Five Strategies for Bias Estimation in Artificial Intelligence-based Hybrid Deep Learning for Acute Respiratory Distress Syndrome COVID-19 Lung Infected Patients using AP(ai)Bias 2.0: A Systematic Review
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Coronavirus 2019 (COVID-19) has led to a global pandemic infecting 224 million people and has caused 4.6 million deaths. Nearly 80 Artificial Intelligence (AI) articles have been published on COVID-19 diagnosis. The first systematic review on the Deep Learning (DL)-based paradigm for COVID-19 diagnosis was recently published by Suri et al. [IEEE J Biomed Health Inform. 2021]. The above study used AtheroPoint’s “AP(ai)Bias 1.0” using 10 AI attributes in the DL framework. The proposed study uses “AP(ai)Bias 2.0” as part of the three quantitative paradigms for Risk-of-Bias quantification by using the best 40 dedicated Hybrid DL (HDL) studies and utilizing 39 AI attributes. In the first method, the radial-bias map (RBM) was computed for each AI study, followed by the computation of bias value. In the second method, the regional-bias area (RBA) was computed by the area difference between the best and the worst AI performing attributes. In the third method, ranking-bias score (RBS) was computed, where AI-based cumulative scores were computed for all the 40 studies. These studies were ranked, and the cutoff was determined, categorizing the HDL studies into three bins: low, moderate, and high. Using the Venn diagram, these three quantitative methods were benchmarked against the two qualitative non-randomized-based AI trial methods (ROBINS-I and PROBAST). Using the analytically derived moderate-high and low-moderate cutoff of 2.9 and 3.6, respectively, we observed 40%, 27.5%, 17.5%, 10%, and 20% of studies were low-biased for RBM, RBA, RBS, ROBINS-I, and PROBAST, respectively. We present an eight-point recommendation for AP(ai)Bias 2.0 minimization.
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Very few studies discuss the impact of bias and its computation.
“Very few studies discuss about the impact of bias and its computation [45,142].”
From Artificial intelligence bias in medical system designs: a systematic review (2023) · cites this paper as [142]
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Rana, S. A., Azizul, Z. H., & Awan, A. A. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1630 · Cited by 23 · see citing works
- Authors
- Suri, J. S., Agarwal, S., Jena, B., Saxena, S., El-Baz, A., Agarwal, V., Kalra, M. K., Saba, L., Viskovic, K., Fatemi, M., & Naidu, S.
- Affiliations
- Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA, USA.
- Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA, USA and Dept. of CSE, PSIT, Kanpur, UP, INDIA.
- International Institute of Information Technology (IN)
- University of Louisville (US)
- Sanjay Gandhi Post Graduate Institute of Medical Sciences (IN)
- Massachusetts General Hospital (US)
- University of Cagliari (IT)
- University Hospital for Infectious Diseases, Zagreb, CROATIA.
- Mayo Clinic (US)
- University of Minnesota, Duluth (US)
- ORCID
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2024
- Pages
- 1-1
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W4285176431
- Cited by
- 38
- References
- 81
- Citations by year
- 2025: 2 · 2024: 14 · 2023: 13 · 2022: 9
- FWCI
- 1.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- IEEE Transactions on Instrumentation and Measurement (publishedVersion)
- UNICA IRIS Institutional Research Information System (University of Cagliari) (submittedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- bias estimation, artificial intelligence, hybrid deep learning model, COVID-19 diagnosis, acute respiratory distress syndrome, risk of bias, ROBINS-I, PROBAST, bias mitigation
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