ArticleHuman brain mapping2020
Your evidence? Machine learning algorithms for medical diagnosis and prediction.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The ethical requirement of explainability for AI-DSS in healthcare: a systematic review of reasons.BMC medical ethics · 2024Pooled it
- Multi-dimensional attention framework for personalised Alzheimer's disease progression prediction across sporadic and genetic risk cohorts.Research square · 2026Article
- Ethical and Regulatory Perception of Artificial Intelligence Among Dental Students: A Cross-Sectional Study.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026Article
- The Deep Learning Revolution in Neuroimaging: Insights from a Bibliometric Analysis (2014-2024).Neuroinformatics · 2026Review
- Development and validation of a machine learning model for online predicting the risk of in heart failure: based on the routine blood test and their derived parameters.Frontiers in cardiovascular medicine · 2025Article
- Physician awareness of, interest in, and current use of artificial intelligence large language model-based virtual assistants.PloS one · 2025Article
- Exploring home healthcare clinicians' needs for using clinical decision support systems for early risk warning.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Impact of gist intervention on automated system interpretability and user decision making.Cognitive research: principles and implications · 2024Article
- Machine Learning Model in Obesity to Predict Weight Loss One Year after Bariatric Surgery: A Pilot Study.Biomedicines · 2024Article
- Prediction of the risk of mortality in older patients with coronavirus disease 2019 using blood markers and machine learning.Frontiers in immunology · 2024Article
- Trust and stakeholder perspectives on the implementation of AI tools in clinical radiology.European radiology · 2024Article
- Sex-related patterns in the electroencephalogram and their relevance in machine learning classifiers.Human brain mapping · 2023Article
- Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.NeuroImage · 2023Article
- A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions.Diagnostics (Basel, Switzerland) · 2023Review
- The Deception of Certainty: how Non-Interpretable Machine Learning Outcomes Challenge the Epistemic Authority of Physicians. A deliberative-relational Approach.Medicine, health care, and philosophy · 2022Article
- Feeding the machine: Challenges to reproducible predictive modeling in resting-state connectomics.Network neuroscience (Cambridge, Mass.) · 2022Article
- Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices.Frontiers in public health · 2022Article
- Explanatory pragmatism: a context-sensitive framework for explainable medical AI.Ethics and information technology · 2022Article
- Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans.Communications biology · 2021Article
- [The predictable human : Possibilities and risks of AI-based prediction of cognitive abilities, personality traits and mental illnesses].Der Nervenarzt · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer systems for medical diagnosis based on machine learning are not mere science fiction. Despite undisputed potential benefits, such systems may also raise problems. Two (interconnected) issues are particularly significant from an ethical point of view: The first issue is that epistemic opacity is at odds with a common desire for understanding and potentially undermines information rights. The second (related) issue concerns the assignment of responsibility in cases of failure. The core of the two issues seems to be that understanding and responsibility are concepts that are intrinsically tied to the discursive practice of giving and asking for reasons. The challenge is to find ways to make the outcomes of machine learning algorithms compatible with our discursive practice. This comes down to the claim that we should try to integrate discursive elements into machine learning algorithms. Under the title of "explainable AI" initiatives heading in this direction are already under way. Extensive research in this field is needed for finding adequate solutions.
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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.