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In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.

The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs’ potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor.

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.

Some herbivorous insects possess the ability to synthesize phytohormones and are considered to use them for manipulating their host plants, but how these insects acquired the ability remains unclear. We investigated endogenous levels of auxin (IAA) and cytokinins (iP and t Z), including their ribosides (iPR and t ZR), in various terrestrial arthropod taxa. Surprisingly, IAA was detected in all arthropods analysed. In contrast, t Z and/or t ZR was detected only in some taxa. Endogenous levels of IAA were not significantly different among groups with different feeding habits, but gall inducers possessed significantly higher levels of iPR, t Z and t ZR. Ancestral state reconstruction of the ability to synthesize t Z and t ZR revealed that the trait has only been acquired in taxa containing gall inducers. Our results strongly suggest critical role of the cytokinin synthetic ability in the evolution of gall-inducing habit and IAA has some function in arthropods.

There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.

BackgroundLittle is known about the disadvantages of the coronavirus disease 2019 (COVID-19) pandemic in patients with gastric cancer. This study aimed to examine the negative impact of the COVID-19 pandemic on patients with gastric cancer in the first era in Japan.MethodsThis retrospective study included 725 patients diagnosed with gastric cancer who visited our hospital between April 2019 and March 2021. The number of patients and their characteristics before and during the COVID-19 pandemic were compared.ResultsThe number of patients diagnosed with gastric cancer during the COVID-19 pandemic decreased by 26.2% (from 417 to 308; p = 0.013) compared to that before the COVID-19 pandemic. There was a significant decrease in cStage I cancer and an increase in cStage III cancer (p = 0.004). Patients were often symptomatic (p = 0.029), especially those with stenosis-related symptoms (p < 0.001) and longer symptom duration (p < 0.001). The number of endoscopic resections was decreased by 34.8% (p = 0.005). The number of total gastrectomy was higher than that of partial gastrectomy (p = 0.021). The median time to treatment was significantly shorter (p < 0.001).ConclusionsIn Japan, delays diagnosing patients with gastric cancer, probably due to refraining from consultation, may have resulted in an increase in the diagnosis of advanced-stage cancer. Moreover, an increasing proportion of patients required more invasive gastrectomy. Therefore, it may be necessary to educate patients not to refrain from consultation, even during the COVID-19 pandemic, as it can have a negative impact on treatment, policy decision, and prognosis of gastric cancer.

This review explores the potential applications of Large Language Models (LLMs) in nuclear medicine, especially nuclear medicine examinations such as PET and SPECT, reviewing recent advancements in both fields. Despite the rapid adoption of LLMs in various medical specialties, their integration into nuclear medicine has not yet been sufficiently explored. We first discuss the latest developments in nuclear medicine, including new radiopharmaceuticals, imaging techniques, and clinical applications. We then analyze how LLMs are being utilized in radiology, particularly in report generation, image interpretation, and medical education. We highlight the potential of LLMs to enhance nuclear medicine practices, such as improving report structuring, assisting in diagnosis, and facilitating research. However, challenges remain, including the need for improved reliability, explainability, and bias reduction in LLMs. The review also addresses the ethical considerations and potential limitations of AI in healthcare. In conclusion, LLMs have significant potential to transform existing frameworks in nuclear medicine, making it a critical area for future research and development.

Purpose The reproducibility of neurite orientation dispersion and density imaging (NODDI) metrics in the human brain has not been explored across different magnetic resonance (MR) scanners from different vendors. This study aimed to evaluate the scan–rescan and inter-vendor reproducibility of NODDI metrics in white and gray matter of healthy subjects using two 3-T MR scanners from two vendors. Methods Ten healthy subjects (7 males; mean age 30 ± 7 years, range 23–37 years) were included in the study. Whole-brain diffusion-weighted imaging was performed with b-values of 1000 and 2000 s/mm 2 using two 3-T MR scanners from two different vendors. Automatic extraction of the region of interest was performed to obtain NODDI metrics for whole and localized areas of white and gray matter. The coefficient of variation (CoV) and intraclass correlation coefficient (ICC) were calculated to assess the scan–rescan and inter-vendor reproducibilities of NODDI metrics. Results The scan–rescan and inter-vendor reproducibility of NODDI metrics (intracellular volume fraction and orientation dispersion index) were comparable with those of diffusion tensor imaging (DTI) metrics. However, the inter-vendor reproducibilities of NODDI (CoV = 2.3–14%) were lower than the scan–rescan reproducibility (CoV: scanner A = 0.8–3.8%; scanner B = 0.8–2.6%). Compared with the finding of DTI metrics, the reproducibility of NODDI metrics was lower in white matter and higher in gray matter. Conclusion The lower inter-vendor reproducibility of NODDI in some brain regions indicates that data acquired from different MRI scanners should be carefully interpreted.

The radiopharmaceutical 2-[fluorine-18]fluoro-2-deoxy- d -glucose (FDG) has been dominantly used in positron emission tomography (PET) scans for over 20 years, and due to its vast utility its applications have expanded and are continuing to expand into oncology, neurology, cardiology, and infectious/inflammatory diseases. More recently, the addition of artificial intelligence (AI) has enhanced nuclear medicine diagnosis and imaging with FDG-PET, and new radiopharmaceuticals such as prostate-specific membrane antigen (PSMA) and fibroblast activation protein inhibitor (FAPI) have emerged. Nuclear medicine therapy using agents such as [ 177 Lu]-dotatate surpasses conventional treatments in terms of efficacy and side effects. This article reviews recently established evidence of FDG and non-FDG drugs and anticipates the future trajectory of nuclear medicine.

Iturin A and surfactin are antimicrobial cyclic lipopeptides secreted by antagonistic Bacillus strains, and both elicit defence gene expression in host plants. We previously showed that a soil amendment with either of the purified lipopeptides confers suppression against Fusarium yellows of tatsoi (Brassica rapa) caused by the soil-borne pathogen Fusarium oxysporum and that excess amounts of iturin A or surfactin do not confer suppression. Here, we evaluated the impacts of purified iturin A or surfactin against Fusarium wilt of lettuce (Lactuca sativa). As a soil amendment, iturin A and surfactin each conferred suppression of Fusarium wilt of lettuce on diverse lettuce cultivars similar to the results for tatsoi. However, higher concentrations of iturin A (1.88 mg/l of soil or higher) but not of surfactin (up to 7.5 mg/l of soil) negated the suppression. Growth of Fusarium in liquid cultures was suppressed by iturin A but not surfactin.