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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 934 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.

934 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 · 934

265
HealthNewly addedModerate evidence · 1 source

A Categorical Boosting model integrating routine serological markers, age, and liver stiffness measurement predicted biopsy-confirmed significant fibrosis in patients with CLD of uncertain etiology with AUC 0.827, outperforming FIB-4, GPR, Forns Index and LSM alone.

Researchers retrospectively studied 503 patients with chronic liver disease of uncertain etiology who had liver biopsies at Union Hospital of Tongji Medical College. They trained 12 machine-learning models on routine clinical variables and liver stiffness measurement to predict significant fibrosis, with Categorical Boosting showing the best performance at AUC 0.827.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
99

Updated Oct 9, 2026 · TRV-2026-1316

68Index score
266
BusinessNewly addedModerate evidence · 1 source

Generative AI allows businesses to create texts, images, audio, code and video with ease of use and intuitive interfaces, enabling development of new or improved business models.

By September 2023, after OpenAI introduced ChatGPT in November 2022, a peer-reviewed study examined generative AI from a business model innovation perspective. It conducted a qualitative scoping review of 513 sources and formulated six propositions about how GAI affects firms, illustrated with examples from software engineering, healthcare and financial services.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
99

Updated Oct 8, 2026 · TRV-2026-1313

68Index score
267
EducationNewly addedModerate evidence · 1 source

When used with guidance in collaborative human-AI interaction, ChatGPT helped university students complete all four cognitive presence phases and enhanced critical thinking, with self-reported improvements in triggering, exploration, and integration.

On September 11, 2025, a mixed-methods study reported on 40 students from a southwestern US university who used ChatGPT for learning tasks. Survey data showed mean improvements in triggering events, exploration, and integration, while resolution remained neutral, and script analysis identified passive AI-directed versus collaborative AI-supported interaction patterns.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
99

Updated Oct 8, 2026 · TRV-2026-1312

68Index score
268
HealthNewly addedModerate evidence · 1 source

Researchers developed a machine learning driven QSAR model using four molecular descriptors to predict energy barriers for N-hydroxylation of aniline disinfection byproducts, achieving R2 around 0.8.

Published October 3 2026, researchers investigated how P450 enzymes metabolize aniline disinfection byproducts formed during water treatment. Using high-throughput DFT, QM/MM for 2,6-dichloroaniline across four species, and a machine learning driven QSAR model with four Mordred descriptors, they found N-hydroxylation is feasible with OH rebound as the rate-determining step, built a predictive model with R2 approximately 0.8, and observed zebrafish P450 as most readily activated.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
99

Updated Oct 7, 2026 · TRV-2026-1306

68Index score

AI problems · 771

265
HealthStableModerate evidence · 1 source

Objective To evaluate the accuracy, text quality, and perceived potential harm of EHR-integrated and LLM-generated hospital courses in pediatric inpatient care during early clinical implementation.

Background Large language model (LLM)-generated hospital courses are increasingly integrated into electronic health records (EHRs), yet their accuracy and safety in pediatric populations remain poorly characterized. Objective To evaluate the accuracy, text quality, and perceived potential harm of EHR-integrated and LLM-generated hospital courses in pediatric inpatient care during early clinical implementation.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 4, 2026 · TRV-2026-0977

68Index score
266
HealthStableModerate evidence · 1 source

Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation: Furthermore, advancements in deep learning methodologies are required to improve the accuracy of prediction concerning target functionalities and ligand characteristics, even when complete receptor structures are not available.

Recent years have witnessed considerable progress in computer-aided drug discovery, driven by the incorporation of computational technologies within both academic and pharmaceutical environments. This evolution is marked by a significant accumulation of data pertaining to detailed three-dimensional structural information, ligand properties, and their interactions with therapeutic targets.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 4, 2026 · TRV-2026-0976

68Index score
267
HealthStableModerate evidence · 1 source

Machine learning can enhance risk detection accuracy and support proactive management of complications.

Background Renal transplantation is the preferred treatment for end-stage chronic kidney disease but requires lifelong immunosuppression, increasing the risk of infections such as urinary tract infection (UTI). UTI in kidney transplant recipients can lead to serious complications, including acute kidney injury, reduced graft survival, and increased mortality.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Sep 3, 2026 · TRV-2026-0974

68Index score
268
HealthStableModerate evidence · 1 source

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.

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. Here we present SlideChat, a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Sep 3, 2026 · TRV-2026-0973

68Index score

Recomputed live from the record · Oct 12, 2026, 10:18 AM