Controlling Safety of Artificial Intelligence-Based Systems in Healthcare

Davahli, Mohammad Reza; Karwowski, Waldemar; Fiok, Krzysztof; Wan, Thomas T. H.; Parsaei, Hamid R.

doi:10.3390/sym13010102

Cited by 21 publications

(8 citation statements)

References 78 publications

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“…Although the literature indicates that artificial intelligence-based systems have achieved significant success in healthcare since 2016 [59], the prediction associated with blood donation has not been an area with significant development. We believe that this study provides a significant advance.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Intention to Donate Blood among Blood Donors Using a Decision Tree Algorithm

Salazar-Concha

Ramírez-Correa

2021

Symmetry

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The blood donation process is essential for health systems. Therefore, the ability to predict donor flow has become relevant for hospitals. Although it is possible to predict this behaviour intention from donor questionnaires, the need to reduce social contact in pandemic settings leads to decreasing the extension of these surveys with the minimum loss of predictivity. In this context, this study aims to predict the intention to give blood again, among donors, based on a limited number of attributes. This research uses data science and learning concepts based on symmetry in a particular classification to predict blood donation intent. We carried out a face-to-face survey of Chilean donors based on the Theory of Planned Behaviour. These data, including control variables, were analysed using the decision tree technique. The results indicate that it is possible to predict the intention to donate blood again with an accuracy of 84.17% and minimal variables. The added scientific value of this article is to propose a more simplified way of measuring a multi-determined social phenomenon, such as the intention to donate blood again and the application of the decision tree technique to achieve this simplification, thereby contributing to the field of data science.

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Section: Discussionmentioning

confidence: 99%

Predicting the Intention to Donate Blood among Blood Donors Using a Decision Tree Algorithm

Salazar-Concha

Ramírez-Correa

2021

Symmetry

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“…Screen and monitoring: Continuous monitoring and validation are needed even after any SML method is clinically deployed. It will reduce the risks and adverse events of post-market shadowiness [ 121 ].…”

Section: Challenges From the Sml Implementation Sidementioning

confidence: 99%

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

Roy¹,

Meena²,

Lim

2022

Diagnostics

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The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.

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“…Data availability is one of the main concerns of AI developers (Davahli et al, 2021). Despite significant efforts in collecting and releasing datasets, most data might have different deficiencies, such as, missing data in datasets, lack of coverage of rare and novel cases, high-dimensionality with small sample sizes, lack of appropriately labeled data, and data contamination with artifacts (Davahli et al, 2021). Furthermore, most data are generally collected for operations but not specifically for AI research and training.…”

Section: Xr Serving Aimentioning

confidence: 99%

The Combination of Artificial Intelligence and Extended Reality: A Systematic Review

Reiners

Davahli

Karwowski

et al. 2021

Front. Virtual Real.

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Artificial intelligence (AI) and extended reality (XR) differ in their origin and primary objectives. However, their combination is emerging as a powerful tool for addressing prominent AI and XR challenges and opportunities for cross-development. To investigate the AI-XR combination, we mapped and analyzed published articles through a multi-stage screening strategy. We identified the main applications of the AI-XR combination, including autonomous cars, robotics, military, medical training, cancer diagnosis, entertainment, and gaming applications, advanced visualization methods, smart homes, affective computing, and driver education and training. In addition, we found that the primary motivation for developing the AI-XR applications include 1) training AI, 2) conferring intelligence on XR, and 3) interpreting XR- generated data. Finally, our results highlight the advancements and future perspectives of the AI-XR combination.

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Controlling Safety of Artificial Intelligence-Based Systems in Healthcare

Cited by 21 publications

References 78 publications

Predicting the Intention to Donate Blood among Blood Donors Using a Decision Tree Algorithm

Predicting the Intention to Donate Blood among Blood Donors Using a Decision Tree Algorithm

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

The Combination of Artificial Intelligence and Extended Reality: A Systematic Review

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