets. AI techniques are applied throughout different stages of drug development, ranging from drug discovery to post-marketing benefit-risk assessment. Kolluri et al. provided a review of several case studies that span these stages, featuring key applications such as protein structure prediction, success probability estimation, subgroup identification, and AI-assisted clinical trial monitoring. From a regulatory standpoint, there was a notable uptick in submissions incorporating AI components in 2021. The most prevalent therapeutic areas leveraging AI were oncology (27%), psychiatry (15%), gastroenterology (12%), and neurology (11%). The paradigm of personalized or precision medicine has gained significant traction in recent research, partly due to advancements in AI techniques \cite{hamburg2010path}. This shift has had a transformative impact on the pharmaceutical industry. Departing from the traditional "one-size-fits-all" model, personalized medicine incorporates various individual factors, such as environmental conditions, lifestyle choices, and health histories, to formulate customized treatment plans. By utilizing sophisticated machine learning algorithms, clinicians and researchers are better equipped to make informed decisions in areas such as disease prevention, diagnosis, and treatment selection, thereby optimizing health outcomes for each individual.

Sutirna Sutirna, Safuri Musa

The objective study is to know students' level of ability, understanding, skills, and attitudes in practice service guidance and counseling in schools. The approach research used is a study survey of guidance and counseling teachers who become tutors in accompaniment student practice guidance and counseling. Instruments in questionnaires closed as many as 25 items with indicator understanding, skills and attitudes students in implementation activity practice guidance and counseling. While processing techniques results survey uses percentages from many answer respondents compared amount whole respondents multiplied by 100%, the results percentage categorized as very good, good, well enough, less well, and very less. Research results conclude that students' level of ability in understanding, skills, and attitudes in implementation service guidance and counseling. The research results are concluded (1) the level of ability to understand guidance and counseling for students who carry out practices in schools is included in the sufficient category (very good 29.17% and good 56.25% ), (2) the level of students' skills in providing guidance and counseling services to students in the aspects of attending, responding, personalizing, and initiating is included in the sufficient category (very good 33.16% and good 56.88%), and (3) the level of ability of students' attitudes in carrying out guidance and counseling services in schools is categorized as sufficient (for very good 51.49% and good 41.96%).  

Samantha So, Teng Qing Wang, Brian Edward Yu et al.

Introduction: The COVID-19 pandemic has created a chronically stressful work environment for healthcare workers, increasing the negative psychological effects experienced. Aims: The authors of this systematic review and meta-analysis aimed to assess the impact of COVID-19 on frontline healthcare workers’ mental health, using various psychological outcomes. Methods: A systematic literature search was conducted up until June 30th, 2022 on MEDLINE, EMBASE, CINAHL, Cochrane Library, Web of Science, ClinicalTrials.gov, and Dissertations and Theses. Results: This meta-analysis includes 22 cross-sectional studies with a total of 32,690 participants. Anxiety (ES = 0.23, CI: [0.18, 0.28]), depression (ES = 0.17, CI: [0.10, 0.24]), PTSD (ES = 0.28, CI: [0.08, 0.48]), and stress (ES = 0.35, CI: [0.17, 0.53]) was significantly prevalent among frontline healthcare workers. Conclusions: Our results suggested that European healthcare workers were experiencing high psychological symptoms associated with the COVID-19 pandemic. The monitoring of their psychological symptoms, preventative interventions, and treatments should be implemented to prevent, reduce, and treat the worsening of their mental health.