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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

353
HealthStableModerate evidence · 1 source

Large language models have the potential to democratize medical knowledge and facilitate access to healthcare in clinical practice.

A Communications Medicine overview published October 10 2023 examines large language models as text-processing AI tools that gained wide attention after ChatGPT's November 2022 release, assessing their near-human ability to answer, summarize and translate and their emerging use in clinical practice, medical research and medical education.

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

Updated Sep 14, 2026 · TRV-2026-1089

68Index score
354
HealthStableModerate evidence · 1 source

GPT-4-generated, accessibility-optimised discharge letters increased comprehension of diagnosis, treatment, investigations and follow-up compared with conventional letters.

In two controlled quasi-experimental studies published 12 September 2026, 341 online-recruited adults and 791 medical and nursing students at the University of Turin were assigned to read either a GPT-4-generated accessibility-optimised discharge letter or a traditional discharge letter. Comprehension was measured with a structured score covering diagnosis, treatment, investigations and follow-up, with secondary measures of readability, clarity and satisfaction.

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

Updated Sep 14, 2026 · TRV-2026-1088

68Index score
355
HealthStableModerate evidence · 1 source

Standardizing pre-analytical H&E preparation before AI deployment can reduce computational burden and deployment costs while supporting robust AI performance in pathology.

A peer-reviewed analysis of French national external quality assessment data from 2019-2024 and a 2024 survey found frequent H&E preparation issues: up to 25.5% of slides had technical imperfections such as sectioning, thickness, and stretching problems, and 8.3% to 23.8% had suboptimal staining with poor nucleo-cytoplasmic contrast or intensity fluctuations.

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

Updated Sep 14, 2026 · TRV-2026-1087

68Index score
356
ClimateStableModerate evidence · 1 source

Machine learning-based risk zoning using integrated biological and abiotic indexes improved fit and classification, with CFBRI raising R2 to 0.62 and Random Forest models reaching cross-validation accuracies of 0.888 for agricultural land and 0.905 for construction land.

Researchers proposed a framework that integrates biological and non-biological indicators for soil heavy metal risk assessment. They developed a CRITIC-Fuzzy Biomarker Response Index to weight multi-timepoint biomarker data, then combined it with four abiotic pollution indices via PCA to create agricultural and construction land comprehensive indexes, which were used as labels to train five machine learning classifiers.

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

Updated Sep 14, 2026 · TRV-2026-1086

68Index score

AI problems · 771

353
EducationStableModerate evidence · 1 source

A quarter of students reported exam-time assistance, while consistent verification was low and awareness of institutional guidelines was limited.

A cross-sectional survey of 454 final-year dental students in the UAE, Jordan, Malaysia, Oman, and Brazil examined LLM use, motivations, and safeguards. Published August 6 2026, it found ChatGPT predominated at 95.9%, with 39.2% using LLMs several times per week and 28.6% daily for tasks like understanding complex concepts and summarising lecture notes.

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

Updated Aug 7, 2026 · TRV-2026-0675

67Index score
354
HealthStableModerate evidence · 1 source

The evidence base for ML prediction of ART outcomes is limited by substantial heterogeneity and frequent high or unclear risk of bias, requiring prospective multi-center external validation before clinical use.

A systematic review and diagnostic meta-analysis of 20 studies, 14 in quantitative synthesis, evaluated machine learning models to predict clinical pregnancy or live birth after assisted reproductive technology. As of the August 2026 publication, pooled sensitivity was 0.737 and specificity 0.789 with a DOR of 10.49 and acceptable discrimination on SROC, but heterogeneity was very high.

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

Updated Aug 7, 2026 · TRV-2026-0674

67Index score
355
HealthStableModerate evidence · 1 source

LLM reasoning did not establish clinical equivalence in this limited evaluation and requires specialist oversight and further validation before clinical use.

Researchers compared large language models and clinicians on 34 real-world glaucoma cases, with glaucoma specialists scoring responses on medical accuracy, key-point recall, and logical completeness. AI models produced structured reasoning with weighted mean scores overlapping attending ophthalmologists and exceeding some residents.

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

Updated Aug 7, 2026 · TRV-2026-0673

67Index score
356
HealthStableModerate evidence · 1 source

Integrating AI and LLMs into medical education raises ethical concerns across privacy and data security, algorithmic bias, accountability, fairness, reliability, dependency, and patient autonomy.

Published October 22, 2025, this PLOS One scoping review examined literature on AI and large language models like ChatGPT in medical education. It found potential for personalized learning alongside a set of ethical challenges, synthesizing 50 studies from three major databases covering 2010 to August 2024.

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

Updated Aug 6, 2026 · TRV-2026-0670

67Index score

Recomputed live from the record · Oct 12, 2026, 10:24 PM