Abstract:Background. Artificial intelligence (AI) has developed rapidly, and its application extends to clinical decision support system (CDSS) for improving healthcare quality. However, the interpretability of AI-driven CDSS poses significant challenges to widespread application. Objective. This study is a review of the knowledge-based and data-based CDSS literature regarding interpretability in health care. It highlights the relevance of interpretability for CDSS and the area for improvement from technological and me… Show more
“…An interdisciplinary perspective, particularly from fields such as computer science, ethics in artificial intelligence, and psychology, offers new directions and methodologies for addressing the issue of model interpretability [ 151 , 152 , 153 ]. Integrating concepts like attention mechanisms [ 154 ] and local interpretable models can uncover the rationale behind model decisions [ 155 ].…”
This article explores the challenges of continuum and magnetic soft robotics for medical applications, extending from model development to an interdisciplinary perspective. First, we established a unified model framework based on algebra and geometry. The research progress and challenges in principle models, data-driven, and hybrid modeling were then analyzed in depth. Simultaneously, a numerical analysis framework for the principle model was constructed. Furthermore, we expanded the model framework to encompass interdisciplinary research and conducted a comprehensive analysis, including an in-depth case study. Current challenges and the need to address meta-problems were identified through discussion. Overall, this review provides a novel perspective on understanding the challenges and complexities of continuum and magnetic soft robotics in medical applications, paving the way for interdisciplinary researchers to assimilate knowledge in this domain rapidly.
“…An interdisciplinary perspective, particularly from fields such as computer science, ethics in artificial intelligence, and psychology, offers new directions and methodologies for addressing the issue of model interpretability [ 151 , 152 , 153 ]. Integrating concepts like attention mechanisms [ 154 ] and local interpretable models can uncover the rationale behind model decisions [ 155 ].…”
This article explores the challenges of continuum and magnetic soft robotics for medical applications, extending from model development to an interdisciplinary perspective. First, we established a unified model framework based on algebra and geometry. The research progress and challenges in principle models, data-driven, and hybrid modeling were then analyzed in depth. Simultaneously, a numerical analysis framework for the principle model was constructed. Furthermore, we expanded the model framework to encompass interdisciplinary research and conducted a comprehensive analysis, including an in-depth case study. Current challenges and the need to address meta-problems were identified through discussion. Overall, this review provides a novel perspective on understanding the challenges and complexities of continuum and magnetic soft robotics in medical applications, paving the way for interdisciplinary researchers to assimilate knowledge in this domain rapidly.
“…The integration of Artificial Intelligence (AI) into medical healthcare systems has acquired important attentiveness from policymakers, researchers, and practitioners correspondingly. This fragment reviews current literature to postulate a comprehensive understanding of the existing state of expertise observing the ethical associations and scenarios of AI in healthcare [17,18]. The researchers highlight how AI is revolutionizing several aspects of healthcare.…”
Artificial intelligence (AI) is the main branch of computer science that permits advanced machines to interpret and analyze complex healthcare data elaborating the recent challenges in the medical field of study. The current state of AI applications in healthcare is examined in this systematic literature review, with an emphasis on the technology's accomplishments, difficulties, and potential. The wide breadth of AI technologies used in healthcare settings, such as robots, computer vision, machine learning, and natural language processing, is highlighted in this review through an extensive analysis of peer-reviewed publications. It talks about how customized medicine, predictive analytics, illness detection, and treatment planning are just a few of the areas of healthcare delivery that AI-driven technologies are transforming. According to research by investment bank Goldman Sachs, 300 million full-time employees could be replaced by artificial intelligence (AI). In the US and Europe, it might replace 25% of labor duties, but it might also lead to an increase in productivity and the creation of new jobs. Additionally, it might eventually result in a 7% rise in the global annual value of products and services produced. Additionally, the paper projects that approximately 25% of all employment might be performed totally by AI and that two-thirds of jobs in the U.S. and Europe "are exposed to some degree of AI automation. "The most likely groups to be impacted by workforce automation are educated white-collar workers making up to $80,000 annually, according to research from OpenAI and the University of Pennsylvania. According to a McKinsey Global Institute study, developments in digitalization, robots, and artificial intelligence may require at least 14% of workers worldwide to change jobs by 2030.
“…It evaluated interpretability techniques such as SHAP, Grad-CAM, and LIME and discussed their usability and reliability, aiming to advance XAI in healthcare for researchers and professionals. Also, Authors in [17] reviewed AI-driven CDSS from 2011 to 2020, highlighting the value of interpretability, exploring techniques, and stressing its research prospect in healthcare applications. Similarly, the research study [42] attended demonstrates the significance of openness in AI-driven healthcare tools, providing a framework to quantify transparency and reliability.…”
Section: Related Literaturementioning
confidence: 99%
“…A transparent AI design in healthcare offers vibrant explanations for its diagnostic recommendations, detailing the functions or patterns in client data that led to a specific forecast [15], [16]. Interpretability, on the other hand, distillates human users (doctors) capability to interpret and understand the outputs produced by an AI design [17]. An interpretable AI design supplies insights into how it arrives at its conclusions in an intuitive and significant way to users [18], [19].…”
The increased utilization of disruptive health and biomedical informatics technologies, such as artificial intelligence (AI), has accelerated medical operations from patient-centered medical experience data management to simplified medical procedures in this generative era. As these technologies integrate into traditional approaches, they raise critical medical concerns, entailing transparency and interpretability of these AI models. This study conducts a systematic literature review (SLR) and presents an exhaustive review of the studies using data collection procedures and publicly available academic databases. 1837 articles published between 2014 and 2024 were obtained from eight popular academic databases: PubMed, ACM Library, Springer, Scopus, IEEE Xplore, ScienceDirect, Google Scholar, and Web of Science. A comprehensive screening process was used, and 148 articles were considered based on the relevance of the AI method to healthcare and biomedical. The studied studies demonstrate that the majority of medical people still find it complex to effectively explain the reasoning behind the decisions AI models make during biomedical experiments, leading to limited trust, biased decision-making, and unknown patient data safety. Model-agnostic strategies and explainable AI (XAI) frameworks are inspected, together with crucial datasets for training and assessment. The main challenges are AI model intricacy and regulatory compliance, while future trends highlight fairness and predisposition mitigation. Limited studies are focusing on improving AI openness, trust, and interpretability. Towards the end, it reveals that there is still a big research gap in descriptive explainable AI models in healthcare when integrating AI into clinical practice while maintaining ethical standards and patient-centric care.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.