The assessment list for trustworthy artificial intelligence: A review and recommendations

Radclyffe, Charles; Ribeiro, Mafalda; Wortham, Robert H.

doi:10.3389/frai.2023.1020592

Cited by 18 publications

(12 citation statements)

References 21 publications

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“…For example, in Germany, a patient must give informed consent to the use of AI in the process of his diagnosis and treatment, which we believe is a good practice. Also, rules that should be fulfilled by the AI-based system, like the Assessment List for Trustworthy Artificial Intelligence (ALTAI) [207][208][209][210], have been formulated. In [211,212], 10 ethical risk points (ERPs) important to institutions, policymakers, teachers, students, and patients, including potential impacts on design, content, delivery, and AI-human communication in the field of AI and metaverse-based medical education, were defined.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Rudnicka,

Szczepanski,

Pregowska

2024

Electronics

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Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies, as well as increasing the effectiveness of the training process. In this context, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in medical image scan segmentation and intelligent visual content generation, i.e., computer-generated three-dimensional (3D) images in the context of extended reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. It concludes with possible developments and open challenges in AI applications in extended reality-based solutions. Finally, future lines of research and development directions of artificial intelligence applications, both in medical image segmentation and extended reality-based medical solutions, are discussed.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Rudnicka,

Szczepanski,

Pregowska

2024

Electronics

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“…In addition to the challenges posed by the “black box” issue leading to non-interpretable problems, biases and fairness, technical safety, preservation of human autonomy, privacy, and data security are significant AI ethics concerns within this field [ 20 , 180 ]. The development of trustworthy AI in healthcare has become a crucial responsibility worldwide [ 181 ]. For instance, the European Commission has enacted both the “Ethics Guidelines for Trustworthy AI” and the “Artificial Intelligence Act” [ 182 , 183 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review

Cai,

Tang

et al. 2024

BMC Med

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Background A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation. Methods PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789). Results In 20,887 screened references, 79 articles (82.5% in 2017–2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5–52,000, median 21) and large-span sample size (range 80–3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as “recommended”; however, 281 and 187 were “not recommended” and “warning,” respectively. Conclusion AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.

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“…For example, in Germany, a patient must give informed consent to the use of AI in the process of his diagnosis and treatment, which we believe is a good practice. Also, rules that should be fulfilled by the AI-based system like the Assessment List for Trustworthy Artificial Intelligence (ALTAI) [173][174][175], were formulated. In [163,176] 10 ethical risk points (ERP) important to institutions, policymakers, teachers, students, and patients, including potential impacts on design, content, delivery, and AI-human-communication in the field of AI and Metaversebased medical education were defined.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation

Rudnicka,

Szczepanski,

Pregowska

2023

Preprint

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Recently, Artificial Intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies as well as increasing the effectiveness of the training process. Thus, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in the medical image scan segmentation, and intelligent visual-content generation, i.e. computer-generated three-dimensional (3D) images in the context of Extended Reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper also attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. Finally, this paper concludes with possible developments and open challenges in AI application in Extended Reality-based solutions. Finally, the future lines of research and development directions of Artificial Intelligence applications both in medical image segmentation and Extended reality-based medical solutions are discussed.

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The assessment list for trustworthy artificial intelligence: A review and recommendations

Cited by 18 publications

References 21 publications

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation

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