Could artificial intelligence (AI) assessment have comparable diagnostic accuracy to clinician assessment for fracture detection? In a recently published meta-analysis of 42 studies, the study authors noted 92 percent sensitivity and 91 percent specificity for AI in comparison to 91 percent sensitivity and 92 percent specificity for clinicians based on internal validation test sets. For the external validation test sets, clinicians had 94 percent specificity and sensitivity in comparison to 91 percent specificity and sensitivity for AI, according to the study. In essence, the study authors found no statistically significant differences between AI and clinician diagnosis of fractures. "The results from this meta-analysis cautiously suggest that AI is noninferior to clinicians in terms of diagnostic performance in fracture detection, showing promise as a useful diagnostic tool," wrote Dominic Furniss, DM, MA, MBBCh, FRCS(Plast), a professor of plastic and reconstructive surgery in the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences at the Botnar Research Centre in Oxford, United Kingdom., and colleagues.

Computer-aided review of X-rays can help diagnose people with lung illnesses such as tuberculosis and pneumonia. Such review, augmented by deep learning, can help lead to earlier detection and treatment of these conditions, especially in remote areas where specialists are typically in short supply, according to new research. About one third of people in the world may be infected with tuberculosis. In 2016, there were more than 10 million cases of active tuberculosis, resulting in 1.3 million deaths. It's the No. 1 cause of death for an infectious disease, and more than 95 percent of these deaths occurred in developing countries.