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Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, improved patient outcomes, early diagnosis, augmented clinician capabilities, enhanced operational efficiencies, or improved medical service accessibility. However, deploying AI-driven tools in the healthcare ecosystem could be challenging. This paper categorizes AI applications in healthcare and comprehensively examines the challenges associated with deploying AI in medical practices at scale. As AI continues to make strides in healthcare, its integration presents various challenges, including production timelines, trust generation, privacy concerns, algorithmic biases, and data scarcity. The paper highlights that flawed business models and wrong workflows in healthcare practices cannot be rectified merely by deploying AI-driven tools. Healthcare organizations should re-evaluate root problems such as misaligned financial incentives (e.g., fee-for-service models), dysfunctional medical workflows (e.g., high rates of patient readmissions), poor care coordination between different providers, fragmented electronic health records systems, and inadequate patient education and engagement models in tandem with AI adoption. This study also explores the need for a cultural shift in viewing AI not as a threat but as an enabler that can enhance healthcare delivery and create new employment opportunities while emphasizing the importance of addressing underlying operational issues. The necessity of investments beyond finance is discussed, emphasizing the importance of human capital, continuous learning, and a supportive environment for AI integration. The paper also highlights the crucial role of clear regulations in building trust, ensuring safety, and guiding the ethical use of AI, calling for coherent frameworks addressing transparency, model accuracy, data quality control, liability, and ethics. Furthermore, this paper underscores the importance of advancing AI literacy within academia to prepare future healthcare professionals for an AI-driven landscape. Through careful navigation and proactive measures addressing these challenges, the healthcare community can harness AI's transformative power responsibly and effectively, revolutionizing healthcare delivery and patient care. The paper concludes with a vision and strategic suggestions for the future of healthcare with AI, emphasizing thoughtful, responsible, and innovative engagement as the pathway to realizing its full potential to unlock immense benefits for healthcare organizations, physicians, nurses, and patients while proactively mitigating risks. • This study categorizes AI applications in healthcare and analyzes their deployment challenges at scale in medical practices. • The paper emphasizes a cultural shift to view AI as a healthcare delivery enhancer and job creator, not a threat. • To integrate AI, investments in finance and human capital, continuous learning, and a supportive environment are needed. • Clear regulatory frameworks should be developed to build trust, ensure safety, and guide the ethical use of AI in healthcare. • The study provides strategic suggestions for responsible AI use in healthcare to maximize benefits and minimize risks. [ABSTRACT FROM AUTHOR]

Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions. This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023. Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed. • Use of multi-agent systems to study biological interactions in oncology. • Multi-agent systems enable modeling of cell signaling network dynamics. • Review of multi-agent systems application for personalized therapeutic strategies. • Meta-heuristic algorithms found to be the most used models in recent studies. [ABSTRACT FROM AUTHOR]

Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The precise control over the temporary loss of consciousness is vital to ensure safe, pain-free procedures. Traditional methods of depth of anaesthesia (DoA) assessment, reliant on physical characteristics, have proven inconsistent due to individual variations. In response, electroencephalography (EEG) techniques have emerged, with indices such as the Bispectral Index offering quantifiable assessments. This literature review explores the current scope and frontier of DoA research, emphasising methods utilising EEG signals for effective clinical monitoring. This review offers a critical synthesis of recent advances, specifically focusing on electroencephalography (EEG) techniques and their role in enhancing clinical monitoring. By examining 117 high-impact papers, the review delves into the nuances of feature extraction, model building, and algorithm design in EEG-based DoA analysis. Comparative assessments of these studies highlight their methodological approaches and performance, including clinical correlations with established indices like the Bispectral Index. The review identifies knowledge gaps, particularly the need for improved collaboration for data access, which is essential for developing superior machine learning models and real-time predictive algorithms for patient management. It also calls for refined model evaluation processes to ensure robustness across diverse patient demographics and anaesthetic agents. The review underscores the potential of technological advancements to enhance precision, safety, and patient outcomes in anaesthesia, paving the way for a new standard in anaesthetic care. The findings of this review contribute to the ongoing discourse on the application of EEG in anaesthesia, providing insights into the potential for technological advancement in this critical area of medical practice. • Review of the current depth of anaesthesia analysis methods • Emphasis on machine learning techniques based on EEG signal analysis • Trends and future direction of EEG-based depth of anaesthesia analysis methods [ABSTRACT FROM AUTHOR]

The ongoing digital revolution in the healthcare sector, emphasized by bodies like the US Food and Drug Administration (FDA), is paving the way for a shift towards person-centric healthcare models. These models consider individual needs, turning patients from passive recipients to active participants. A key factor in this shift is Artificial Intelligence (AI), which has the capacity to revolutionize healthcare delivery due to its ability to personalize it. With the rise of software in healthcare and the proliferation of the Internet of Things (IoT), a surge of digital data is being produced. This data, alongside improvements in AI's explainability, is facilitating the spread of person-centric healthcare models, aiming at improving health management and patient experience. This paper outlines a human-centered methodology for the development of an AI-as-a-service platform with the goal of broadening access to personalized healthcare. This approach places humans at its core, aiming to augment, not replace, human capabilities and integrate in current processes. The primary research question guiding this study is: "How can Human-Centered AI principles be considered when designing an AI-as-a-service platform that democratizes access to personalized healthcare?" This informed both our research direction and investigation. Our approach involves a design fiction methodology, engaging clinicians from different domains to gather their perspectives on how AI can meet their needs by envisioning potential future scenarios and addressing possible ethical and social challenges. Additionally, we incorporate Meta-Design principles, investigating opportunities for users to modify the AI system based on their experiences. This promotes a platform that evolves with the user and considers many different perspectives. • Design fiction method effectively probes AI healthcare scenarios, illuminating challenges and opportunities. • Meta-design opportunities identified in personalizing AI-based healthcare interventions. • User journey maps reveal potential biases in AI decision-making and underscore ethical considerations. • AI integration in healthcare requires collaborative efforts between HCI researchers and clinicians. • Aggregated findings across case studies underscore the need for an AI-as-a-service platform prioritizing ethics and personalization. [ABSTRACT FROM AUTHOR]

In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support systems have the potential to enhance diagnosis and management. However, the scope and challenges of applying these technologies remain unclear. This scoping review aims to investigate the current state of AI applications in the development of intelligent decision support systems for dementia care. We conducted a comprehensive scoping review of empirical studies that utilised AI-powered clinical decision support systems in dementia care. The results indicate that AI applications in dementia care primarily focus on diagnosis, with limited attention to other aspects outlined in the World Health Organization (WHO) Global Action Plan on the Public Health Response to Dementia 2017–2025 (GAPD). A trifecta of challenges, encompassing data availability, cost considerations, and AI algorithm performance, emerges as noteworthy barriers in adoption of AI applications in dementia care. To address these challenges and enhance AI reliability, we propose a novel approach: a digital twin-based patient journey model. Future research should address identified gaps in GAPD action areas, navigate data-related obstacles, and explore the implementation of digital twins. Additionally, it is imperative to emphasize that addressing trust and combating the stigma associated with AI in healthcare should be a central focus of future research directions. • This paper examines the trajectory of AI-based CDSS application in dementia care. • It focuses on GAPD's seven action areas, highlighting AI's role in improving diagnosis and care. • It identifies limitations in data volume and cost as barriers to their development and usage in practice. • A model utilising digital twin technology is presented as a potential approach to addressing these identified limitations. [ABSTRACT FROM AUTHOR]