Examinando por Autor "Salhi, Asma"
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Ítem A Systematic Review of Trustworthy and Explainable Artificial Intelligence in Healthcare: Assessment of Quality, Bias Risk, and Data Fusion(Elsevier, 2023-08-10) Albahri, Ahmed Shihab; Duhaim, Ali M.; Fadhel, Mohammed A.; Alnoor, Alhamzah; Baqer, Noor S.; Alzubaidi, Laith; Albahri, Osamah S.; Alamoodi, Abdullah Hussein; Bai, Jinshuai; Salhi, Asma; Santamaria, José; Ouyang, Chun; Gupta, Ashish; Gu, Yuantong; Deveci, MuhammetIn the last few years, the trend in health care of embracing artificial intelligence (AI) has dramatically changed the medical landscape. Medical centres have adopted AI applications to increase the accuracy of disease diagnosis and mitigate health risks. AI applications have changed rules and policies related to healthcare practice and work ethics. However, building trustworthy and explainable AI (XAI) in healthcare systems is still in its early stages. Specifically, the European Union has stated that AI must be human-centred and trustworthy, whereas in the healthcare sector, low methodological quality and high bias risk have become major concerns. This study endeavours to offer a systematic review of the trustworthiness and explainability of AI applications in healthcare, incorporating the assessment of quality, bias risk, and data fusion to supplement previous studies and provide more accurate and definitive findings. Likewise, 64 recent contributions on the trustworthiness of AI in healthcare from multiple databases (i.e., ScienceDirect, Scopus, Web of Science, and IEEE Xplore) were identified using a rigorous literature search method and selection criteria. The considered papers were categorised into a coherent and systematic classification including seven categories: explainable robotics, prediction, decision support, blockchain, transparency, digital health, and review. In this paper, we have presented a systematic and comprehensive analysis of earlier studies and opened the door to potential future studies by discussing in depth the challenges, motivations, and recommendations. In this study a systematic science mapping analysis in order to reorganise and summarise the results of earlier studies to address the issues of trustworthiness and objectivity was also performed. Moreover, this work has provided decisive evidence for the trustworthiness of AI in health care by presenting eight current state-of-the-art critical analyses regarding those more relevant research gaps. In addition, to the best of our knowledge, this study is the first to investigate the feasibility of utilising trustworthy and XAI applications in healthcare, by incorporating data fusion techniques and connecting various important pieces of information from available healthcare datasets and AI algorithms.Ítem Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements(Wiley, Hindawi, 2023-10-26) Alzubaidi, Laith; Al-Sabaawi, Aiman; Bai, Jinshuai; Dukhan, Ammar; Alkenani, Ahmed H.; Al-Asadi, Ahmed; Alwzwazy, Haider A.; Manoufali, Mohammed; Fadhel, Mohammed A.; Albahri, Ahmed Shihab; Moreira, Catarina; Ouyang, Chun; Zhang, Jinglan; Santamaria, José; Salhi, Asma; Hollman, Freek; Gupta, Ashish; Duan, Ye; Rabczuk, Timon; Abbosh, Amin; Gu, YuantongGiven the tremendous potential and infuence of artifcial intelligence (AI) and algorithmic decision-making (DM), these systems have found wide-ranging applications across diverse felds, including education, business, healthcare industries, government, and justice sectors. While AI and DM ofer signifcant benefts, they also carry the risk of unfavourable outcomes for users and society. As a result, ensuring the safety, reliability, and trustworthiness of these systems becomes crucial. Tis article aims to provide a comprehensive review of the synergy between AI and DM, focussing on the importance of trustworthiness. Te review addresses the following four key questions, guiding readers towards a deeper understanding of this topic: (i) why do we need trustworthy AI? (ii) what are the requirements for trustworthy AI? In line with this second question, the key requirements that establish the trustworthiness of these systems have been explained, including explainability, accountability, robustness, fairness, acceptance of AI, privacy, accuracy, reproducibility, and human agency, and oversight. (iii) how can we have trustworthy data? and (iv) what are the priorities in terms of trustworthy requirements for challenging applications? Regarding this last question, six diferent applications have been discussed, including trustworthy AI in education, environmental science, 5G-based IoTnetworks, robotics for architecture, engineering and construction, fnancial technology, and healthcare. Te review emphasises the need to address trustworthiness in AI systems before their deployment in order to achieve the AI goal for good. An example is provided that demonstrates how trustworthy AI can be employed to eliminate bias in human resources management systems. Te insights and recommendations presented in this paper will serve as a valuable guide for AI researchers seeking to achieve trustworthiness in their applications.