Elad Walach[email protected]AI3 predictions for radiology AI in 2021With virtual RSNA 2020 over and the year drawing to a close, Elad Walach of artificial intelligence (AI) developer Aidoc has taken the time to share his predictions about trends in the world of radiology AI for next year.December 14, 2020AI5 reasons why imaging AI is different from CADArtificial intelligence (AI) in medical imaging is on the rise and already becoming part of the standard of care. But with all the hype, one must ask: Is next-generation imaging AI simply a new take on computer-aided detection/diagnosis (CAD)? Aidoc CEO Elad Walach presents five reasons why AI is fundamentally different.October 28, 2018AI3 ways to ensure AI algorithms are really workingArtificial intelligence (AI), deep learning, and related technologies have emerged as solutions that can really make a difference in almost every medical field imaginable, from radiology to pharmaceuticals to diagnosis. But the question remains: How do we know that these algorithms are actually working?August 5, 2018Page 1 of 1Top StoriesCTGoogle AI Studio shows potential for AI-based lung cancer detectionThe model identified lung malignancies and produced consistent, structured reports, but needs further refinement.Risk ManagementPodcast: Whose duty is it to ensure MRI safety?Womens ImagingThe future of BI-RADS includes AIMRINew MRI scoring system predicts early liver cancer recurrenceMRIUnstable brain networks may drive multiple sclerosis fatigue
![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&dpr=2&fit=crop&h=167&q=70&w=250)
