ReviewFrontiers in digital health2022
Erroneous data: The Achilles' heel of AI and personalized medicine.
Review in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review.Journal of medical Internet research · 2024Pooled it
- The Ethics of Leveraging Routinely Collected Patient Data for AI Development: Mixed Methods Study.Journal of medical Internet research · 2026Article
- Unveiling the power of artificial intelligence for image-based diagnosis and treatment in endodontics: An ally or adversary?International endodontic journal · 2025Review
- Article
- Prospective and External Validation of Machine Learning Models for Short- and Long-Term Mortality in Acutely Admitted Patients Using Blood Tests.Journal of clinical medicine · 2024Article
- Review
- Use of artificial intelligence in obstetric and gynaecological diagnostics: a protocol for a systematic review and meta-analysis.BMJ open · 2024Article
- Development and validation of prognostic machine learning models for short- and long-term mortality among acutely admitted patients based on blood tests.Scientific reports · 2024Article
- Artificial Intelligence in Heart Failure: Friend or Foe?Life (Basel, Switzerland) · 2024Review
- Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities.Journal of perinatology : official journal of the California Perinatal Association · 2024Review
- Evaluating the Efficacy of ChatGPT in Navigating the Spanish Medical Residency Entrance Examination (MIR): Promising Horizons for AI in Clinical Medicine.Clinics and practice · 2023Article
- The robot butler: How and why should we study predictive algorithms and artificial intelligence (AI) in healthcare?Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper reviews dilemmas and implications of erroneous data for clinical implementation of AI. It is well-known that if erroneous and biased data are used to train AI, there is a risk of systematic error. However, even perfectly trained AI applications can produce faulty outputs if fed with erroneous inputs. To counter such problems, we suggest 3 steps: (1) AI should focus on data of the highest quality, in essence paraclinical data and digital images, (2) patients should be granted simple access to the input data that feed the AI, and granted a right to request changes to erroneous data, and (3) automated high-throughput methods for error-correction should be implemented in domains with faulty data when possible. Also, we conclude that erroneous data is a reality even for highly reputable Danish data sources, and thus, legal framework for the correction of errors is universally needed.
Indexed as
Identifiers
What OpenQuestion holds
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.