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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

333
HealthStableModerate evidence · 1 source

AI-based digital pathology models have been qualified by regulators for endpoint determination in MASH trials, with additional AI risk models and large language models applied to patient enrollment, recruitment and retention.

Published September 16, 2026, this narrative review examines how AI is being applied to metabolic dysfunction-associated steatohepatitis trials, a field where only resmetirom and semaglutide had received conditional approval at the time of writing.

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

Updated Sep 18, 2026 · TRV-2026-1134

68Index score
334
ScienceStableModerate evidence · 1 source

Genolator integrates DNA, amino acid and protein structure embeddings with natural language queries and answers questions about protein localization and function with high accuracy, outperforming general LLMs.

Researchers present Genolator, a multimodal large language model that combines DNA sequence, amino acid sequence, and protein structure embeddings with natural language interaction. Fine-tuned on over 365,000 question-answer pairs generated from abstracted Gene-Ontology terms, the model is evaluated on answering queries about protein subcellular localization, molecular function, and biological processes.

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

Updated Sep 18, 2026 · TRV-2026-1133

68Index score
335
HealthStableModerate evidence · 1 source

A random survival forest clinicogenomic model integrating a 21-gene recurrence score, 14-gene immunoglobulin signature, and clinicopathologic features improved prognostic stratification in ER-positive/HER2-negative early breast cancer, identifying larger low-risk groups and improving discrimination in external datasets

Researchers developed and externally validated a machine-learning-based clinicogenomic model for ER-positive/HER2-negative early breast cancer using four independent datasets totaling 5,132 patients. The random survival forest integrated a research-grade 21-gene recurrence score, a 14-gene immunoglobulin signature, and clinicopathologic features, and was tested against the standalone 21-gene score.

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

Updated Sep 18, 2026 · TRV-2026-1132

68Index score
336
HealthStableModerate evidence · 1 source

Advanced LLMs like ChatGPT-4.5 can provide accurate, comprehensive periodontal information to patients seeking quick answers to common gum disease questions.

On 2026-09-16, a peer-reviewed comparative study reported testing 10 large language models on 15 common periodontal questions in English and Persian, with blinded ratings by two board-certified periodontists across six criteria. Performance varied significantly by model architecture, language, and question type, with the most advanced configuration providing highly accurate and well-referenced answers.

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

Updated Sep 18, 2026 · TRV-2026-1131

68Index score

AI problems · 771

333
HealthStableModerate evidence · 1 source

When used zero-shot to identify lumbar disc herniations on sagittal MRI, Gemini 3.1 Pro produced low specificity and a substantial false-positive burden, with T1+T2 input performing worse than T1-only.

Researchers tested Gemini 3.1 Pro in a zero-shot setting to detect lumbar disc herniations on sagittal MRI from 119 SPIDER cases (26% prevalence). Using only the mid-sagittal slice and a forced binary prompt, T1-only achieved 70% accuracy with 58% sensitivity and 74% specificity, while paired T1+T2 achieved 58% accuracy with 77% sensitivity and 51% specificity.

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

Updated Aug 14, 2026 · TRV-2026-0758

67Index score
334
HealthStableModerate evidence · 1 source

Current AI toxicity models are limited by black-box opacity that lowers transparency and regulatory confidence, with most explainable AI applications still stuck at computational or preclinical stage.

On August 12, 2026, a review in the International Journal of Toxicology summarized AI and machine learning use in toxicological risk assessment to predict chemical toxicity and support regulatory decisions, noting that explainable AI methods like SHAP and LIME are being explored to make model decisions interpretable.

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

Updated Aug 14, 2026 · TRV-2026-0757

67Index score
335
HealthStableModerate evidence · 1 source

Standard initial pathology of RRSO specimens missed serous (pre)malignancies in BRCA1/2 carriers, leaving occult STIC or HGSC undetected in patients who subsequently developed peritoneal HGSC.

By August 2026, a Histopathology study re-examined fallopian tube tissue from 19 BRCA1/2 carriers who had undergone risk-reducing salpingo-oophorectomy around age 40. Using deeper sections cut at 150 μm intervals and a deep learning model to support STIC detection, the team found occult STIC or HGSC in all patients who later developed peritoneal HGSC despite having no STIC or HGSC at initial diagnosis.

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

Updated Aug 14, 2026 · TRV-2026-0755

67Index score
336
HealthStableModerate evidence · 1 source

When recommending musculoskeletal providers, LLMs at times provided inaccurate phone numbers and contact information, potentially preventing patients from reaching the appropriate clinic.

Researchers prompted ChatGPT, DeepSeek, and Gemini with standardized musculoskeletal complaints for Lynchburg, VA and Trumbull, CT, and judged whether recommended physicians were currently practicing locally in the relevant specialty and whether phone numbers were correct. By the August 13, 2026 publication date, ChatGPT was appropriate in all 17 recommendations, while Gemini and DeepSeek were appropriate in 43% and 40% of recommendations respectively.

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

Updated Aug 14, 2026 · TRV-2026-0752

67Index score

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