A Fair and Ethical Healthcare Artificial Intelligence System for Monitoring Driver Behavior and Preventing Road Accidents

Oueida, Soraia; Hossain, Soaad; Kotb, Yehia; Ahmed, Syed Ishtiaque

doi:10.1007/978-3-030-89880-9_33

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Additionally, scrutiny of AI algorithms is escalating due to the mistrust of AI, leading to the high demand for auditing frameworks [ 141 ]. Attempts made help minimize road accidents by applying AI fairness to the health sector [ 145 ]. Moreover, documented guidelines on integrating AI ethics in wildlife conservation AI systems help researchers consider AI ethics in wildlife conservation [ 146 ].…”

Section: Rqn5: What Are the Limitations Of The Reviewed Studies In Mi...mentioning

confidence: 99%

Intelligent Systems Using Sensors and/or Machine Learning to Mitigate Wildlife–Vehicle Collisions: A Review, Challenges, and New Perspectives

Nandutu

Atemkeng

Okouma

2022

Sensors

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Worldwide, the persistent trend of human and animal life losses, as well as damage to properties due to wildlife–vehicle collisions (WVCs) remains a significant source of concerns for a broad range of stakeholders. To mitigate their occurrences and impact, many approaches are being adopted, with varying successes. Because of their increased versatility and increasing efficiency, Artificial Intelligence-based methods have been experiencing a significant level of adoption. The present work extensively reviews the literature on intelligent systems incorporating sensor technologies and/or machine learning methods to mitigate WVCs. Included in our review is an investigation of key factors contributing to human–wildlife conflicts, as well as a discussion of dominant state-of-the-art datasets used in the mitigation of WVCs. Our study combines a systematic review with bibliometric analysis. We find that most animal detection systems (excluding autonomous vehicles) are relying neither on state-of-the-art datasets nor on recent breakthrough machine learning approaches. We, therefore, argue that the use of the latest datasets and machine learning techniques will minimize false detection and improve model performance. In addition, the present work covers a comprehensive list of associated challenges ranging from failure to detect hotspot areas to limitations in training datasets. Future research directions identified include the design and development of algorithms for real-time animal detection systems. The latter provides a rationale for the applicability of our proposed solutions, for which we designed a continuous product development lifecycle to determine their feasibility.

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Section: Rqn5: What Are the Limitations Of The Reviewed Studies In Mi...mentioning

confidence: 99%

Intelligent Systems Using Sensors and/or Machine Learning to Mitigate Wildlife–Vehicle Collisions: A Review, Challenges, and New Perspectives

Nandutu

Atemkeng

Okouma

2022

Sensors

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“…Proactive accident prevention will evaluate the risk of road accidents in real-time using AI and is a key aspect of road safety (Tonni et al, 2021). AI enables road accidents to be predicted and prevented (Olayode et al, 2020;Oueida et al, 2022). Investigation of the stability and efficiency of AI techniques for reducing road accidents (Olugbade et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Road Traffic Accident Prediction

Siswanto,

Syaban,

Hariani

2023

JJi

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The rapid development of AI shows its power and great development potential in practical engineering applications. Critical issues and potential solutions can reduce road traffic accidents and application of AI in road accident prediction. The published use of AI for road accident prediction is reviewed, presented, and represented as the main objective. The methods are collecting article data, quotations, presentation, and representation. The article data collection was obtained from 671 conference and journal articles in 2019-2023, but the suitability of articles that can be used is 69. Quotation produces a grouping of approaches, models, predictions, and benefits. The presentation showed that most approaches used were machine learning, the most used model was random forest, the prediction was mostly about severity, and the most benefit was about number reduction. Representation produces road accidents and related factors into factors in road accident predictions using artificial intelligence, so strategies and anticipation can be made to overcome them to improve road safety. AI in road accident prediction plays an important role in building predictive models with the hope that road accidents can be identified early, risk factors can be reduced, and effective preventive measures can be taken to improve road safety.

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