The malicious use of artificial intelligence is growing rapidly, creating major security threats for individuals and the healthcare sector. Individuals with mental illness may be especially vulnerable. Healthcare provider data are a prime target for cybercriminals. There is a need to improve cybersecurity to detect and prevent cyberattacks against individuals and the healthcare sector, including the use of artificial intelligence predictive tools.
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Loneliness is associated with different structural brain changes in schizophrenia spectrum disorders and major depression.
Schizophr Res
January 2025
Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; German Center for Mental Health (DZPG), partner site Mannheim-Heidelberg-Ulm, Germany. Electronic address:
Background: Loneliness, distress from having fewer social contacts than desired, has been recognized as a significant public health crisis. Although a substantial body of research has established connections between loneliness and various forms of psychopathology, our understanding of the neural underpinnings of loneliness in schizophrenia spectrum disorders (SSD) and major depressive disorder (MDD) remains limited.
Methods: In this study, structural magnetic resonance imaging (sMRI) data were collected from 57 SSD and 45 MDD patients as well as 41 healthy controls (HC).
Impact of Attached File Formats on the Performance of ChatGPT-4 on the Japanese National Nursing Examination: Evaluation Study.
JMIR Nurs
January 2025
Department of Healthcare Epidemiology, Graduate School of Medicine and Public Health, Kyoto University, Kyoto, Japan.
This research letter discusses the impact of different file formats on ChatGPT-4's performance on the Japanese National Nursing Examination, highlighting the need for standardized reporting protocols to enhance the integration of artificial intelligence in nursing education and practice.
View Article and Find Full Text PDFThe Significance of a Cerebrovascular Accident Outcome Prediction Model for Patients, Family Members, and Health Care Professionals: Qualitative Evaluation Study.
JMIR Hum Factors
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Department of Value Improvement, St. Antonius Hospital, Nieuwegein, Netherlands.
Background: Patients with cerebrovascular accident (CVA) should be involved in setting their rehabilitation goals. A personalized prediction of CVA outcomes would allow care professionals to better inform patients and informal caregivers. Several accurate prediction models have been created, but acceptance and proper implementation of the models are prerequisites for model adoption.
View Article and Find Full Text PDFDiscrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study.
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Department of Psychology, The University of Texas at San Antonio, San Antonio, TX, United States.
Background: Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists use personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions of these search patterns, which involve the physician verbalizing or annotating the order he or she analyzes the image, can be unreliable due to discrepancies in what is reported versus the actual visual patterns.
View Article and Find Full Text PDFEfficacy of a Mobile Health-Based Behavioral Treatment for Lifestyle Modification in Type 2 Diabetes Self-Management: Greenhabit Randomized Controlled Trial.
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Department of Internal Medicine, Hospital Clinic, Institut d'Investigacio Biomèdica August Pi i Sunyer, Barcelona, Spain.
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Methods: A 12-week, parallel, single-blind randomized controlled trial was conducted with 123 participants (62/123, 50%, female; mean age 58.
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