Sanjay M. Parekh, PhD[email protected]AIReimbursement issues give impetus to AI adoptionAdoption of AI solutions remains nascent and inconsistent. The market has yet to see widespread adoption of medical AI technologies, as Sanjay M. Parekh, PhD, explains.January 15, 2024Advanced VisualizationMedical imaging AI market poised for strong growthThe world market for medical imaging artificial intelligence (AI) applications is projected to reach almost $1.2 billion by 2025, with a compound annual growth rate of 26%, according to a new report from Signify Research.July 27, 2021AIMedical imaging AI market projected for strong growthThe world market for artificial intelligence (AI)-based clinical applications for use in medical imaging is set to reach almost $1.5 billion by 2024, despite a slower-than-expected uptake of these products and the impact of the COVID-19 pandemic, according to a new report from Signify Research.September 9, 2020Page 1 of 1Top StoriesCTDL segmentation of chest muscle volume shows promise for COPD patientsDeep learning (DL)-based muscle volume assessment from routine CT scans could help identify COPD patients with comorbid sarcopenia.CareersHPI: Workforce attrition higher in radiology subspecialtiesAssociationsVideo from ECR: Minerva Becker shares congress highlightsCTECR: 'Black swan events' demand informed imaging infrastructurePractice ManagementECR: Integrating actionable reporting into the imaging workflow
![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&fit=crop&h=400&q=70&w=600)
![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)
