The rapid advancement of artificial intelligence (AI) in healthcare has revolutionized the industry, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. However, the increasing adoption of AI systems also raises concerns about their environmental impact, particularly in the context of climate change. This review explores the intersection of climate change and AI in healthcare, examining the challenges posed by the energy consumption and carbon footprint of AI systems, as well as the potential solutions to mitigate their environmental impact. The review highlights the energy-intensive nature of AI model training and deployment, the contribution of data centers to greenhouse gas emissions, and the generation of electronic waste. To address these challenges, the development of energy-efficient AI models, the adoption of green computing practices, and the integration of renewable energy sources are discussed as potential solutions. The review also emphasizes the role of AI in optimizing healthcare workflows, reducing resource waste, and facilitating sustainable practices such as telemedicine. Furthermore, the importance of policy and governance frameworks, global initiatives, and collaborative efforts in promoting sustainable AI practices in healthcare is explored. The review concludes by outlining best practices for sustainable AI deployment, including eco-design, lifecycle assessment, responsible data management, and continuous monitoring and improvement. As the healthcare industry continues to embrace AI technologies, prioritizing sustainability and environmental responsibility is crucial to ensure that the benefits of AI are realized while actively contributing to the preservation of our planet.

Objectives To compare the image quality of the lungs between ultra-high-resolution CT (U-HRCT) and conventional area detector CT (AD-CT) images. Methods Image data of slit phantoms (0.35, 0.30, and 0.15 mm) and 11 cadaveric human lungs were acquired by both U-HRCT and AD-CT devices. U-HRCT images were obtained with three acquisition modes: normal mode (U-HRCT_N: 896 channels, 0.5 mm × 80 rows; 512 matrix), super-high-resolution mode (U-HRCT_SHR: 1792 channels, 0.25 mm × 160 rows; 1024 matrix), and volume mode (U-HRCT_SHR-VOL: non-helical acquisition with U-HRCT_SHR). AD-CT images were obtained with the same conditions as U-HRCT_N. Three independent observers scored normal anatomical structures (vessels and bronchi), abnormal CT findings (faint nodules, solid nodules, ground-glass opacity, consolidation, emphysema, interlobular septal thickening, intralobular reticular opacities, bronchovascular bundle thickening, bronchiectasis, and honeycombing), noise, artifacts, and overall image quality on a 3-point scale (1 = worst, 2 = equal, 3 = best) compared with U-HRCT_N. Noise values were calculated quantitatively. Results U-HRCT could depict a 0.15-mm slit. Both U-HRCT_SHR and U-HRCT_SHR-VOL significantly improved visualization of normal anatomical structures and abnormal CT findings, except for intralobular reticular opacities and reduced artifacts, compared with AD-CT (p < 0.014). Visually, U-HRCT_SHR-VOL has less noise than U-HRCT_SHR and AD-CT (p < 0.00001). Quantitative noise values were significantly higher in the following order: U-HRCT_SHR (mean, 30.41), U-HRCT_SHR-VOL (26.84), AD-CT (16.03), and U-HRCT_N (15.14) (p < 0.0001). U-HRCT_SHR and U-HRCT_SHR-VOL resulted in significantly higher overall image quality than AD-CT and were almost equal to U-HRCT_N (p < 0.0001). Conclusions Both U-HRCT_SHR and U-HRCT_SHR-VOL can provide higher image quality than AD-CT, while U-HRCT_SHR-VOL was less noisy than U-HRCT_SHR. Key Points • Ultra-high-resolution CT (U-HRCT) can improve spatial resolution. • U-HRCT can reduce streak and dark band artifacts. • U-HRCT can provide higher image quality than conventional area detector CT. • In U-HRCT, the volume mode is less noisy than the super-high-resolution mode. • U-HRCT may provide more detailed information about the lung anatomy and pathology.

Recently, PET response criteria in solid tumors (PERCIST) have been proposed as a new standardized method to assess chemotherapeutic response metabolically and quantitatively. The aim of this study was to evaluate therapeutic response to neoadjuvant chemotherapy for locally advanced esophageal cancer, comparing PERCIST with the currently widely used response evaluation criteria in solid tumors (RECIST). Methods: Fifty-one patients with locally advanced esophageal cancer who received neoadjuvant chemotherapy (5-fluorouracil, adriamycin, and cisplatin), followed by surgery were studied. Chemotherapeutic lesion responses were evaluated using ¹⁸F-FDG PET and CT according to the RECIST and PERCIST methods. The PET/CT scans were obtained before chemotherapy and about 2 wk after completion of chemotherapy. Associations were statistically analyzed between survival (overall and disease-free survival) and clinicopathologic results (histology [well-, moderately, and poorly differentiated squamous cell carcinoma], lymphatic invasion, venous invasion, clinical stage, pathologic stage, resection level, reduction rate of tumor diameter, reduction rate of tumor uptake, chemotherapeutic responses in RECIST and PERCIST, and pathologic response). Results: There was a significant difference in response classification between RECIST and PERCIST (Wilcoxon signed-rank test, P < 0.0001). Univariate analysis showed that lymphatic invasion, venous invasion, resection level, pathologic stage, and PERCIST were significant factors associated with disease-free or overall survival in this study. Although multivariate analysis demonstrated that venous invasion (disease-free survival: hazard ratio [HR] = 4.519, P = 0.002; overall survival: HR = 5.591, P = 0.003) and resection level (disease-free survival: HR = 11.078, P = 0.001) were the significant predictors, PERCIST was also significant in noninvasive therapy response assessment before surgery (disease-free survival: HR = 4.060, P = 0.025; overall survival: HR = 8.953, P = 0.034). Conclusion: RECIST based on the anatomic size reduction rate did not demonstrate the correlation between therapeutic responses and prognosis in patients with esophageal cancer receiving neoadjuvant chemotherapy. However, PERCIST was found to be the strongest independent predictor of outcomes. Given the significance of noninvasive radiologic imaging in formulating clinical treatment strategies, PERCIST might be considered more suitable for evaluation of chemotherapeutic response to esophageal cancer than RECIST.

Disruption of air bronchiologram and a solid portion greater than 0.8 cm at high-spatial-resolution CT was associated with a greater likelihood of invasiveness in lung adenocarcinoma. Background High-spatial-resolution (HSR) CT provides detailed information and clear delineation of lung anatomy and disease states. HSR CT may have high diagnostic performance for predicting invasi...

Interstitial lung abnormalities are incidental findings at chest CT that may have potential clinical significance and be associated with increased risk for lung cancer and complications related to ... Interstitial lung abnormality (ILA) is defined as an interstitial change detected incidentally on CT images. It is seen in 4%–9% of smokers and 2%–7% of nonsmokers. ILA has a tendency to progress w... Created with BioRender.com

Measuring the size of invasiveness on computed tomography (CT) for the T descriptor size was deemed important in the 8th edition of the TNM lung cancer classification. We aimed to correlate the maximal dimensions of the solid portions using both lung and mediastinal window settings on CT imaging with the pathologic invasiveness (> 0.5 cm) in lung adenocarcinoma patients. The study population consisted of 378 patients with a histologic diagnosis of adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IVA)-lepidic, IVA-acinar and/or IVA-papillary, and IVA-micropapillary and/or solid adenocarcinoma. A panel of 15 radiologists was divided into 2 groups (group A, 9 radiologists; and group B, 6 radiologists). The 2 groups independently measured the maximal and perpendicular dimensions of the solid components and entire tumors on the lung and mediastinal window settings. The solid proportion of nodule was calculated by dividing the solid portion size (lung and mediastinal window settings) by the nodule size (lung window setting). The maximal dimensions of the invasive focus were measured on the corresponding pathologic specimens by 2 pathologists. The solid proportion was larger in the following descending order: IVA-micropapillary and/or solid, IVA-acinar and/or papillary, IVA-lepidic, MIA, and AIS. For both groups A and B, a solid portion > 0.8 cm in the lung window setting or > 0.6 cm in the mediastinal window setting on CT was a significant indicator of pathologic invasiveness > 0.5 cm (P < .001; receiver operating characteristic analysis using Youden's index). A solid portion > 0.8 cm on the lung window setting or solid portion > 0.6 cm on the mediastinal window setting on CT predicts for histopathologic invasiveness to differentiate IVA from MIA and AIS.