☆ 3.8 Article

Intensive Care Unit Physicians' Perspectives on Artificial Intelligence-Based Clinical Decision Support Tools: Preimplementation Survey Study

JMIR HUMAN FACTORS (2023)

Related references

Note: Only part of the references are listed.

Review Health Care Sciences & Services

Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review

Han Shi Jocelyn Chew et al.

Summary: This study provides an overview of the perceptions and needs of AI in health care to increase its adoption. The use of AI is perceived as positive due to its availability, ease of use, and potential to improve efficiency and reduce costs. However, concerns are raised regarding data privacy, patient safety, and technological maturity. Suggestions for improving AI adoption include enhancing personalization and customizability, empathy and personification of AI chatbots, and educating the public on AI capabilities.

JOURNAL OF MEDICAL INTERNET RESEARCH (2022)

Add to Collection

Article Computer Science, Information Systems

Defining AMIA's artificial intelligence principles

Anthony E. Solomonides et al.

Summary: Recent advances in AI technology and its deployment in healthcare and other fields have raised concerns about ethical principles and governance. In response, a set of principles has been proposed to guide the commission, creation, implementation, maintenance, and retirement of AI systems for responsible and ethical use.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2022)

Add to Collection

Article Economics

The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge

Juliette de Vos et al.

Summary: This study evaluates the cost-effectiveness of the machine learning prediction model Pacmed Critical (PC) compared to standard care for Dutch ICU patients. The study finds that PC has the potential to be cost-effective for Dutch ICUs within a 1-year time horizon, with a 71% likelihood of cost-effectiveness at a willingness-to-pay threshold of €30,000 per quality-adjusted life-year.

VALUE IN HEALTH (2022)

Add to Collection

Review Health Care Sciences & Services

Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review

Anne A. H. de Hond et al.

Summary: This article collects guidance on the development, evaluation, and implementation of AI-based prediction models in healthcare, finding that there is more detailed guidance for early stages such as data preparation, AIPM development, and AIPM validation, while later stages such as software development, impact assessment, and implementation have received less attention.

NPJ DIGITAL MEDICINE (2022)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Developing machine learning models for prediction of mortality in the medical intensive care unit

Beatriz Nistal-Nuno

Summary: This study compared the performance of three machine learning models with conventional severity-of-illness scoring systems in predicting mortality risk in the Medical Intensive Care Unit (MICU). The results showed that the machine learning models XGB and BN outperformed the conventional systems in terms of discrimination, calibration, and accuracy. The naive Bayes network model NB also showed better discrimination and accuracy compared to the conventional systems. Among all the models, the XGB model had the best performance.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2022)

Add to Collection

Article Biochemistry & Molecular Biology

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Baptiste Vasey et al.

Summary: The article introduces the DECIDE-AI checklist, which includes key items that should be reported in early-stage clinical studies of AI-based decision support systems. It emphasizes the importance of responsible and transparent deployment of AI systems in healthcare. The checklist was developed through a consensus-based process involving multiple stakeholders.

NATURE MEDICINE (2022)

Add to Collection

Article Health Care Sciences & Services

Clinicians' Perceptions of an Artificial Intelligence-Based Blood Utilization Calculator: Qualitative Exploratory Study

Avishek Choudhury et al.

Summary: This study explores how clinicians perceive an AI-based decision support system and identifies workload and usability, as well as clinical decision-making, as factors hindering its use. The findings emphasize that effective integration of AI-based decision support systems requires multidisciplinary engagement and user training, in addition to analytical efficacy.

JMIR HUMAN FACTORS (2022)

Add to Collection

Review Health Care Sciences & Services

Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter

Davy van de Sande et al.

Summary: This study proposes a step-by-step overview to enhance clinicians' understanding and promote quality of medical AI research. By summarizing key elements and current guidelines, this overview can help facilitate the translation of AI from the lab to the bedside.

BMJ HEALTH & CARE INFORMATICS (2022)

Add to Collection

Article Health Care Sciences & Services

Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey

Oliver Maassen et al.

Summary: Physicians in German university hospitals show a generally positive attitude towards using artificial intelligence in medicine, expecting it to assist in clinical decision making, especially in fields like radiology and pathology. They see potential in AI for identifying drug interactions, but are more reserved in its use for diagnosing psychiatric diseases. The majority of physicians also support open access to anonymized patient databases for medical and biomedical research.

JOURNAL OF MEDICAL INTERNET RESEARCH (2021)

Add to Collection

Article Multidisciplinary Sciences

A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology

Jane Scheetz et al.

Summary: The survey revealed that the majority of medical specialists believe that artificial intelligence will improve healthcare outcomes and impact workforce demands in the next decade. Key benefits of artificial intelligence include improved disease screening and streamlining of monotonous tasks, while concerns revolve around medical liability and the involvement of technology companies in healthcare.

SCIENTIFIC REPORTS (2021)

Add to Collection

Review Medicine, General & Internal

Artificial Intelligence for Clinical Decision Support in Sepsis

Miao Wu et al.

Summary: Artificial intelligence has great potential in clinical decision support for sepsis, with applications in prediction, diagnosis, prognosis assessment, and clinical management. However, the implementation and acceptance of this non-traditional method in clinical settings still face challenges.

FRONTIERS IN MEDICINE (2021)

Add to Collection

Article Health Care Sciences & Services

Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study

Stina Matthiesen et al.

Summary: The study found that the ML-based prediction tool has the potential to support clinical decision-making in ICD remote monitoring by providing reassurance, increasing confidence, acting as a second opinion, reducing information search time, and enabling delegation of decisions. However, the tool did not lead to changes in clinical action in cases where data quality was poor or when VT/VF predictions were irrelevant for patient evaluation. Addressing the sociotechnical gap between AI development and clinical implementation is essential for successful adoption in cardiac care.

JMIR HUMAN FACTORS (2021)

Add to Collection

Proceedings Paper Computer Science, Interdisciplinary Applications

Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens

Maia Jacobs et al.

Summary: Research investigated clinicians' perceptions of using DSTs in antidepressant treatment decisions and found that DSTs need to engage with the healthcare sociotechnical system. Results suggest that clinical DSTs should be designed as multi-user systems that support patient-provider collaboration and offer on-demand explanations to address discrepancies between predictions and current standards of care.

CHI '21: PROCEEDINGS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Artificial intelligence and the future of psychiatry: Insights from a global physician survey

P. Murali Doraiswamy et al.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2020)

Add to Collection

Article Computer Science, Information Systems

A lesson in implementation: A pre-post study of providers' experience with artificial intelligence-based clinical decision support

Santiago Romero-Brufau et al.

INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS (2020)

Add to Collection

Article Medical Informatics

Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study

Mark P. Sendak et al.

JMIR MEDICAL INFORMATICS (2020)

Add to Collection

Article Computer Science, Information Systems

Envisioning an artificial intelligence documentation assistant for future primary care consultations: A co-design study with general practitioners

A. Baki Kocaballi et al.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2020)

Add to Collection

Article Medical Informatics

Explainability for artificial intelligence in healthcare: a multidisciplinary perspective

Julia Amann et al.

BMC MEDICAL INFORMATICS AND DECISION MAKING (2020)

Add to Collection

Article Gastroenterology & Hepatology

Physician sentiment toward artificial intelligence (AI) in colonoscopic practice: a survey of US gastroenterologists

Vaibhav Wadhwa et al.

ENDOSCOPY INTERNATIONAL OPEN (2020)

Add to Collection

Article Computer Science, Information Systems

How did you get to this number? Stakeholder needs for implementing predictive analytics: a pre-implementation qualitative study

Natalie C. Benda et al.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2020)

Add to Collection

Review Health Care Sciences & Services

A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

Evangelia Christodoulou et al.

JOURNAL OF CLINICAL EPIDEMIOLOGY (2019)

Add to Collection

Article Medicine, General & Internal

Key challenges for delivering clinical impact with artificial intelligence

Christopher J. Kelly et al.

BMC MEDICINE (2019)

Add to Collection

Article Health Care Sciences & Services

The Last Mile: Where Artificial Intelligence Meets Reality

Enrico Coiera

JOURNAL OF MEDICAL INTERNET RESEARCH (2019)

Add to Collection

Article Health Care Sciences & Services

Physician Confidence in Artificial Intelligence: An Online Mobile Survey

Songhee Oh et al.

JOURNAL OF MEDICAL INTERNET RESEARCH (2019)

Add to Collection

Review Anesthesiology

Big data and targeted machine learning in action to assist medical decision in the ICU

Romain Pirracchio et al.

ANAESTHESIA CRITICAL CARE & PAIN MEDICINE (2019)

Add to Collection

Article Health Care Sciences & Services

Physician perspectives on integration of artificial intelligence into diagnostic pathology

Shihab Sarwar et al.

NPJ DIGITAL MEDICINE (2019)

Add to Collection

Review Critical Care Medicine

Use of machine learning to analyse routinely collected intensive care unit data: a systematic review

Duncan Shillan et al.

CRITICAL CARE (2019)

Add to Collection

Review Surgery

Artificial intelligence systems for complex decision-making in acute care medicine: a review

Lawrence A. Lynn

PATIENT SAFETY IN SURGERY (2019)

Add to Collection

Proceedings Paper Computer Science, Cybernetics

Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes

Qian Yang et al.

CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (2019)

Add to Collection

Editorial Material Critical Care Medicine

Can this patient be safely discharged from the ICU?

Andrew A. Kramer et al.

INTENSIVE CARE MEDICINE (2016)

Add to Collection

Review Computer Science, Cybernetics

Technology acceptance model: a literature review from 1986 to 2013

Nikola Marangunic et al.

UNIVERSAL ACCESS IN THE INFORMATION SOCIETY (2015)

Add to Collection

© Peeref 2019-2024. All rights reserved.