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Objectives: Artificial intelligence (AI) is seen as a major disrupting force in the future healthcare system. However, the assessment of the value of AI technologies is still unclear. Therefore, a multidisciplinary group of experts and patients developed a Model for ASsessing the value of AI (MAS-AI) in medical imaging. Medical imaging is chosen due to the maturity of AI in this area, ensuring a robust evidence-based model.
Methods: MAS-AI was developed in three phases. First, a literature review of existing guides, evaluations, and assessments of the value of AI in the field of medical imaging. Next, we interviewed leading researchers in AI in Denmark. The third phase consisted of two workshops where decision makers, patient organizations, and researchers discussed crucial topics for evaluating AI. The multidisciplinary team revised the model between workshops according to comments.
Results: The MAS-AI guideline consists of two steps covering nine domains and five process factors supporting the assessment. Step 1 contains a description of patients, how the AI model was developed, and initial ethical and legal considerations. In step 2, a multidisciplinary assessment of outcomes of the AI application is done for the five remaining domains: safety, clinical aspects, economics, organizational aspects, and patient aspects.
Conclusions: We have developed an health technology assessment-based framework to support the introduction of AI technologies into healthcare in medical imaging. It is essential to ensure informed and valid decisions regarding the adoption of AI with a structured process and tool. MAS-AI can help support decision making and provide greater transparency for all parties.
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http://dx.doi.org/10.1017/S0266462322000551 | DOI Listing |
Cytopathology
September 2025
Department of Cardiothoracic and Vascular Surgery, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry, India.
Mediastinal masses often present acutely as medical emergencies, necessitating prompt and accurate diagnosis. Imaging-guided fine needle aspiration cytology (FNAC) plays a pivotal role in rapidly identifying rare mediastinal tumours and differentiating them from other potential aetiologies, enabling timely intervention. Primary mediastinal germ cell tumours (PMGCTs) constitute approximately 15% of adult mediastinal neoplasms.
View Article and Find Full Text PDFScand J Rheumatol
September 2025
Centre for Rheumatology, Department of Medicine, Turku University Hospital and University of Turku, Turku, Finland.
Alzheimers Dement
September 2025
Department of Neurology, Beijing TianTan Hospital, Capital Medical University, Beijing, China.
Cognitive impairment and dementia, including Alzheimer's disease (AD), pose a global health crisis, necessitating non-invasive biomarkers for early detection. This review highlights the retina, an accessible extension of the central nervous system (CNS), as a window to cerebral pathology through structural, functional, and molecular alterations. By synthesizing interdisciplinary evidence, we identify retinal biomarkers as promising tools for early diagnosis and risk stratification.
View Article and Find Full Text PDFAlzheimers Dement
September 2025
Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea.
Introduction: We developed and validated age-related amyloid beta (Aβ) positron emission tomography (PET) trajectories using a statistical model in cognitively unimpaired (CU) individuals.
Methods: We analyzed 849 CU Korean and 521 CU non-Hispanic White (NHW) participants after propensity score matching. Aβ PET trajectories were modeled using the generalized additive model for location, scale, and shape (GAMLSS) based on baseline data and validated with longitudinal data.
Hum Brain Mapp
September 2025
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.
Investigating neuroimaging data to identify brain-based markers of mental illnesses has gained significant attention. Nevertheless, these endeavors encounter challenges arising from a reliance on symptoms and self-report assessments in making an initial diagnosis. The absence of biological data to delineate nosological categories hinders the provision of additional neurobiological insights into these disorders.
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