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| http://dx.doi.org/10.1136/bmj.288.6427.1369 | DOI Listing |
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Clinically probable RBD is an early predictor of malignant non-motor Parkinson's disease phenotypes.
NPJ Parkinsons Dis
January 2025
Norman Fixel Institute for Neurological Disease, University of Florida, Gainesville, FL, USA.
Non-motor symptoms (NMS) in Parkinson's disease (PD) significantly impact quality of life, especially in later stages. REM sleep behavior disorder (RBD) affects approximately 42% of all PD patients and frequently precedes motor PD symptoms. RBD is linked to increased rates of depression and cognitive decline.
View Article and Find Full Text PDFThe Clinicians' Guide to Large Language Models: A General Perspective With a Focus on Hallucinations.
Interact J Med Res
January 2025
Medical Directorate, Lausanne University Hospital, Lausanne, Switzerland.
Large language models (LLMs) are artificial intelligence tools that have the prospect of profoundly changing how we practice all aspects of medicine. Considering the incredible potential of LLMs in medicine and the interest of many health care stakeholders for implementation into routine practice, it is therefore essential that clinicians be aware of the basic risks associated with the use of these models. Namely, a significant risk associated with the use of LLMs is their potential to create hallucinations.
View Article and Find Full Text PDFDeep Learning Superresolution for Simultaneous Multislice Parallel Imaging-Accelerated Knee MRI Using Arthroscopy Validation.
Radiology
January 2025
From the Department of Radiology, Division of Musculoskeletal Radiology, NYU Grossman School of Medicine, 660 1st Ave, 3rd Fl, Rm 313, New York, NY 10016 (S.S.W., J.V., R.K., E.H.P., J.F.); Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tübingen, University Hospital Tübingen, Tübingen, Germany (S.S.W.); Department of Radiology, University Hospital Basel, Basel, Switzerland (J.V.); Department of Radiology, Hospital do Coraçao, São Paulo, Brazil (T.C.R.); Academic Surgical Unit, South West London Elective Orthopaedic Centre (SWLEOC), London, United Kingdom (D.D.); Department of Radiology, Balgrist University Hospital, Zurich, Switzerland (B.F.); Department of Radiology, Jeonbuk National University Hospital, Jeonju, Republic of Korea (E.H.P.); Research Institute of Clinical Medicine of Jeonbuk National University, Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea (E.H.P.); Medscanlagos Radiology, Cabo Frio, Brazil (A.S.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Siemens Healthineers AG, Erlangen, Germany (I.B.); and Siemens Medical Solutions USA, Malvern, Pa (G.K.).
Background Deep learning (DL) methods can improve accelerated MRI but require validation against an independent reference standard to ensure robustness and accuracy. Purpose To validate the diagnostic performance of twofold-simultaneous-multislice (SMSx2) twofold-parallel-imaging (PIx2)-accelerated DL superresolution MRI in the knee against conventional SMSx2-PIx2-accelerated MRI using arthroscopy as the reference standard. Materials and Methods Adults with painful knee conditions were prospectively enrolled from December 2021 to October 2022.
View Article and Find Full Text PDFSystemic Lupus Erythematosus Presenting as Anorexia Nervosa in a Middle-Aged Woman: A Rare Case Report and Literature Review.
Immun Inflamm Dis
January 2025
Department of Internal Medicine, Al-Khor Hospital, Hamad Medical Corporation, Doha, Qatar.
Background: Systemic lupus erythematosus (SLE) is a complex autoimmune disease with multisystemic involvement and unclear etiology. Although SLE could be linked to multiple neuropsychiatric manifestations, the co-occurrence of anorexia nervosa was only described through a few case reports that mainly affected children and adolescents.
Case Presentation: a 40-year-old Filipina woman presented to hospital with a 3-day history of agitation, anorexia and auditory hallucinations.
Scalable information extraction from free text electronic health records using large language models.
BMC Med Res Methodol
January 2025
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030-R, Boston, MA, 02120, USA.
Background: A vast amount of potentially useful information such as description of patient symptoms, family, and social history is recorded as free-text notes in electronic health records (EHRs) but is difficult to reliably extract at scale, limiting their utility in research. This study aims to assess whether an "out of the box" implementation of open-source large language models (LLMs) without any fine-tuning can accurately extract social determinants of health (SDoH) data from free-text clinical notes.
Methods: We conducted a cross-sectional study using EHR data from the Mass General Brigham (MGB) system, analyzing free-text notes for SDoH information.
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