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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

293
HealthNewly addedModerate evidence · 1 source

AI-assisted conavigation can augment digital health navigators with lived experience by handling triage, documentation, and engagement monitoring in mental health services.

As AI expands across mental health services, this peer-reviewed article asks whether it can replace digital health navigators with lived experience and concludes the role resists full automation. It identifies least-substitutable strengths in service knowledge, cultural grounding, and trust, and proposes a conavigation model where AI supports triage, documentation, and engagement monitoring.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 3, 2026 · TRV-2026-1257

68Index score
294
HealthNewly addedModerate evidence · 1 source

Integrating AI into clinical practice can improve disease diagnosis, treatment selection, and lab testing by leveraging large datasets to increase accuracy, reduce costs, save time, and minimize human errors.

Published September 22, 2023, this peer-reviewed review in BMC Medical Education surveys the current state of AI in clinical practice, covering disease diagnosis, treatment recommendations, patient engagement, and laboratory testing. It reports that AI tools can use large datasets to find patterns and potentially surpass human performance in some aspects.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 2, 2026 · TRV-2026-1252

68Index score
295
ScienceNewly addedModerate evidence · 1 source

AI and humans combining capabilities in service settings can achieve joint outcomes through teamwork rather than replacement.

On 2025-09-17, a peer-reviewed article in Journal of Service Management reported an AI-based systematic review of the SERVSIG Literature Alert database to map human-machine collaboration in service industries. The authors traced the evolution of the field and developed an integrative framework describing foundation, process, and outcomes of H-M teamwork, and mapped special-issue empirical studies to it.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 2, 2026 · TRV-2026-1250

68Index score
296
ScienceNewly addedModerate evidence · 1 source

AI-assisted predictive microbiome design helps identify and deploy beneficial microbial inoculants that lower pest and disease burden and enhance productivity in cereal, legume, and horticulture crops.

Published October 1 2026, this peer-reviewed review synthesizes ecological, molecular, and technological work on eco-smart biocontrol, positioning the rhizosphere as a hotspot for plant-microbe interactions and describing how multi-omics and artificial intelligence-assisted predictive microbiome design are used to discover and design microbial biocontrol agents.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 2, 2026 · TRV-2026-1249

68Index score

AI problems · 771

293
HealthStableModerate evidence · 1 source

The same canine lymphoma cytology AI performed less favorably on B-cell versus T-cell classification, prompting concern that allowing outputs to influence reports without pathologist review and without assessing whether the specimen supports the claim could lead to unsupported clinical claims.

On August 24, 2026, a commentary in the Journal of Veterinary Diagnostic Investigation argued that veterinary diagnostic laboratories should decide what AI outputs are permitted to do in service, not just how accurate models are. It defines service entry as the moment an output can influence triage, interpretation, draft reports, or result release, and proposes a standard operating procedure covering intended use, reviewer and signer roles, disclosure, input compatibility, refusal conditions, pathologist override, QC monitoring, and stop rules.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 25, 2026 · TRV-2026-0877

68Index score
294
HealthStableModerate evidence · 1 source

AI use in mental health care is limited by data bias, limited external validity, methodological heterogeneity, insufficient transparency, and weaker evidence for sustained therapeutic interventions.

This umbrella review synthesized 27 systematic reviews with over 14 million participants to examine AI applications in mental health care between 2021 and 2025. It found AI improved early detection and risk stratification for depression, anxiety, stress, PTSD and suicidal ideation, and that chatbots and mobile platforms expanded access and engagement.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 25, 2026 · TRV-2026-0874

68Index score
295
Media & ArtsStableModerate evidence · 1 source

Current newsroom AI assessments focus narrowly on technical accuracy or efficiency and struggle to capture broader organizational and ethical implications for trust, governance, and human collaboration.

As of November 2025, this peer-reviewed review examines agentic AI in newsrooms that can autonomously plan, decide, and generate content. Analyzing 46 sources from 2015-2025, it finds current evaluations emphasize technical accuracy and efficiency while neglecting trust, governance, and collaboration.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 24, 2026 · TRV-2026-0868

68Index score
296
HealthStableModerate evidence · 1 source

In the matched Stage II-III cohort, patients with low lymph node ratio showed potential harm from adjuvant chemotherapy, indicating heterogeneous treatment effects and risk of overtreatment.

Researchers used a Causal Forest causal machine learning model on a retrospective cohort of gastric cancer patients treated between 2007 and 2017 to estimate who benefits from adjuvant chemotherapy. The model identified lymph node ratio as the most dominant predictor of benefit, with a significant interaction at a 0.25 threshold in a propensity-matched Stage II-III cohort.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 24, 2026 · TRV-2026-0866

68Index score

Recomputed live from the record · Oct 12, 2026, 1:29 PM