Would the Use of Artificial Intelligence in COVID-19 Patient Management Add Value to the Healthcare System?

Cossio, Manuel; Gilardino, Ramiro

doi:10.3389/fmed.2021.619202

Cited by 3 publications

(1 citation statement)

References 36 publications

(20 reference statements)

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“…AI techniques reduce operator-dependence, standardize the interpretation of images and provide stable results; they have been focused principally on COVID-19 syndrome detection [101,[182][183][184][185][186], segmentation of lung regions [133,[182][183][184][185][186], classification of lung diseases between COVID-19 positivity and COVID-19 negativity [174,[182][183][184][185][186][187][188][189].…”

Section: Lungsmentioning

confidence: 99%

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Micucci

Iula

2022

Electronics

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Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed.

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Section: Lungsmentioning

confidence: 99%

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Micucci

Iula

2022

Electronics

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A multi-criteria decision analysis framework that prioritizes economic policy to enhance value based health care during the COVID-19 pandemic

Antioch,

Kuek,

Pilla

et al. 2024

Discov Health Systems

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Objective To investigate pandemic impacts on Value-Based Health Care (VBHC) globally, using multi-criteria decision analysis (MCDA) to address challenges through prioritizing economic policies. Methods Preferred Reporting Items for Systematic reviews and Meta Analyses extension for Scoping-Reviews (PRISMA-ScR), PUBMED, Medline, and google-scholar were used to analyze pandemic impacts on VBHC domains. MCDA prioritized economic policies, utilizing the value-measurement model with additive-weighted scores. The criteria weighting-method used direct-rating, ranking and robust deliberation by four experts to priority rank economic policies for each VBHC domain. Results COVID-19 impacted on all VBHC domains: Integrated Practice Units, outcome and cost measurement, Value-Based Reimbursement, Information Technology (IT), regional systems integration, geography of care and VBHC policy and institutions. There were changes in patient complexity, service delivery, activity, models of care, pricing, costs, IT, quality, and health status. COVID-19 pricing impacts included increased capacity costs, Personal Protective Equipment costs, cost per patient, service delivery changes, and telehealth costs. A median of 37% of patients globally avoided care, negatively impacting on diabetes, venous thromboembolism, Parkinson’s disease, kidney disease, myocardial infarction, stroke, and cancer patients. A consequence is inaccurate assessments of health status, underestimated projected health needs, and costs for risk-adjustment, impacting health services. Innovations include telehealth and changing service redesign. New care models include state-wide ICU-COVID Models of Care, Telehealth-Inpatient Physician Services, COVID-linked pathology testing, and increased Hospital in the Home. Artificial Intelligence innovations involved patient-screening, triage, risk-evaluation, care assessment, pathways, and laboratories. Risk-adjustment challenges impacted on Value-Based Reimbursement and patient risk-stratification. MCDA prioritized economic policies, including healthcare system performance, healthcare demand and supply, risk-adjustment, planning and market mechanisms, economic evaluation of healthcare, economic sustainability initiatives, and health determinants. Conclusion COVID-19 demonstrated the limits of health systems globally, with increasing costs and limited budgets. The economic strategies identified can enhance VBHC globally.

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Digital innovation for healthcare in COVID-19 pandemic

Nazari

2022

Digital Innovation for Healthcare in COVID-19 Pandemic: Strategies and Solutions

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Would the Use of Artificial Intelligence in COVID-19 Patient Management Add Value to the Healthcare System?

Cited by 3 publications

References 36 publications

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Recent Advances in Machine Learning Applied to Ultrasound Imaging

A multi-criteria decision analysis framework that prioritizes economic policy to enhance value based health care during the COVID-19 pandemic

Digital innovation for healthcare in COVID-19 pandemic

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