scholarly journals Internet of Things and Artificial Intelligence in Healthcare During COVID-19 Pandemic—A South American Perspective

2020 ◽  
Vol 8 ◽  
Author(s):  
Parag Chatterjee ◽  
Andreína Tesis ◽  
Leandro J. Cymberknop ◽  
Ricardo L. Armentano
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Mobile Apps ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Healthcare Management ◽  
Healthcare Personnel ◽  
South American ◽  
Risk Modeling ◽  
Holistic View

The shudders of the COVID-19 pandemic have projected newer challenges in the healthcare domain across the world. In South American scenario, severe issues and difficulties have been noticed in areas like patient consultations, remote monitoring, medical resources, healthcare personnel etc. This work is aimed at providing a holistic view to the digital healthcare during the times of COVID-19 pandemic in South America. It includes different initiatives like mobile apps, web-platforms and intelligent analyses toward early detection and overall healthcare management. In addition to discussing briefly the key issues toward extensive implementation of eHealth paradigms, this work also sheds light on some key aspects of Artificial Intelligence and the Internet of Things along their potential applications like clinical decision support systems and predictive risk modeling, especially in the direction of combating the emergent challenges due to the COVID-19 pandemic.

scholarly journals A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6209
Author(s):  
Andrei Velichko
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Support ◽  
Data Analysis ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Classification Accuracy ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Data

Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the~3–10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems.

The Role of Artificial Intelligence (AI) in Cancer Treatment in Low-Middle Income Countries: The Watson for Oncology (WfO) Experience (Preprint)

2021 ◽  
Author(s):  
Angela Rui ◽  
Srinivas Emani ◽  
Hermano Alexandre Lima Rocha ◽  
Rubina F. Rizvi ◽  
Sergio Ferreira Juaçaba ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cancer Treatment ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Cultural Barriers ◽  
Successful Implementation ◽  
Middle Income ◽  
Middle Income Countries ◽  
Cancer Management ◽  
Low Middle Income Countries

UNSTRUCTURED As technology continues to improve, healthcare systems have the opportunity to utilize a variety of innovative tools for decision making that extend beyond traditional clinical decision support systems (CDSSs). The feasibility and efficacy integrating artificial intelligence (AI) systems into medical practice has shown variable success, especially in resource-poor areas. In this paper, we cover the existing challenges surrounding cancer treatment in low-middle income countries (LMICs). By focusing on the implementation of an AI-based CDSS for oncology, we aim to demonstrate how AI can be both beneficial and challenging for cancer management globally. Additionally, we summarize current physician perspectives from China, India, Brazil, Thailand, and Mexico in regard to their experiences and recommendations for improving the system. By doing so, we hope to highlight the need for additional research on user experience and unique cultural barriers for the successful implementation of AI in LMICs.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

2020 ◽  
pp. 167-186
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

scholarly journals Psychiatric Advance Directives and Artificial Intelligence: A Conceptual Framework for Theoretical and Ethical Principles

2021 ◽  
Vol 11 ◽  
Author(s):  
Stéphane Mouchabac ◽  
Vladimir Adrien ◽  
Clara Falala-Séchet ◽  
Olivier Bonnot ◽  
Redwan Maatoug ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Advance Directives ◽  
Ethical Issues ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Legal Framework ◽  
Ethical Principles ◽  
Psychiatric Advance Directives ◽  
Contractual Relationship

The patient's decision-making abilities are often altered in psychiatric disorders. The legal framework of psychiatric advance directives (PADs) has been made to provide care to patients in these situations while respecting their free and informed consent. The implementation of artificial intelligence (AI) within Clinical Decision Support Systems (CDSS) may result in improvements for complex decisions that are often made in situations covered by PADs. Still, it raises theoretical and ethical issues this paper aims to address. First, it goes through every level of possible intervention of AI in the PAD drafting process, beginning with what data sources it could access and if its data processing competencies should be limited, then treating of the opportune moments it should be used and its place in the contractual relationship between each party (patient, caregivers, and trusted person). Second, it focuses on ethical principles and how these principles, whether they are medical principles (autonomy, beneficence, non-maleficence, justice) applied to AI or AI principles (loyalty and vigilance) applied to medicine, should be taken into account in the future of the PAD drafting process. Some general guidelines are proposed in conclusion: AI must remain a decision support system as a partner of each party of the PAD contract; patients should be able to choose a personalized type of AI intervention or no AI intervention at all; they should stay informed, i.e., understand the functioning and relevance of AI thanks to educational programs; finally, a committee should be created for ensuring the principle of vigilance by auditing these new tools in terms of successes, failures, security, and relevance.

A Framework for Data and Mined Knowledge Interoperability in Clinical Decision Support Systems

2012 ◽  
pp. 84-110 ◽  
Author(s):  
Reza S. Kazemzadeh ◽  
Kamran Sartipi ◽  
Priya Jayaratna
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Guideline ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Healthcare Personnel

Due to reliance on human knowledge, the practice of medicine is subject to errors that endanger patients’ health and cause substantial financial loss to healthcare institutions. Computer-based decision support systems assist healthcare personnel to improve quality of clinical practice. Currently, most clinical guideline modeling languages represent decision-making knowledge in terms of basic logical expressions. In this paper, we focus on encoding, sharing, and using results of data mining analyses to influence decision making within Clinical Decision Support Systems. A knowledge management framework is proposed that addresses the issues of data and knowledge interoperability by adopting healthcare and data mining modeling standards. In a further step, data mining results are incorporated into a guideline-based decision support system. A prototype tool has been developed to provide an environment for clinical guideline authoring and execution. Also, three real world case studies have been presented, one of which is used as a running example throughout the paper.

scholarly journals Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study (Preprint)

2020 ◽  
Author(s):  
Mengting Ji ◽  
Georgi Z Genchev ◽  
Hengye Huang ◽  
Ting Xu ◽  
Hui Lu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Measurement Instrument ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
User Acceptance ◽  
Evaluation Framework

BACKGROUND Clinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence–enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice. OBJECTIVE The purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence–enabled clinical decision support system evaluation framework. METHODS An artificial intelligence–enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents. RESULTS The Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (<i>χ<sub>26</sub><sup>2</sup></i>=36.984; <i>P</i>=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension. CONCLUSIONS User acceptance is the central dimension of artificial intelligence–enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.

scholarly journals Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study

10.2196/25929 ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. e25929
Author(s):  
Mengting Ji ◽  
Georgi Z Genchev ◽  
Hengye Huang ◽  
Ting Xu ◽  
Hui Lu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Measurement Instrument ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
User Acceptance ◽  
Evaluation Framework

Background Clinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence–enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice. Objective The purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence–enabled clinical decision support system evaluation framework. Methods An artificial intelligence–enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents. Results The Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (χ262=36.984; P=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension. Conclusions User acceptance is the central dimension of artificial intelligence–enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

2020 ◽  
pp. 390-409
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

scholarly journals Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

2019 ◽  
Vol 28 (01) ◽  
pp. 120-127 ◽  
Author(s):  
Stefania Montani ◽  
Manuel Striani
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Domain Knowledge ◽  
Knowledge Integration ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Data Driven ◽  
Knowledge Based ◽  
Hybrid Approaches

Objectives: This survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to “classical" knowledge-based ones, and to consider the issues raised and their possible solutions. Methods: We included PubMed and Web of ScienceTM publications, focusing on contributions describing clinical DSSs that adopted one or more AI methodologies. Results: We selected 75 papers, 49 of which describe approaches in the data-driven AI area, 20 present purely knowledge-based DSSs, and 6 adopt hybrid approaches relying on both formalized knowledge and data. Conclusions: Recent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.

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