PURPOSE: Due to significant growth in the volume of information produced by cancer research, staying abreast of recent developments has become a challenging task. Artificial intelligence (AI) can learn, reason, and understand the enormous corpus of l...
AJR. American journal of roentgenology
Sep 13, 2023
Timely and accurate interpretation of chest radiographs obtained to evaluate endotracheal tube (ETT) position is important for facilitating prompt adjustment if needed. The purpose of our study was to evaluate the performance of a deep learning (DL...
OBJECTIVE: To evaluate a biparametric MRI (bpMRI)-based artificial intelligence (AI) model for the detection of local prostate cancer (PCa) recurrence in patients with radiotherapy history.
BACKGROUND: In the Danish Head and Neck Cancer Group (DAHANCA) 35 trial, patients are selected for proton treatment based on simulated reductions of Normal Tissue Complication Probability (NTCP) for proton compared to photon treatment at the referrin...
Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders. However, the development of AI models requires substantial a...
How can we examine so-called 'artificial intelligence' ('AI') without turning our backs on the STS tradition that questions both notions of artificiality and intelligence? This special issue attempts a step to the side: Instead of considering 'AI' as...
Despite challenges in severity scoring systems, artificial intelligence-enhanced electrocardiography (AI-ECG) could assist in early coronavirus disease 2019 (COVID-19) severity prediction. Between March 2020 and June 2022, we enrolled 1453 COVID-19 p...
BACKGROUND: Nonspecific low back pain (NSLBP) carries significant socioeconomic relevance and leads to substantial difficulties for those who are affected by it. The effectiveness of app-based treatments has been confirmed, and clinicians are recomme...
High-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed molecular studies have revealed marked intra-patient heterogeneity at the tumour microenvironment level, likely contributing to poor prognosis. Despite large quan...
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