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.

The concept of the glymphatic system was proposed more than a decade ago as a mechanism for interstitial fluid flow and waste removal in the central nervous system. The function of the glymphatic system has been shown to be particularly activated during sleep. Dysfunction of the glymphatic system has been implicated in several neurodegenerative diseases. Noninvasive in vivo imaging of the glymphatic system is expected to be useful in elucidating the pathophysiology of these diseases. Currently, magnetic resonance imaging is the most commonly used technique to evaluate the glymphatic system in humans, and a large number of studies have been reported. This review provides a comprehensive overview of investigations of the human glymphatic system function using magnetic resonance imaging. The studies can be divided into 3 categories, including imaging without gadolinium-based contrast agents (GBCAs), imaging with intrathecal administration of GBCAs, and imaging with intravenous administration of GBCAs. The purpose of these studies has been to examine not only the interstitial fluid movement in the brain parenchyma, but also the fluid dynamics in the perivascular and subarachnoid spaces, as well as the parasagittal dura and meningeal lymphatics. Recent research has even extended to include the glymphatic system of the eye and the inner ear. This review serves as an important update and a useful guide for future research directions.

This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist’s perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.

The efficacy of proton beam therapy (PBT) for hepatocellular carcinoma (HCC) has been reported, but insertion of fiducial markers in the liver is usually required. We evaluated the efficacy and toxicity of respiratory-gated PBT without fiducial markers for HCC located within 2 cm of the gastrointestinal tract. From March 2011 to December 2015 at our institution, 40 patients were evaluated (median age, 72 years; range, 38–87 years). All patients underwent PBT at a dose of 60 to 80 cobalt gray equivalents (CGE) in 20 to 38 fractions. The median follow-up period was 19.9 months (range, 1.2–72.3 months). The median tumor size was 36.5 mm (range, 11–124 mm). Kaplan–Meier estimates of the 2-year overall survival, progression-free survival, and local tumor control rates were 76%, 60%, and 94%, respectively. One patient (2.5%) developed a grade 3 gastric ulcer and one (2.5%) developed grade 3 ascites retention; none of the remaining patients developed grade >3 toxicities (National Cancer Institute Common Terminology Criteria for Adverse Events ver. 4.0.). This study indicates that PBT without fiducial markers achieves good local control without severe treatment-related toxicity of the gastrointestinal tract for HCC located within 2 cm of the gastrointestinal tract.

To date, there are no reports of intraoperative radiotherapy (IORT) use with long-term follow up as a method of accelerated partial breast irradiation (APBI) in Asian countries. We initiated a prospective phase I/II clinical trial of IORT in Japan in 2007, and herein, we report the 5-year follow-up results. The following inclusion criteria were used for enrollment in the trial: (1) tumor size < 2.5 cm, (2) desire for breast-conserving surgery, (3) age >50 years, and (4) negative margins after resection. In February 2009, the eligibility criteria were changed to include only patients with sentinel lymph node-negative disease. In phase I, the radiotherapy dose was escalated from 19 Gy/fr to 21 Gy/fr, incremented by 1 Gy per step, with 3 patients in each step. Doses were escalated after all patients in the preceding cohort had completed treatment and exhibited only grade 1 or 2 toxicities at a given dose level. The recommended phase II dose was set at 21 Gy at 90 % isodose. The primary endpoint was early toxicity. Secondary endpoints were long-term efficacy and late toxicity. In addition, Hypertrophic scarring was evaluated retrospectively as a cosmetic outcome by a radiation oncologist. Between December 2007 and March 2010, 32 women with breast cancer were enrolled in the trial. The median age was 65 years (51–80 years), and the median follow-up time was 6 years. No recurrence or metastasis was observed in any patient. Grade 2 fibrosis was detected in 3 patients as an acute adverse event and in 2 patients as a late adverse event. Ten patients developed a hypertrophic scar 1 year after the IORT; the number of patients decreased to 7 in the 3 years of follow-up. The first group of female Asian patients tolerated the treatment with IORT in this Phase I/II study and remained recurrence-free for more than 5 years after treatment. However, 24 % of the patients developed hypertrophic scarring, an event that is being further examined in our ongoing multi-center Phase II trial of IORT for early breast cancer.