Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

Artificial Intelligence Techniques in Medical Imaging: A Systematic Review

This scientific review presents a comprehensive overview of medical imaging modalities and their diverse applications in artificial intelligence (AI)-based disease classification and segmentation. The paper begins by explaining the fundamental concepts of AI, machine learning (ML), and deep learning (DL). It provides a summary of their different types to establish a solid foundation for the subsequent analysis. The prmary focus of this study is to conduct a systematic review of research articles that examine disease classification and segmentation in different anatomical regions using AI methodologies. The analysis includes a thorough examination of the results reported in each article, extracting important insights and identifying emerging trends. Moreover, the paper critically discusses the challenges encountered during these studies, including issues related to data availability and quality, model generalization, and interpretability. The aim is to provide guidance for optimizing technique selection. The analysis highlights the prominence of hybrid approaches, which seamlessly integrate ML and DL techniques, in achieving effective and relevant results across various disease types. The promising potential of these hybrid models opens up new opportunities for future research in the field of medical diagnosis. Additionally, addressing the challenges posed by the limited availability of annotated medical images through the incorporation of medical image synthesis and transfer learning techniques is identified as a crucial focus for future research efforts

“It Is a Moving Process”:Understanding the Evolution of Explainability Needs of Clinicians in Pulmonary Medicine

Clinicians increasingly pay attention to Artificial Intelligence (AI) to improve the quality and timeliness of their services. There are converging opinions on the need for Explainable AI (XAI) in healthcare. However, prior work considers explanations as stationary entities with no account for the temporal dynamics of patient care. In this work, we involve 16 Idiopathic Pulmonary Fibrosis (IPF) clinicians from a European university medical centre and investigate their evolving uses and purposes for explainability throughout patient care. By applying a patient journey map for IPF, we elucidate clinicians' informational needs, how human agency and patient-specific conditions can influence the interaction with XAI systems, and the content, delivery, and relevance of explanations over time. We discuss implications for integrating XAI in clinical contexts and more broadly how explainability is defined and evaluated. Furthermore, we reflect on the role of medical education in addressing epistemic challenges related to AI literacy.</p

“It Is a Moving Process”:Understanding the Evolution of Explainability Needs of Clinicians in Pulmonary Medicine

Clinicians increasingly pay attention to Artificial Intelligence (AI) to improve the quality and timeliness of their services. There are converging opinions on the need for Explainable AI (XAI) in healthcare. However, prior work considers explanations as stationary entities with no account for the temporal dynamics of patient care. In this work, we involve 16 Idiopathic Pulmonary Fibrosis (IPF) clinicians from a European university medical centre and investigate their evolving uses and purposes for explainability throughout patient care. By applying a patient journey map for IPF, we elucidate clinicians' informational needs, how human agency and patient-specific conditions can influence the interaction with XAI systems, and the content, delivery, and relevance of explanations over time. We discuss implications for integrating XAI in clinical contexts and more broadly how explainability is defined and evaluated. Furthermore, we reflect on the role of medical education in addressing epistemic challenges related to AI literacy.</p

Early development of decision support systems based on artificial intelligence: an application to postoperative complications and a cross-specialty reporting guideline for early-stage clinical evaluation

Background: Complications after major surgery occur in a similar manner internationally but the success of response process in preventing death varies widely depending on speed and appropriateness. Artificial intelligence (AI) offers new opportunities to provide support to the decision making of clinicians in this stressful situation when uncertainty is high. However, few AI systems have been robustly and successfully tested in real-world clinical settings. Whilst preparing to develop an AI decision support algorithm and planning to evaluate it in real-world settings, a lack of appropriate guidance on reporting early clinical evaluation of such systems was identified. Objectives: The objectives of this work were twofold: i) to develop a prototype of AI system to improve the management of postoperative complications; and ii) to understand expert consensus on reporting standards for early-stage evaluation of AI systems in live clinical settings. Methods: I conducted and thematically analysed interviews with clinicians to identify their main challenges and support needs when managing postoperative complications. I then systematically reviewed the literature on the impact of AI-based decision support systems on clinicians’ diagnostic performance. A model based on unsupervised clustering and providing prescription recommendations was developed, optimised, and tested on an internal hold out dataset. Finally, I conducted a Delphi process, to reach expert consensus on minimum reporting standards for the early-stage clinical evaluation of AI systems in live clinical settings. Results: 12 interviews were conducted with junior and senior clinicians identifying 54 themes about challenges, common errors, strategies, and support needs when managing postoperative complications. 37 studies were included in the systematic review, which found no robust evidence of a positive association between the use of AI decision support systems and improved clinician diagnostic performance. The developed algorithm showed no improvement in recall at position ten compared to a list of the most common prescriptions in the study population. When considering the prevalence of the individual prescriptions, the algorithm showed a 12% relative increase in performance compared to the same baseline. 151 experts participated in the Delphi study, representing 18 countries and 20 stakeholder groups. The final DECIDE-AI checklist comprises 27 items, accompanied by Explanation & Elaboration sections for each. Conclusion: The proposed algorithm offers a proof of concept for an AI system to improve the management of postoperative complications. However, it needs further development and evaluation before claiming clinical utility. The DECIDE-AI guideline provides a practicable checklist for researchers reporting on the implementation of AI decision support systems in clinical settings, and merits future iterative evaluation-update cycles in practice

In-Suit Doppler Technology Assessment

The objective of this program was to perform a technology assessment survey of non-invasive air embolism detection utilizing Doppler ultrasound methodologies. The primary application of this technology will be a continuous monitor for astronauts while performing extravehicular activities (EVA's). The technology assessment was to include: (1) development of a full understanding of all relevant background research; and (2) a survey of the medical ultrasound marketplace for expertise, information, and technical capability relevant to this development. Upon completion of the assessment, LSR was to provide an overview of technological approaches and R&D/manufacturing organizations

Artificial Intelligence in Medicine and Healthcare: applications, availability and societal impact

This report reviews and classifies the current and near-future applications of Artificial Intelligence (AI) in Medicine and Healthcare according to their ethical and societal impact and the availability level of the various technological implementations. It provides conceptual foundations for well-informed policy-oriented work, research, and forward-looking activities that address the opportunities and challenges created in the field of AI in Medicine and Healthcare. This report is aimed for policy developers, but it also makes contributions that are of interest for researchers studying the impact and the future of AI on Healthcare, for scientific and technological stakeholders in this field and for the general public. This report is based on an analysis of the state of the art of research and technology, including software, personal monitoring devices, genetic tests and editing tools, personalized digital models, online platforms, augmented reality devices, and surgical and companion robotics. From this analysis, it is presented the concept of “extended personalized medicine”, and it is explored the public perception of medical AI systems, and how they show, simultaneously, extraordinary opportunities and drawbacks. In addition, this report addresses the transformation of the roles of doctors and patients in an age of ubiquitous information and identifies three main paradigms in AI-supported Medicine: “fake-based”, “patient-generated”, and “scientifically tailored” views. This Report presents: - An updated overview of the many aspects related to the social impact of Artificial Intelligence and its applications in Medicine and Health. A new ‘Technology Availability Scale’ is defined to evaluate and compare their current status. - Recent examples of the growing social concerns and debates in the general press, social media and other web-bases sources. - A ‘Visual Overview of AI and AI-mediated technologies in Medicine and Healthcare’, in which two figures show, respectively, a (newly proposed) classification according to their ethical and social impact, and the most relevant ethical and social aspects considered for such classification. Some key questions, controversies, significant, and conflicting issues are outlined for each aspect. - A ‘Structured Overview’, with a sorted list of technologies and their implementations, including perspectives, conflicting views and potential pitfalls, and a corresponding, extensive list of references. - A conclusive set of policy challenges, namely the need of informed citizens, key aspects (of AI and AI-mediated technologies in Medicine and Healthcare) to evaluate, and some recommendations towards a European leadership in this sector. - We finally relate our study with an update on the use of AI technologies to fight the SARS-CoV-2 virus and COVID-19 pandemic disease.JRC.A.5-Scientific Developmen

Artificial Intelligence in Medicine and Healthcare: applications, availability and societal impact

Comisión Europea. Joint Research Centre. Serie: JRC Science for Police ReportThis report reviews and classifies the current and near-future applications of Artificial Intelligence (AI) in Medicine and Healthcare according to their ethical and societal impact and the availability level of the various technological implementations. It provides conceptual foundations for well-informed policy-oriented work, research, and forward-looking activities that address the opportunities and challenges created in the field of AI in Medicine and Healthcare. This report is aimed for policy developers, but it also makes contributions that are of interest for researchers studying the impact and the future of AI on Healthcare, for scientific and technological stakeholders in this field and for the general public.This report is based on an analysis of the state of the art of research and technology, including software, personal monitoring devices, genetic tests and editing tools, personalized digital models, online platforms, augmented reality devices, and surgical and companion robotics. From this analysis, it is presented the concept of “extended personalized medicine”, and it is explored the public perception of medical AI systems, and how they show, simultaneously, extraordinary opportunities and drawbacks. In addition, this report addresses the transformation of the roles of doctors and patients in an age of ubiquitous information and identifies three main paradigms in AI-supported Medicine: “fake-based”, “patient-generated”, and “scientifically tailored” views.This Report presents:- An updated overview of the many aspects related to the social impact of Artificial Intelligence and its applications in Medicine and Health. A new ‘Technology Availability Scale’ is defined to evaluate and compare their current status.- Recent examples of the growing social concerns and debates in the general press, social media and other web-bases sources.- A ‘Visual Overview of AI and AI-mediated technologies in Medicine and Healthcare’, in which two figures show, respeComisión Europea. Joint Research Centr

An intelligent decision support tool for early diagnosis of functional pituitary adenomas

In this work, a web based integrated Medical Decision Support System (MDSS) tool for mainly early diagnosis of functional pituitary adenomas (i.e., somatotrophinoma, corticotrophinoma and prolactinoma) is developed. In the MDSS tool, hormone diseases are described by means of well-classified set of attributes generated from the typical sign and symptoms of disorders.The proposed tool is based on a stationary linear stochastic system model which specifically predicts the selected hormone diseases employing certain system parameters. The MDSS tool is user friendly which includes questions and answers at the opening session of the self-test. Questions and answers session will be completed by “yes” or “no” type of simple-responses. Based on our clinical results, MDSS tool yields more than 99% correct decisions on the selected hormone diseases. It is expected that effective use of the proposed MDSS tool will save substantial amount of valuable time of an expert endocrinologists and minimizes the cost of diagnosis. Furthermore, it will provide the opportunity for early diagnosis for the patient and the expert medical doctor to take the necessary preventive measures.Publisher's Versio