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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 929 sourced gains and 770 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.

929 gains
770 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 · 929

17
ScienceNewly addedModerate evidence · 1 source

An ensemble machine learning pipeline detected flowers on herbarium specimens with relatively strong accuracy and labeled 11.1 million of 22 million records, expanding taxonomic and temporal coverage when integrated into Phenobase.

By October 2026, researchers described a machine learning pipeline using an ensemble modeling approach to detect flowers on herbarium specimens, addressing challenges from diverse floral morphologies and variable specimen and image quality. After finding expert-curated training data essential, they applied the model to 22 million filtered records and labeled 11.1 million as having flowers present.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
99

Updated Oct 3, 2026 · TRV-2026-1263

78Index score
18
EducationNewly addedModerate evidence · 1 source

Integration of generative AI into library systems is creating new possibilities for improving how information is accessed, managed, and disseminated.

A September 2025 review examined how generative AI is being integrated into library systems worldwide. It found the technology offers new possibilities for improving information access, management, and dissemination, while simultaneously raising ethical concerns such as algorithmic bias, privacy breaches, job displacement, misinformation, and digital inequality.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
98

Updated Oct 2, 2026 · TRV-2026-1251

78Index score
19
ScienceNewly addedModerate evidence · 1 source

AlphaFold DB scaled from 300k to over 214 million predicted structures and was integrated into primary resources like PDB and UniProt, expanding structure coverage for research use.

By October 2023, the authors reported that the AlphaFold Protein Structure Database had grown from 300,000 structures in 2021 to over 214 million predicted structures, enabled by AlphaFold2. The paper outlines archiving enhancements, coverage of model organisms and other curated sets, and access routes including FTP and Google Cloud Public Datasets.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
97

Updated Sep 27, 2026 · TRV-2026-1205

78Index score
20
PolicyStableModerate evidence · 1 source

Artificial Intelligence in Pharmaceutical Regulatory Science: Opportunities, Challenges, and Emerging Frameworks: However, robust evidence demonstrating sustained improvements in regulatory performance and long-term operational impact remains limited.

Digital transformation in pharmaceutical regulatory affairs is accelerating as global submissions grow in complexity and traditional document-based workflows reach their limits. Artificial intelligence (AI), particularly natural language processing (NLP), is increasingly being explored to support regulatory data management, document preparation, and decision support activities.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
96

Updated Sep 20, 2026 · TRV-2026-1151

78Index score

AI problems · 770

17
HealthStableModerate evidence · 1 source

AI research in pain medicine still faces challenges with research data generalization, multimodal fusion strategies, model interpretability, and ethical compliance.

A September 2026 review in Journal of Translational Medicine surveyed AI research in pain medicine across three areas: objective pain assessment by fusing facial expressions, voice and physiological signals, automated segmentation of spine, nerves and needle tips from medical images, and AI support for classification, treatment decisions and prognosis in conditions from osteoarthritis to cancer pain.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
95

Updated Sep 14, 2026 · TRV-2026-1080

78Index score
18
HealthStableModerate evidence · 1 source

In non-small-cell lung cancer, AI tools show strong benchmark performance but have repeatedly failed to translate into patient benefit because most models are retrospective, single-center, and validated only on metrics that do not track survival, toxicity, or procedural burden.

This peer-reviewed review examines why AI tools for non-small-cell lung cancer, despite promises of earlier detection and more precise treatment selection and radiotherapy, have rarely changed bedside care. It introduces the biological-groundingd7translational-readiness matrix to map each model on biological grounding and lifecycle validation and to specify the next study needed for clinical advancement.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
94

Updated Sep 8, 2026 · TRV-2026-1017

78Index score
19
LaborStableModerate evidence · 1 source

Currently, there is a lack of comprehensive artificial intelligence frameworks capable of automating the entire meta-analysis workflow, including literature screening, data extraction, and quality assessment.

Traditional secondary meta-analysis workflows are highly labour-intensive, time-consuming, and difficult to update in real time. Currently, there is a lack of comprehensive artificial intelligence frameworks capable of automating the entire meta-analysis workflow, including literature screening, data extraction, and quality assessment.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
93

Updated Sep 5, 2026 · TRV-2026-0983

78Index score
20
HealthStableModerate evidence · 1 source

Results Yet, challenges persist, including algorithmic bias, data inequity and variable regulatory standards across regions.

Background Artificial intelligence (AI) is transforming global health care through innovations in deep learning, generative models and agentic AI systems. Traditional reductionist approaches to complex pathophysiological pathways fail to capture the true complexity of disease, motivating the adoption of network medicine (NM), which models biological systems as dynamic, interconnected networks.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
93

Updated Sep 3, 2026 · TRV-2026-0972

78Index score

Recomputed live from the record · Oct 11, 2026, 1:37 AM