Systematic review of deep learning image analyses for the diagnosis and monitoring of skin disease.
Shern Ping Choy et al. NPJ Digit Med 2023 9 (1) 180
From the abstract: "We searched for studies applying deep learning to skin images, excluding benign/malignant lesions. The primary outcome was accuracy of deep learning algorithms in disease diagnosis or severity assessment. We modified QUADAS-2 for quality assessment. Of 13,857 references identified, 64 were included. The most studied diseases were acne, psoriasis, eczema, rosacea, vitiligo, urticaria. Deep learning algorithms had high specificity and variable sensitivity in diagnosing these conditions. Accuracy of algorithms in diagnosing acne (median 94%, IQR 86–98; n?=?11), rosacea (94%, 90–97; n?=?4), eczema (93%, 90–99; n?=?9) and psoriasis (89%, 78–92; n?=?8) was high. "
Distinguishing features of Long COVID identified through immune profiling.
Jon Klein et al. Nature 2023 9
From the abstract: "Here, 273 individuals with or without LC were enrolled in a cross-sectional study that included multi-dimensional immune phenotyping and unbiased machine learning methods to identify biological features associated with LC. Marked differences were noted in circulating myeloid and lymphocyte populations relative to matched controls, as well as evidence of exaggerated humoral responses directed against SARS-CoV-2 among participants with LC. Further, higher antibody responses directed against non-SARS-CoV-2 viral pathogens were observed among individuals with LC, particularly Epstein-Barr virus. "
AI can help to speed up drug discovery - but only if we give it the right data.
Marissa Mock et al. Nature 2023 9 (7979) 467-470
From the paper: "Artificial-intelligence tools that enable companies to share data about drug candidates while keeping sensitive information safe can unleash the potential of machine learning and cutting-edge lab techniques, for the common good. "
Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking
C Frascarelli et al, J Per Med, September 2023
From the abstract: "As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. Methods. In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. "