Artificial intelligence (AI) algorithms continue to rival human performance on a variety of clinical tasks, while their actual impact on human diagnosticians, when incorporated into clinical workflows, remains relatively unexplored. In this study, we developed a deep learning-based assistant to help pathologists differentiate between two subtypes of primary liver cancer, hepatocellular carcinoma and cholangiocarcinoma, on hematoxylin and eosin-stained whole-slide images (WSI), and evaluated its effect on the diagnostic performance of 11 pathologists with varying levels of expertise. Our model achieved accuracies of 0.885 on a validation set of 26 WSI, and 0.842 on an independent test set of 80 WSI. Although use of the assistant did not change the mean accuracy of the 11 pathologists (p = 0.184, OR = 1.281), it significantly improved the accuracy (p = 0.045, OR = 1.499) of a subset of nine pathologists who fell within well-defined experience levels (GI subspecialists, non-GI subspecialists, and trainees). In the assisted state, model accuracy significantly impacted the diagnostic decisions of all 11 pathologists. As expected, when the model's prediction was correct, assistance significantly improved accuracy (p = 0.000, OR = 4.289), whereas when the model's prediction was incorrect, assistance significantly decreased accuracy (p = 0.000, OR = 0.253), with both effects holding across all pathologist experience levels and case difficulty levels. Our results highlight the challenges of translating AI models into the clinical setting, and emphasize the importance of taking into account potential unintended negative consequences of model assistance when designing and testing medical AI-assistance tools.npj Digital Medicine (2020) 3:23 ; https://doi.

Tuberculosis (TB) is the leading cause of preventable death in HIV-positive patients, and yet often remains undiagnosed and untreated. Chest x-ray is often used to assist in diagnosis, yet this presents additional challenges due to atypical radiographic presentation and radiologist shortages in regions where co-infection is most common. We developed a deep learning algorithm to diagnose TB using clinical information and chest x-ray images from 677 HIV-positive patients with suspected TB from two hospitals in South Africa. We then sought to determine whether the algorithm could assist clinicians in the diagnosis of TB in HIV-positive patients as a web-based diagnostic assistant. Use of the algorithm resulted in a modest but statistically significant improvement in clinician accuracy (p = 0.002), increasing the mean clinician accuracy from 0.60 (95% CI 0.57, 0.63) without assistance to 0.65 (95% CI 0.60, 0.70) with assistance. However, the accuracy of assisted clinicians was significantly lower (p < 0.001) than that of the stand-alone algorithm, which had an accuracy of 0.79 (95% CI 0.77, 0.82) on the same unseen test cases. These results suggest that deep learning assistance may improve clinician accuracy in TB diagnosis using chest x-rays, which would be valuable in settings with a high burden of HIV/TB co-infection. Moreover, the high accuracy of the stand-alone algorithm suggests a potential value particularly in settings with a scarcity of radiological expertise.

whwong@stanford.edu.

Statins are a group of lipid-lowering drugs that inhibit cholesterol biosynthesis and have anti-inflammatory, anti-tumor, and immunomodulatory properties. Several lines of evidence indicate that statins regulate multiple proteins associated with the regulation of differing cellular pathways. The 5′-adenosine monophosphate-activated protein kinase (AMPK) pathway plays an important role in metabolism homeostasis with effects on cellular processes including apoptosis and the inflammatory responses through several pathways. Recently, it has been shown that statins can affect the AMPK pathway in differing physiological and pathological ways, resulting in anti-cancer, cardio-protective, neuro-protective, and anti-tubercular effects; additionally, they have therapeutic effects on non-alcoholic fatty liver disease and diabetes mellitus-associated complications. Statins activate AMPK as an energy sensor that inhibits cell proliferation and induces apoptosis in cancer cells, whilst exerting its cardio-protective effects through inhibition of inflammation and fibrosis, and promotion of angiogenesis. Furthermore, statin-associated AMPK activation leads to decreased lipid accumulation and decreased amyloid beta deposition in the liver and brain, respectively, and may have therapeutic effects on the liver and neurons. In this review, we summarize the results of studies of AMPK-associated therapeutic effects of statins in different pathological conditions.