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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

341
HealthStableModerate evidence · 1 source

Machine learning with SHAP interpretability predicted enfortumab vedotin toxicities including grade 3-4 events, diarrhea, and dose skipping in advanced urothelial carcinoma patients using real-world data.

In a proof-of-concept study published September 16, 2026, investigators used real-world data from 542 patients with advanced urothelial carcinoma to train machine learning models to predict toxicities from enfortumab vedotin, including severe adverse events and specific events like diarrhea and cutaneous toxicity.

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

Updated Sep 17, 2026 · TRV-2026-1121

68Index score
342
HealthStableModerate evidence · 1 source

AI tools for sepsis, VTE, diabetic retinopathy, cardiac amyloidosis, and pediatric asthma were feasibly deployed into routine clinical practice across diverse health care settings.

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
343
HealthStableModerate evidence · 1 source

Gemini AI-generated overviews provided good-quality information for management of lower limb pain in children with hypermobility-associated conditions when retrieved via Google searches.

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
344
HealthStableModerate evidence · 1 source

Across UK Biobank, NHANES, and a Tianjin hospital cohort, machine learning models flagged lower bone mineral density as a recurrent contributor to age-related macular degeneration risk prediction alongside age.

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

AI problems · 771

341
CrimeStableModerate evidence · 1 source

Frontier AI chatbots frequently exhibited concerning behavior when interacting with simulated users with psychiatric vulnerabilities, especially when supportive responses reinforced underlying vulnerability mechanisms.

On 2026-08-07, Nature Medicine published a clinically validated auditing framework called SIM-VAIL that simulates users with psychiatric vulnerabilities to test frontier chatbots including Claude, ChatGPT, Gemini, Grok and Llama. Across 810 multi-turn conversations with 30 simulated profiles and scoring on 13 risk dimensions, the study observed widespread concerning behavior that accumulated over turns.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
88

Updated Aug 10, 2026 · TRV-2026-0726

67Index score
342
HealthStableModerate evidence · 1 source

AI augmentation of oncology trials faces cross-cutting equity, data quality and drift, transparency, and regulatory oversight challenges, while AI approaches intended to replace clinical evidence generation lack prospective validation.

On 2026-08-07, a Review in Nature Reviews Clinical Oncology described how AI enabled by electronic health record datasets and machine learning is being applied across pre-trial design, conduct, and post-trial inference in oncology. It reported that the most immediate evidence-supported uses are operational workflows under human oversight, including patient identification, eligibility assessment, data extraction, and trial monitoring, now implemented at select cancer centres.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
88

Updated Aug 10, 2026 · TRV-2026-0725

67Index score
343
HealthStableModerate evidence · 1 source

Analysis of 125 compensated orthodontic cases found the majority of patient harms were avoidable and driven by multiple interacting human factors such as monitoring and clinical knowledge and organizational factors such as protocols and workplace culture.

Researchers retrospectively reviewed 125 orthodontic claims approved for compensation by the Danish Dental Compensation Association from September 2019 to August 2024. They applied Eindhoven incident analysis and AI-assisted qualitative analysis using a large language model to identify root causes and rate perceived avoidability on a 6-point scale.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
88

Updated Aug 9, 2026 · TRV-2026-0717

67Index score
344
HealthStableModerate evidence · 1 source

Multimodal AI can infer sensitive information without patient awareness and embed those inferences as durable data objects in medical records without clear provenance, where they acquire the status of observed clinical facts.

A 2026 perspective in AI and Ethics examines multimodal AI that fuses images, speech, behavior, physiological signals and text into unified representations for cross-modal inference and synthesis in biomedicine. The authors note potential clinical benefits while warning that inferred data can be materialized as images or clinical text and inserted into records without provenance.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
88

Updated Aug 9, 2026 · TRV-2026-0716

67Index score

Recomputed live from the record · Oct 12, 2026, 8:17 PM