The Future of AI-Driven Technologies in Medical Laboratories

In this systematic review, we conclude that many AI models in health care result from volumetric exploration and almost exclusively focus on black box models whose underlying mechanisms or reasons for making specific predictions are not understood by the user. However, most of the current methods are not transparent and difficult to understand due to their inherent black-box nature. Our analysis also shows that medical decision relevant AI models do not yet consider the factors of explainability, interpretability, reproducibility, robustness, and trustworthiness of deployment in clinical routine. All the above-described properties are essential to ensure the quality and safety of clinical decisions based on AI.

Authors

Publication Details

Journal
International Journal For Multidisciplinary Research
Published
2024-12-13
DOI
https://doi.org/10.36948/ijfmr.2024.v06i06.33107
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

The Future of AI-Driven Technologies in Medical Laboratories

Moh A. Alkhamis, Moayad Sadek Alshawi -, Mohammed Hashim, Ali Ahmad Saleh Alsmail - et al.
International Journal For Multidisciplinary Research
Artificial Intelligence in Healthcare and Education
article

The Future of AI-Driven Technologies in Medical Laboratories

Moh A. Alkhamis, Moayad Sadek Alshawi -, Mohammed Hashim, Ali Ahmad Saleh Alsmail -, Hassan Ali Alhadab -
article en

Abstract

In this systematic review, we conclude that many AI models in health care result from volumetric exploration and almost exclusively focus on black box models whose underlying mechanisms or reasons for making specific predictions are not understood by the user. However, most of the current methods are not transparent and difficult to understand due to their inherent black-box nature. Our analysis also shows that medical decision relevant AI models do not yet consider the factors of explainability, interpretability, reproducibility, robustness, and trustworthiness of deployment in clinical routine. All the above-described properties are essential to ensure the quality and safety of clinical decisions based on AI.

International Journal For Multidisciplinary ResearchVol. 6(6)
Gender equality
Openalex Percentile: Top 82%
Artificial Intelligence in Healthcare and Education
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