The global burden of hepatocellular carcinoma (HCC) has shifted from viral to nonviral etiologies.However, successful antiviral therapy does not fully eliminate the risk of HCC, underscoring the demand for more effective surveillance strategies. Current screening methods, such as semi-annual ultrasonography and the measurement of α-fetoprotein levels, offer suboptimal sensitivityfor early detection. A cost-effective, reliable surveillance approach remains an unmet need. The Barcelona Clinic Liver Cancer staging system provides a framework to guide HCC therapy; yet, some gray zone exists, particularly for patients with intermediate-stage disease. Although tyrosine kinase inhibitors and immunotherapies have transformed the therapeutic landscape, their efficacies vary among patients, highlighting the necessity for personalized treatment strategies.In response to these challenges, artificial intelligence (AI) approaches have emerged as transformative tools in healthcare. By processing complex, nonlinear relationships and uncovering hidden patterns in clinical data, AI methods offer capabilities beyond those of traditional statistical methods. Furthermore, AI-driven multi-omics analysis holds promise for identifying novel biomarkers, thereby advancing precision medicine for HCC patients. This review introduces the potential of AI applications in enhancing the diagnosis, treatment, and prognosis of HCC.

Child abuse is a widespread global health crisis with longterm implications for children’s physical, psychological, and emotional well-being. Pediatric emergency departments (PEDs) are often the first point of contact for children experiencing abuse, presenting unique diagnostic challenges due to the diverse manifestations of maltreatment. In contrast to physical abuse that often leaves visible markers, nonphysical abuse (e.g., emotional neglect or psychological harm) frequently go undetected, exacerbating underreporting and delayed interventions. This article examines the prevalence, diagnostic challenges, and multidisciplinary strategies for addressing child maltreatment in PEDs. Cultural and systemic barriers, coupled with insufficient training and resource constraints, hinder the recognition of subtle signs of nonphysical abuse. Advances in diagnostic imaging, behavioral assessment tools, and laboratory investigations are discussed as critical components of comprehensive evaluations. Multidisciplinary teams play a pivotal role in addressing the complexity of abuse cases, combining medical, psychological, and legal expertise to provide holistic care while minimizing additional trauma. The integration of artificial intelligence into diagnostic workflows is explored as a transformative innovation, offering potential for early detection of abuse patterns and enhanced decision-making. However, the implementation of artificial intelligence requires careful consideration to ensure that it complements human expertise and aligns with ethical standards. There is a need for standardized protocols, targeted training programs, and supportive systems to empower healthcare professionals in recognizing and reporting all forms of child maltreatment. We propose more research to develop tools capable of detecting nonphysical abuse in PEDs. By combining technological advancements, multidisciplinary collaboration, and educational initiatives, PEDs can improve outcomes for vulnerable children and address the widespread issue of child maltreatment more effectively.

Child abuse is a widespread global health crisis with longterm implications for children’s physical, psychological, and emotional well-being. Pediatric emergency departments (PEDs) are often the first point of contact for children experiencing abuse, presenting unique diagnostic challenges due to the diverse manifestations of maltreatment. In contrast to physical abuse that often leaves visible markers, nonphysical abuse (e.g., emotional neglect or psychological harm) frequently go undetected, exacerbating underreporting and delayed interventions. This article examines the prevalence, diagnostic challenges, and multidisciplinary strategies for addressing child maltreatment in PEDs. Cultural and systemic barriers, coupled with insufficient training and resource constraints, hinder the recognition of subtle signs of nonphysical abuse. Advances in diagnostic imaging, behavioral assessment tools, and laboratory investigations are discussed as critical components of comprehensive evaluations. Multidisciplinary teams play a pivotal role in addressing the complexity of abuse cases, combining medical, psychological, and legal expertise to provide holistic care while minimizing additional trauma. The integration of artificial intelligence into diagnostic workflows is explored as a transformative innovation, offering potential for early detection of abuse patterns and enhanced decision-making. However, the implementation of artificial intelligence requires careful consideration to ensure that it complements human expertise and aligns with ethical standards. There is a need for standardized protocols, targeted training programs, and supportive systems to empower healthcare professionals in recognizing and reporting all forms of child maltreatment. We propose more research to develop tools capable of detecting nonphysical abuse in PEDs. By combining technological advancements, multidisciplinary collaboration, and educational initiatives, PEDs can improve outcomes for vulnerable children and address the widespread issue of child maltreatment more effectively.

Child abuse is a widespread global health crisis with longterm implications for children’s physical, psychological, and emotional well-being. Pediatric emergency departments (PEDs) are often the first point of contact for children experiencing abuse, presenting unique diagnostic challenges due to the diverse manifestations of maltreatment. In contrast to physical abuse that often leaves visible markers, nonphysical abuse (e.g., emotional neglect or psychological harm) frequently go undetected, exacerbating underreporting and delayed interventions. This article examines the prevalence, diagnostic challenges, and multidisciplinary strategies for addressing child maltreatment in PEDs. Cultural and systemic barriers, coupled with insufficient training and resource constraints, hinder the recognition of subtle signs of nonphysical abuse. Advances in diagnostic imaging, behavioral assessment tools, and laboratory investigations are discussed as critical components of comprehensive evaluations. Multidisciplinary teams play a pivotal role in addressing the complexity of abuse cases, combining medical, psychological, and legal expertise to provide holistic care while minimizing additional trauma. The integration of artificial intelligence into diagnostic workflows is explored as a transformative innovation, offering potential for early detection of abuse patterns and enhanced decision-making. However, the implementation of artificial intelligence requires careful consideration to ensure that it complements human expertise and aligns with ethical standards. There is a need for standardized protocols, targeted training programs, and supportive systems to empower healthcare professionals in recognizing and reporting all forms of child maltreatment. We propose more research to develop tools capable of detecting nonphysical abuse in PEDs. By combining technological advancements, multidisciplinary collaboration, and educational initiatives, PEDs can improve outcomes for vulnerable children and address the widespread issue of child maltreatment more effectively.

Child abuse is a widespread global health crisis with longterm implications for children’s physical, psychological, and emotional well-being. Pediatric emergency departments (PEDs) are often the first point of contact for children experiencing abuse, presenting unique diagnostic challenges due to the diverse manifestations of maltreatment. In contrast to physical abuse that often leaves visible markers, nonphysical abuse (e.g., emotional neglect or psychological harm) frequently go undetected, exacerbating underreporting and delayed interventions. This article examines the prevalence, diagnostic challenges, and multidisciplinary strategies for addressing child maltreatment in PEDs. Cultural and systemic barriers, coupled with insufficient training and resource constraints, hinder the recognition of subtle signs of nonphysical abuse. Advances in diagnostic imaging, behavioral assessment tools, and laboratory investigations are discussed as critical components of comprehensive evaluations. Multidisciplinary teams play a pivotal role in addressing the complexity of abuse cases, combining medical, psychological, and legal expertise to provide holistic care while minimizing additional trauma. The integration of artificial intelligence into diagnostic workflows is explored as a transformative innovation, offering potential for early detection of abuse patterns and enhanced decision-making. However, the implementation of artificial intelligence requires careful consideration to ensure that it complements human expertise and aligns with ethical standards. There is a need for standardized protocols, targeted training programs, and supportive systems to empower healthcare professionals in recognizing and reporting all forms of child maltreatment. We propose more research to develop tools capable of detecting nonphysical abuse in PEDs. By combining technological advancements, multidisciplinary collaboration, and educational initiatives, PEDs can improve outcomes for vulnerable children and address the widespread issue of child maltreatment more effectively.

Child abuse is a widespread global health crisis with longterm implications for children’s physical, psychological, and emotional well-being. Pediatric emergency departments (PEDs) are often the first point of contact for children experiencing abuse, presenting unique diagnostic challenges due to the diverse manifestations of maltreatment. In contrast to physical abuse that often leaves visible markers, nonphysical abuse (e.g., emotional neglect or psychological harm) frequently go undetected, exacerbating underreporting and delayed interventions. This article examines the prevalence, diagnostic challenges, and multidisciplinary strategies for addressing child maltreatment in PEDs. Cultural and systemic barriers, coupled with insufficient training and resource constraints, hinder the recognition of subtle signs of nonphysical abuse. Advances in diagnostic imaging, behavioral assessment tools, and laboratory investigations are discussed as critical components of comprehensive evaluations. Multidisciplinary teams play a pivotal role in addressing the complexity of abuse cases, combining medical, psychological, and legal expertise to provide holistic care while minimizing additional trauma. The integration of artificial intelligence into diagnostic workflows is explored as a transformative innovation, offering potential for early detection of abuse patterns and enhanced decision-making. However, the implementation of artificial intelligence requires careful consideration to ensure that it complements human expertise and aligns with ethical standards. There is a need for standardized protocols, targeted training programs, and supportive systems to empower healthcare professionals in recognizing and reporting all forms of child maltreatment. We propose more research to develop tools capable of detecting nonphysical abuse in PEDs. By combining technological advancements, multidisciplinary collaboration, and educational initiatives, PEDs can improve outcomes for vulnerable children and address the widespread issue of child maltreatment more effectively.

Fatty liver is now a major cause of liver disease in the Asia-Pacific region. Liver diseases in this region have distinctive characteristics. First, fatty liver is frequently observed in leanormal-weight individuals. However, there is no standard definition of this unique phenotype. Second, fatty liver is often observed in patients with concomitant viral hepatitis. The exclusion of viral hepatitis from non-alcoholic fatty liver disease limits its value and detracts from the investigation and holistic management of coexisting fatty liver in patients with viral hepatitis. Third, fatty liver-associated hepatocellular carcinoma (HCC) is generally categorized as non-B non-C HCC. Fourth, the population is aging rapidly, and it is imperative to develop a practicable, low-intensity exercise program for elderly patients. Fifth, most patients and nonspecialized healthcare professionals still lack an awareness of the significance of fatty liver both in terms of intrahepatic and extrahepatic disease and cancer. Recently, an international expert panel proposed a new definition of fatty liver: metabolic dysfunction-associated fatty liver disease (MAFLD). One feature of MAFLD is that metabolic dysfunction is a prerequisite for diagnosis. Pertinent to regional issues, MAFLD also provides its diagnostic criteria in leanormal-weight individuals. Furthermore, MAFLD is independent of any concomitant liver disease, including viral hepatitis. Therefore, MAFLD may be a more suitable definition for fatty liver in the Asia-Pacific region. In this review, we introduce the regional characteristics of fatty liver and discuss the advantages of MAFLD for improving clinical practice for liver disease in the region.