+ HEALTH Background Discharge letters are essential for continuity of care, yet patients often misunderstand their con…± HEALTH Hematoxylin and Eosin (H&E) glass slide preparation is fundamental to histopathology. Pathologists' expertise…+ CLIMATE Traditional soil heavy metal risk assessments suffer from fragmented indices and poor logical integration. Co…± HEALTH Background Neonatal pneumothorax can progress rapidly, yet detection on supine chest radiographs remains chal…+ HEALTH T cell exhaustion has widespread implications for the progression and treatment of chronic diseases including…+ HEALTH Background Standard pulmonary arterial hypertension (PAH) risk models may not fully apply to PAH associated w…+ HEALTH Objectives To map and synthesize the scientific evidence regarding the use of Augmented Reality (AR) and Mixe…± HEALTH Objective To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovas…
TruaceTracing the truth around AIMonday, September 14, 2026
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The Problems surrounding AI

Documented harms, ranked by source quality, corroboration, and recency. Reader feedback is shown separately and never changes the evidence rank. 252 records · page 2 of 9.

252 results
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32
Reader signal

How should this claim be treated?

Evidence-backed problemPeer-reviewedHealth

Multimodal artificial intelligence, albeit showing great potential in computational pathology, remains limited to isolated patch-level interpretation and often fails to analyze gigapixel-scale whole-slide images (WSIs) essential for clinical utility.

Source article: SlideChat is a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types

Nature Cancer
34
Reader signal

How should this claim be treated?

Evidence-backed problemPeer-reviewedHealth

In functional imaging, AI can facilitate automated quantification of myocardial blood flow and ischemic myocardial volume, showing excellent agreement with manual measurements and strong performance for ischemia detection and risk stratification.

Source article: Clinical Applications of Artificial Intelligence in Cardiac CT: From Coronary CT Angiography to CT-Derived Fractional Flow Reserve and Myocardial Perfusion Imaging

Korean Journal of Radiology
35
Reader signal

How should this claim be treated?

Evidence-backed problemPeer-reviewedPolicy

The study contributes to explainable blockchain security, multimodal AI governance, and intelligent media systems by enabling more robust detection of copyright misuse, unauthorized licensing, abnormal content distribution, and cross-chain transaction risks.

Source article: Multimodal Graph Neural Networks and Evolutionary Knowledge Fusion for Secure and Explainable Governance in Intelligent IoT Interactive Media Systems

Big Data
36
Reader signal

How should this claim be treated?

37
Reader signal

How should this claim be treated?

38
Reader signal

How should this claim be treated?

Evidence-backed problemPeer-reviewedLifestyle

Portable Raman spectroscopy coupled with machine learning for rapid identification and source apportionment of plastic particles in aquaculture wastewater: MPs readily adsorb heavy metals and organic pollutants to form composite contamination and accumulate through food chains, imposing ecological and human health risks.

Source article: Portable Raman spectroscopy coupled with machine learning for rapid identification and source apportionment of plastic particles in aquaculture wastewater

Analytical Methods
41
Reader signal

How should this claim be treated?

Evidence-backed problemPeer-reviewedMedia & Arts

Users of AI art platforms showed limited awareness of structural issues, as cultural bias in training data and algorithmic transparency were rated lower in importance than autonomy and usability.

Source article: Generative AI Art and Creative Subjectivity: A Mixed-Methods Study Based on Grounded Theory and CRITIC

Asia-pacific Journal of Convergent Research Interchange
42
Reader signal

How should this claim be treated?

44
Reader signal

How should this claim be treated?

Evidence-backed problemPeer-reviewedHealth

Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications.

Source article: A large language models-assisted and expert-corrected workflow for preoperative anesthesia assessment drafts: A single-centre exploratory feasibility study

Medicina Clínica
45
Reader signal

How should this claim be treated?

Evidence-backed problemPeer-reviewedHealth

Systematic assessment of the medical utility of radiology and diagnostic Artificial Intelligence in fracture detection (SAMURAI-fracture): a protocol for a multicentre cluster-randomised controlled trial: Fracture misdiagnosis is a common diagnostic error in emergency departments (EDs) and minor injury units (MIUs), leading to poor patient outcomes, unnecessary treatments and significant healthcare costs.

Source article: Systematic assessment of the medical utility of radiology and diagnostic Artificial Intelligence in fracture detection (SAMURAI-fracture): a protocol for a multicentre cluster-randomised controlled trial

BMJ Open
49
Reader signal

How should this claim be treated?

Evidence-backed problemPeer-reviewedScience

The optimized ANN and Logistic Regression frameworks exhibited the highest overall discriminative power (AUC > 0.99), while the Random Forest algorithm achieved the peak classification accuracy (97.10%).

Source article: Evaluating machine learning and neural network architectures for forensic sex estimation using mandibular ramus and notch features on panoramic radiographs

International Journal of Legal Medicine
50
Reader signal

How should this claim be treated?

Evidence-backed problemPeer-reviewedHealth

Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust.

Source article: Explainable artificial intelligence in medical imaging: how to interpret, evaluate, and use artificial intelligence explanations

Diagnostic and Interventional Radiology
51
Reader signal

How should this claim be treated?

Evidence-backed problemPeer-reviewedHealth

EXPRESS: Relation between Albumin-Corrected Anion Gap and In-Hospital Mortality in Patients with Traumatic Lung Injury: A Multicenter Retrospective Cohort Study and the Development of Machine Learning-Based Prediction Models: Elevated ACAG was substantially linked to a high risk of mortality in individuals with TLI (hazard ratio (HR) [95% confidence interval (CI)] = 1.115 [1.037-1.199]).

Source article: EXPRESS: Relation between Albumin-Corrected Anion Gap and In-Hospital Mortality in Patients with Traumatic Lung Injury: A Multicenter Retrospective Cohort Study and the Development of Machine Learning-Based Prediction Models

Journal of Investigative Medicine
56
Reader signal

How should this claim be treated?

Evidence-backed problemPeer-reviewedEducation

AI has been rapidly integrated into educational settings without a profound and critical evaluation of its assumptions and consequences for knowledge and power in leadership.

Source article: AI Challenges and the Future of Education: A Needed Epistemic, Political, and Ecological Agenda for Critical Leadership Scholars and Practitioners

New Directions for Student Leadership
57
Reader signal

How should this claim be treated?

Evidence-backed problemPeer-reviewedHealth

AI facial image scoring, editing and curation systems converge on a narrow westernized phenotype and are linked to appearance dissatisfaction, perception drift, and Snapchat and Zoom dysmorphia presentations in plastic surgery patients.

Source article: Artificial Intelligence in Plastic Surgery of the Face: Implications for Esthetic Standards, Patient Perception, and Clinical Practice

Journal of Craniofacial Surgery