Computer methods and programs in biomedicine update最新文献

A study of a community of people with disabilities in a virtual world sheds new light on an important issue of health literacy that has to date remained underreported in the current body of research. Participants revealed a community of individuals who are adults role-playing via child avatars as a coping and recovery mechanism for childhood trauma. One case follows the experience of a woman who role plays an adopted child of a caring adult while another attempts to recreate different ages of herself to unpack past trauma and find therapeutic healing. This phenomenon, as well as both its risks and opportunities, are examined with important considerations for the future of digital mental health support for people who have experienced abuse as children. Researchers, policy makers, and mental health professionals are encouraged to consider the role of social virtual worlds in the future of telemedicine for PTSD therapy.

Abstract

Amid the global lockdowns, the surge in domestic violence cases has been one of the distressing consequences of the Covid19 pandemic [1]. Isolation, stress, and economic distress amongst other factors have all contributed to an increase in this form of abuse. Women have been subjected to discrimination and abuse for around 2700 years, and a clear example of such discrimination can be seen in the form of laws operating in 753 BCE that allowed the disciplining of wives [2]. The matter of domestic abuse started receiving recognition in the 1970s when it became a compulsion on all the certified hospitals by the Joint Commission on Accreditation of Health Care Organizations to refer patients of domestic abuse to authorities after treating them [3].

Background

Artificial Intelligence (AI) is a transformative force in clinical decision support (CDS) systems within healthcare. Its emergence, fuelled by the growing volume and diversity of healthcare data, offers significant potential in patient care, diagnosis, treatment, and health management. This study systematically reviews AI's role in enhancing CDS across six domains, underscoring its impact on patient outcomes and healthcare efficiency.

Methods

A four-step systematic review was conducted, involving a comprehensive literature search, application of inclusion and exclusion criteria, data extraction and synthesis, and analysis. Sources included PubMed, Embase, and Google Scholar, with papers published in English since 2019. Selected studies focused on AI's application in CDS, with 32 papers ultimately reviewed.

Results

The review identified six AI CDS domains: Data-Driven Insights and Analytics, Diagnostic and Predictive Modelling, Treatment Optimisation and Personalised Medicine, Patient Monitoring and Telehealth Integration, Workflow and Administrative Efficiency, and Knowledge Management and Decision Support. Each domain is crucial in improving various aspects of CDS, from enhancing diagnostic accuracy to optimising resource management. AI's capabilities in EHR analysis, predictive analytics, personalised treatment, and telehealth demonstrate its critical role in advancing healthcare.

Discussion

AI significantly enhances healthcare by improving diagnostic precision, predictive capabilities, and administrative efficiency. It facilitates personalised medicine, remote monitoring, and evidence-based decision-making. However, challenges such as data privacy, ethical considerations, and integration with existing systems persist. This requires collaboration among technologists, healthcare professionals, and policymakers.

Conclusion

AI is revolutionising healthcare by enhancing CDS in several domains, contributing to more efficient, effective, and patient-centric care. However, it should complement, not replace, human expertise. Future directions include ethical AI development, continuous professional development for healthcare personnel, and collaborative efforts to address challenges. This approach ensures AI's potential is fully harnessed, leading to a synergistic blend of technology and human care.

Introduction

Diabetes, a major cause of premature mortality and complications, affects millions globally, with its prevalence increasing due to lifestyle factors and aging populations. This systematic review explores the role of Artificial Intelligence (AI) in enhancing the prevention, diagnosis, and management of diabetes, highlighting the potential for personalised and proactive healthcare.

Methods

A structured four-step method was used, including extensive literature searches, specific inclusion and exclusion criteria, data extraction from selected studies focusing on AI's role in diabetes, and thorough analysis to identify specific domains and functions where AI contributes significantly.

Results

Through examining 43 experimental studies, AI has been identified as a transformative force across eight key domains in diabetes care: 1) Diabetes Management and Treatment, 2) Diagnostic and Imaging Technologies, 3) Health Monitoring Systems, 4) Developing Predictive Models, 5) Public Health Interventions, 6) Lifestyle and Dietary Management, 7) Enhancing Clinical Decision-Making, and 8) Patient Engagement and Self-Management. Each domain showcases AI's potential to revolutionize care, from personalizing treatment plans and improving diagnostic accuracy to enhancing patient engagement and predictive healthcare.

Discussion

AI's integration into diabetes care offers personalised, efficient, and proactive solutions. It enhances care accuracy, empowers patients, and provides better understanding of diabetes management. However, the successful implementation of AI requires continued research, data security, interdisciplinary collaboration, and a focus on patient-centered solutions. Education for healthcare professionals and regulatory frameworks are also crucial to address challenges like algorithmic bias and ethics.

Conclusion and Recommendations

AI in diabetes care promises improved health outcomes and quality of life through personalised and proactive healthcare. Future efforts should focus on continued investment, ensuring data security, fostering interdisciplinary collaboration, and prioritizing patient-centered solutions. Regular monitoring and evaluation are essential to adjust strategies and understand long-term impacts, ensuring AI's ethical and effective integration into healthcare.

Background

Due to the complexity of airways and the limitation of experiments, the production mechanism of the lung sounds in airways has not been fully understood, which often confuses diagnosis.

Method

A 3D geometrical model of human airways (G5-G8) has been developed based on Weibel's model. Simulation on transient airflow and the noise production during exhalation under different breathing intensities (Q = 15, 30, 45, 60, 75, 90 L/min) has been carried out with Direct Noise Computation (DNC) and Ffowcs Williams-Hawkings (FW-H) method.

Results

(1) The junctions between airways are most likely to produce lung sounds, and the peak value is located in the junction between G7 and G6 at the middle of exhalation (about 0.75 s). (2) With the increase in breathing intensity, the average sound pressure level first increases, reaches the peak value at 70–75 L/min, and then drops. (3) Higher breathing intensity is helpful to produce the feature of wheezing, namely a comparatively higher sound pressure level in the range of 200–500 Hz. Moreover, this feature is prominent with the increase in breathing intensity.