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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 933 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.

933 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.

1,703 results
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AI gains · 933

213
HealthStableHigh evidence · 2 sources

AI-driven wearable bioelectronics enable continuous monitoring of cardiac activity, glucose and biomarkers to support early disease detection, chronic disease management and remote patient monitoring.

As of the June 2025 review, integration of AI with wearable bioelectronics was presented as enabling proactive, personalized monitoring of cardiac activity, glucose levels and biomarkers, with applications in early detection, chronic condition management and precision therapeutics.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
99
Recency 10%
85

Updated Jul 23, 2026 · TRV-2026-0520

70Index score
214
HealthStableHigh evidence · 2 sources

AI collected patient histories more effectively than general practitioners during initial consultations, with stronger performance in older patients and for allergies and family history of cancer.

In an online cross-sectional study in France with 204 general practitioners and 942 patient histories, researchers compared AI to physicians in recording histories during initial consultations across categories such as chronic disease, surgical, obstetric, occupational, allergies, and family cancer history.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
99
Recency 10%
84

Updated Jul 23, 2026 · TRV-2026-0518

70Index score
215
HealthStableHigh evidence · 3 sources

AI and large language models have matured to improve clinicians' diagnostic decision-making during bedside and clinic consultations and help institutions increase diagnostic safety, addressing preventable diagnostic errors.

A July 2026 narrative review in Diagnosis examined whether artificial intelligence and large language models can reduce diagnostic error in bedside and clinic consultations. It summarized evidence that diagnostic errors affect 5-10% of admissions and visits and contribute to patient harm and hospital mortality.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
84

Updated Jul 22, 2026 · TRV-2026-0506

70Index score
216
EducationStableHigh evidence · 5 sources

Preservice teachers reported high acceptance of ChatGPT as a tool that can enhance metacognitive self-regulated learning and help address teaching and learning challenges.

By April 2024, researchers at UTM University's School of Education had tested ChatGPT acceptance for metacognitive self-regulated learning with 300 preservice teachers. Participants completed a scenario-based activity and a Technology Acceptance Model questionnaire, followed by reflections and interviews, with responses analyzed via structural equation modelling.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0443

70Index score

AI problems · 770

213
HealthStableModerate evidence · 1 source

Real-world implementation was constrained by EHR integration difficulties, data complexity, regulatory requirements, and variation in clinical workflows.

The Augmented Intelligence in Medicine and Healthcare Initiative, led by Kaiser Permanente and funded by the Gordon and Betty Moore Foundation, funded five projects to move AI beyond controlled model development into routine care. The projects addressed sepsis management, venous thromboembolism risk, diabetic retinopathy screening, cardiac amyloidosis detection, and pediatric asthma risk prediction, with synthesis of implementation processes and lessons learned.

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

Updated Sep 17, 2026 · TRV-2026-1120

68Index score
214
ScienceStableModerate evidence · 1 source

Model performance fell to chance in Critical Care and Outcomes and Systems, Technology, and Process Optimization, where publication depended on institutional factors absent from the poster such as multicenter scaffolding, mentorship, and senior author fluency.

Researchers tested whether GPT-4.1 could predict publication from poster images alone for 260 abstracts presented at the 2021-2022 AAST Annual Meetings. By September 2026 publication date, 142 had published, and the model achieved 58.5% accuracy overall, with higher accuracy in Violence, Societal, and Behavioral and Hemorrhage, Resuscitation, and Vascular Control, but chance-level performance in other domains.

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

Updated Sep 16, 2026 · TRV-2026-1114

68Index score
215
HealthStableModerate evidence · 1 source

Gemini AI-generated overviews for pediatric hypermobility leg pain scored lowest on completeness and provision of resources and references, requiring users to verify that cited references exist and are relevant.

A peer-reviewed study in Journal of Paediatrics and Child Health assessed online information for managing lower limb pain in children and adolescents with hypermobility-associated conditions. Using Google searches, the authors evaluated 20 webpages and four Gemini AI-generated overviews for quality with standardized tools.

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

Updated Sep 16, 2026 · TRV-2026-1113

68Index score
216
HealthStableModerate evidence · 1 source

The same machine learning finding lacks proven incremental clinical utility, relies on cohorts with differing AMD ascertainment and BMD measurement, and animal retinal changes cannot be read as direct AMD validation.

Researchers examined whether bone mineral density relates to age-related macular degeneration using data from UK Biobank, NHANES, and a Tianjin hospital cohort, plus genetic Mendelian randomization, proteomics, metabolomics, machine learning, and a low-BMD rat model. By the September 2026 publication date they reported lower BMD was consistently associated with higher AMD risk and that machine learning models identified BMD as a recurrent predictive contributor alongside age.

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

Updated Sep 16, 2026 · TRV-2026-1112

68Index score

Recomputed live from the record · Oct 12, 2026, 3:37 AM