ReviewDiagnostics (Basel, Switzerland)2022
Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson's Disease: A Narrative Review.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning in image forgery: A systematic review for risk of bias (RoB).Journal of forensic sciences · 2026Pooled it
- Brain Gray Matter Alterations in Hepatic Encephalopathy: A Voxel-Based Meta-Analysis of Whole-Brain Studies.Frontiers in human neuroscience · 2022Pooled it
- Enhancing prediction accuracy for Parkinson's disease using advanced machine learning models.Scientific reports · 2026Article
- Women's Cardiovascular Disease and Stroke Risk Stratification Using a Precision and Personalized Framework Embedded with an Explainable Artificial Intelligence Paradigm: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Trustworthy AI for medical decisions: Adversarially robust and fair machine learning prediction for Parkinson's disease.PloS one · 2026Article
- In vivo and in silico models of Drosophila for Parkinson's disease.The FEBS journal · 2025Review
- Quantifying device type and handedness biases in a remote Parkinson's disease AI-powered assessment.NPJ digital medicine · 2025Article
- Parkinson's Disease: Bridging Gaps, Building Biomarkers, and Reimagining Clinical Translation.Cells · 2025Review
- A novel fusion architecture for detecting Parkinson's Disease using semi-supervised speech embeddings.NPJ Parkinson's disease · 2025Article
- Linguistic changes in spontaneous speech for detecting Parkinson's disease using large language models.PLOS digital health · 2025Article
- aiGeneR 3.0: an enhanced deep network model for resistant strain identification and multi-drug resistance prediction inFrontiers in genetics · 2025Article
- Review
- An Artificial Intelligence-Based Non-Invasive Approach for Cardiovascular Disease Risk Stratification in Obstructive Sleep Apnea Patients: A Narrative Review.Reviews in cardiovascular medicine · 2024Review
- Late feature fusion using neural network with voting classifier for Parkinson's disease detection.BMC medical informatics and decision making · 2024Article
- Article
- Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.EClinicalMedicine · 2024Article
- UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review.Reviews in cardiovascular medicine · 2024Review
- Tuning attention based long-short term memory neural networks for Parkinson's disease detection using modified metaheuristics.Scientific reports · 2024Article
- Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics.PeerJ. Computer science · 2024Article
- Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look.Journal of cardiovascular development and disease · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Motivation: Diagnosis of Parkinson's disease (PD) is often based on medical attention and clinical signs. It is subjective and does not have a good prognosis. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. The purpose of this study is to compute the risk of bias (RoB) automatically.
methodThe PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using
resultThe ML and HM cutoffs were 3.50 and 2.33, respectively, which constituted 7, 13, and 6 for low-, moderate-, and high-bias studies. The best and worst architectures were "deep learning with sketches as outcomes" and "machine learning with Electroencephalography," respectively. We recommend (i) the usage of power analysis in big data framework, (ii) that it must undergo scientific validation using unseen AI models, and (iii) that it should be taken towards clinical evaluation for reliability and stability tests.
conclusionThe AI is a vital component for the diagnosis of early PD and the recommendations must be followed to lower the RoB.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.