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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 935 sourced gains and 771 sourced problems, averaging 68 and 66 on the index score. Readers have logged 22 public signals on the Pulse, which is kept apart and never counted as evidence.

935 gains
771 problems
Every sourced claim in the Index, one square each, shaded by the strength of its evidence. High Moderate Emerging

Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.

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AI gains · 935

329
ScienceStableModerate evidence · 1 source

We unfold the successive-interference cancellation (SIC) algorithm into a deep-learning network, where repeated stages of a single multitask network implement iterative signal estimation and interference cancellation that reflect the structural prior of SIC.

Impedance flow cytometry (IFC) is a label-free, single-cell measurement technique that captures biophysical properties beyond traditional biochemical markers. Code-multiplexing allows parallelization of IFC with simple hardware but requires advanced signal processing algorithms to resolve overlaps in signals originating from different channels.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
96

Updated Sep 22, 2026 · TRV-2026-1170

68Index score
330
HealthStableModerate evidence · 1 source

We further clarify that predictive accuracy, interpretability and causal explanation are related but distinct aims, and that machine learning should complement rather than replace psychological theory, causal reasoning and clinical judgement.

Machine learning is increasingly reshaping psychological and mental health research by complementing traditional theory-driven approaches with data-driven predictive modelling. This encompasses three key transitions: from hypothesis-testing to pattern discovery; from controlled experimental settings to naturalistic and multidimensional data ecosystems; and from explanatory theoretical models to prediction-informed theory-building and clinical translation.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
96

Updated Sep 22, 2026 · TRV-2026-1169

68Index score
331
HealthStableModerate evidence · 1 source

Smartphone AI-Enabled Lateral Flow Immunoassay Platform Using Advanced Quantum Dots for Intelligent Quantitative Diagnostics: Lateral flow immunoassays (LFIAs) are widely used in point-of-care diagnostics for their low cost and simplicity, but conventional formats rely on subjective visual readout and lack quantitative accuracy and sensitivity.

Lateral flow immunoassays (LFIAs) are widely used in point-of-care diagnostics for their low cost and simplicity, but conventional formats rely on subjective visual readout and lack quantitative accuracy and sensitivity. Here, we present a smartphone-guided, artificial-intelligence-enhanced LFIA platform that unites a rationally engineered dual-modal nanoprobe with on-device machine learning, demonstrated for quantitative detection of Rift Valley fever virus (RVFV).

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
96

Updated Sep 21, 2026 · TRV-2026-1156

68Index score
332
HealthStableModerate evidence · 1 source

Three ML approaches were evaluated: logistic regression (LR), Convolutional Neural Network (CNN) Attention-Multiple Instance Learning (MIL) (EfficientNet-V2-S), and a CNN-Extreme Gradient Boosting Hybrid (XGBoost).Patients/ParticipantsForty pediatric participants aged 2 to 17, including individuals with VPD, conditions associated with VPD, and healthy participants.Main Outcome Measure(s)Model performance was tested in binary hypernasality classification compared to SLP consensus at two CAPS-A-AM thresholds: absent (0) versus any hypernasality (1-4) and absent/borderline (0-1) versus mild-to-severe hypernasality (2-4).ResultsMultiple independent modeling approaches were able to detect clinically rated hypernasality.

ObjectivePerceptual evaluation by speech-language pathologists (SLPs) is essential for initial evaluation of velopharyngeal dysfunction (VPD). Machine learning (ML) offers a promising avenue for developing accessible speech assessment tools when SLP expertise is limited.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
95

Updated Sep 19, 2026 · TRV-2026-1140

68Index score

AI problems · 771

329
HealthStableModerate evidence · 1 source

Deep learning OCT models trained on a single dataset often degrade across scanners and sites due to device-dependent speckle variability, limiting reliability in real-world screening.

Researchers developed NA-DyCNN, a lightweight noise-aware dynamic convolutional network for OCT-based retinal disease classification that explicitly models post-acquisition speckle variability during training to improve cross-scanner robustness.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 15, 2026 · TRV-2026-0775

67Index score
330
HealthStableModerate evidence · 1 source

Most AI-ECG studies in pediatric and congenital heart disease remain retrospective and single-center with limited external validation, facing challenges from small datasets and age-dependent ECG variation.

This review from August 2026 summarizes how artificial intelligence applied to standard electrocardiograms has been tested in pediatric and congenital heart disease. It reports that deep learning models have been shown to identify arrhythmias, ventricular dysfunction, and CHD, and are being extended to predict future risk and to analyze wearable and telemetry data.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 15, 2026 · TRV-2026-0774

67Index score
331
PolicyStableModerate evidence · 1 source

AI-enabled medical devices create dynamic, data-dependent hazards that current ISO 14971, AAMI CR34971 and EU AI Act approaches address only in fragmented form, leaving gaps in hazard linkage, control adequacy, and lifecycle monitoring.

A peer-reviewed review published August 13, 2026 examined how AI-enabled medical devices challenge traditional safety-risk management. Drawing on 19 academic and regulatory sources, it found ISO 14971, AAMI CR34971 and the EU AI Act each cover parts of device safety and algorithmic governance but remain fragmented in practice.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 15, 2026 · TRV-2026-0771

67Index score
332
HealthStableModerate evidence · 1 source

Prompt-engineered simplification performed significantly worse for completeness and harmfulness in specific diagnoses and provided generic education disconnected from pathological findings.

A peer-reviewed survey study from January to April 2025 asked 52 US dermatology and dermatopathology professionals to rate AI-simplified versions of six fictitious dermatopathology reports. One version used Basic ChatGPT-4.0 with a simple prompt and the other used a custom DermDecoder GPT with a structured 489-word prompt, evaluated for factualness, completeness, and potential harm.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 15, 2026 · TRV-2026-0766

67Index score

Recomputed live from the record · Oct 12, 2026, 7:07 PM