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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.

1,706 results
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AI gains · 935

357
HealthStableModerate evidence · 1 source

Systematic review found most ML algorithms improved CVD risk prediction compared with Framingham Risk Score by incorporating sociodemographic predictors and modeling non-linear interactions.

A systematic review published 12 September 2026 searched three databases to 1 January 2025 and included 29 studies that directly compared machine learning CVD risk predictions with the Framingham Risk Score in healthy adults. Twenty-three studies reported improved predictive ability, often by adding sociodemographic predictors absent from FRS or costly diagnostics such as CT angiography.

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

Updated Sep 14, 2026 · TRV-2026-1081

68Index score
358
HealthStableModerate evidence · 1 source

AI-assisted orthodontic systems and remote monitoring platforms reduced in-person appointments while maintaining clinical standards and improving patient compliance in included studies.

This scoping review examined 23 studies published between January 2000 and September 2025 on AI in orthodontic diagnosis, treatment planning, appliance design, and teledentistry. It found AI-assisted systems can improve diagnostic precision and reduce clinical workload, and that remote monitoring platforms can cut in-person appointments while maintaining standards and improving compliance.

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

Updated Sep 14, 2026 · TRV-2026-1078

68Index score
359
HealthStableModerate evidence · 1 source

XGBoost models trained on 5798 participants identified depression risk among older adults with chronic diseases across cognitive impairment levels with accuracy up to 0.767 and good calibration.

Researchers developed three XGBoost models to identify depression risk among older adults with chronic illnesses, stratified by cognitive impairment status, using 5798 participants from the Chinese Longitudinal Healthy Longevity Survey and SHAP for interpretability.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 13, 2026 · TRV-2026-1070

68Index score
360
HealthStableModerate evidence · 1 source

Large language models including ChatGPT-o3, ChatGPT-5.2, Gemini 3 and DeepSeek provided generally acceptable clinical accuracy when answering 30 frequently asked patient questions about robotic-assisted total knee arthroplasty.

A September 2026 peer-reviewed study in The Knee compared four large language models on 30 common patient questions about robotic-assisted total knee arthroplasty, evaluating responses with DISCERN, QAMAI, a 5-point clinical accuracy scale, and PEMAT and Flesch-Kincaid readability measures.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 13, 2026 · TRV-2026-1069

68Index score

AI problems · 771

357
HealthStableModerate evidence · 1 source

AI applications in nutrition face persistent challenges with model transparency, ethical use of health data, and limited generalizability, particularly underrepresentation of low-resource settings.

A systematic review published October 14, 2025 synthesized peer-reviewed literature from January 2020 to July 2025 on AI in nutrition and dietetics, covering dietary assessment, personalized nutrition and chronic disease management, generative AI and conversational agents, public health nutrition, sensory science, and ethics.

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

Updated Aug 6, 2026 · TRV-2026-0669

67Index score
358
HealthStableModerate evidence · 1 source

Low-cost AI translations of oncology informed consent forms showed variable language equivalence and remain unsuitable for clinical use without human review.

On August 5, 2026, a peer-reviewed study in JCO Oncology Practice evaluated AI translation of three oncology clinical trial informed consent forms from English to Spanish, comparing DeepL Pro, ChatGPT-4o, and a medically trained model Med_English2Spanish against certified translations using five equivalence domains scored by two bilingual physicians.

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

Updated Aug 6, 2026 · TRV-2026-0663

67Index score
359
HealthStableModerate evidence · 1 source

AI chatbots used for intimate support regularly hallucinate clinical guidance, validate dysfunctional beliefs, handle crises without accountability, and may cultivate isolation.

On August 5, 2026, a viewpoint in the Journal of Participatory Medicine described how large language model chatbots and purpose-built companion agents are being used by millions for emotional support, distress processing, and relationship-like interaction, with 48.7% of people with self-reported mental health concerns reporting use for mental health support.

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

Updated Aug 6, 2026 · TRV-2026-0662

67Index score
360
HealthStableModerate evidence · 1 source

The same AI responses lacked guaranteed, verifiable sourcing and were not tested for actual patient comprehension, with authors noting need to adapt materials to meet health literacy standards before reliable use in safety-critical tracheostomy education.

On 2026-08-04, a peer-reviewed cross-sectional study reported testing ChatGPT4, Gemini 2.0, Copilot, DeepSeek V3, and Grok 3 on 12 tracheostomy care questions, with three blinded laryngologists rating responses for accuracy, completeness, clarity, and sourcing, and readability measured with nine metrics.

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

Updated Aug 5, 2026 · TRV-2026-0657

67Index score

Recomputed live from the record · Oct 12, 2026, 10:39 PM