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Geny M, Andres E, Talha S, Geny B. Liability of Health Professionals Using Sensors, Telemedicine and Artificial Intelligence for Remote Healthcare. SENSORS (BASEL, SWITZERLAND) 2024; 24:3491. [PMID: 38894282 PMCID: PMC11174849 DOI: 10.3390/s24113491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 05/17/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024]
Abstract
In the last few decades, there has been an ongoing transformation of our healthcare system with larger use of sensors for remote care and artificial intelligence (AI) tools. In particular, sensors improved by new algorithms with learning capabilities have proven their value for better patient care. Sensors and AI systems are no longer only non-autonomous devices such as the ones used in radiology or surgical robots; there are novel tools with a certain degree of autonomy aiming to largely modulate the medical decision. Thus, there will be situations in which the doctor is the one making the decision and has the final say and other cases in which the doctor might only apply the decision presented by the autonomous device. As those are two hugely different situations, they should not be treated the same way, and different liability rules should apply. Despite a real interest in the promise of sensors and AI in medicine, doctors and patients are reluctant to use it. One important reason is a lack clear definition of liability. Nobody wants to be at fault, or even prosecuted, because they followed the advice from an AI system, notably when it has not been perfectly adapted to a specific patient. Fears are present even with simple sensors and AI use, such as during telemedicine visits based on very useful, clinically pertinent sensors; with the risk of missing an important parameter; and, of course, when AI appears "intelligent", potentially replacing the doctors' judgment. This paper aims to provide an overview of the liability of the health professional in the context of the use of sensors and AI tools in remote healthcare, analyzing four regimes: the contract-based approach, the approach based on breach of duty to inform, the fault-based approach, and the approach related to the good itself. We will also discuss future challenges and opportunities in the promising domain of sensors and AI use in medicine.
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Affiliation(s)
- Marie Geny
- Joint Research Unit-UMR 7354, Law, Religion, Business and Society, University of Strasbourg, 67000 Strasbourg, France;
| | - Emmanuel Andres
- Biomedicine Research Center of Strasbourg (CRBS), UR 3072, “Mitochondria, Oxidative Stress and Muscle Plasticity”, University of Strasbourg, 67000 Strasbourg, France; (E.A.); (S.T.)
- Faculty of Medicine, University of Strasbourg, 67000 Strasbourg, France
- Department of Internal Medicine, University Hospital of Strasbourg, 67091 Strasbourg, France
| | - Samy Talha
- Biomedicine Research Center of Strasbourg (CRBS), UR 3072, “Mitochondria, Oxidative Stress and Muscle Plasticity”, University of Strasbourg, 67000 Strasbourg, France; (E.A.); (S.T.)
- Faculty of Medicine, University of Strasbourg, 67000 Strasbourg, France
- Department of Physiology and Functional Explorations, University Hospital of Strasbourg, 67091 Strasbourg, France
| | - Bernard Geny
- Biomedicine Research Center of Strasbourg (CRBS), UR 3072, “Mitochondria, Oxidative Stress and Muscle Plasticity”, University of Strasbourg, 67000 Strasbourg, France; (E.A.); (S.T.)
- Faculty of Medicine, University of Strasbourg, 67000 Strasbourg, France
- Department of Physiology and Functional Explorations, University Hospital of Strasbourg, 67091 Strasbourg, France
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Wang W, Harrou F, Dairi A, Sun Y. Stacked deep learning approach for efficient SARS-CoV-2 detection in blood samples. Artif Intell Med 2024; 148:102767. [PMID: 38325923 DOI: 10.1016/j.artmed.2024.102767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 01/02/2024] [Accepted: 01/05/2024] [Indexed: 02/09/2024]
Abstract
Identifying COVID-19 through blood sample analysis is crucial in managing the disease and improving patient outcomes. Despite its advantages, the current test demands certified laboratories, expensive equipment, trained personnel, and 3-4 h for results, with a notable false-negative rate of 15%-20%. This study proposes a stacked deep-learning approach for detecting COVID-19 in blood samples to distinguish uninfected individuals from those infected with the virus. Three stacked deep learning architectures, namely the StackMean, StackMax, and StackRF algorithms, are introduced to improve the detection quality of single deep learning models. To counter the class imbalance phenomenon in the training data, the Synthetic Minority Oversampling Technique (SMOTE) algorithm is also implemented, resulting in increased specificity and sensitivity. The efficacy of the methods is assessed by utilizing blood samples obtained from hospitals in Brazil and Italy. Results revealed that the StackMax method greatly boosted the deep learning and traditional machine learning methods' capability to distinguish COVID-19-positive cases from normal cases, while SMOTE increased the specificity and sensitivity of the stacked models. Hypothesis testing is performed to determine if there is a significant statistical difference in the performance between the compared detection methods. Additionally, the significance of blood sample features in identifying COVID-19 is analyzed using the XGBoost (eXtreme Gradient Boosting) technique for feature importance identification. Overall, this methodology could potentially enhance the timely and precise identification of COVID-19 in blood samples.
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Affiliation(s)
- Wu Wang
- Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing 100872, China.
| | - Fouzi Harrou
- King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia.
| | - Abdelkader Dairi
- Computer Science Department, University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB), El Mnaouar, BP 1505, 31000, Bir El Djir, Algeria.
| | - Ying Sun
- King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia
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