The Index

What the evidence says.What the public feels.

Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.

AI gains · 788

GainHealth· Stable· Evidence: Moderate (1 source)

AI models Gemini and FaceApp generated post-denture facial images rated as esthetically comparable to actual clinical outcomes by patients and experts.

A prospective study of 14 edentulous patients compared actual post-denture facial photographs with AI-generated predictions from pretreatment images using Gemini and FaceApp, assessing patient preference, expert esthetic ratings, and quantitative anthropometric measurements.

Impact 30%49

Evidence 25%95

Scale 20%35

Confidence 15%87

Recency 10%97

Updated Aug 31, 2026 · TRV-2026-0938

GainHealth· Stable· Evidence: Moderate (1 source)

A multimodal deep learning model analyzing resting 12-lead ECG and clinical data was feasible for pre-participation cardiovascular screening and achieved moderate discrimination for fitness for competitive sports.

In a prospective single-center study of 526 competitive athletes undergoing routine pre-participation cardiovascular screening, researchers tested a multimodal deep learning model that combined resting 12-lead ECG with clinical variables to predict clinical fitness classification. Using stratified 10-fold cross-validation, the model achieved test accuracy of 0.64 b1 0.08 and AUC 0.72 (0.66-0.78).

Impact 30%49

Evidence 25%95

Scale 20%35

Confidence 15%87

Recency 10%97

Updated Aug 31, 2026 · TRV-2026-0937

GainLifestyle· Stable· Evidence: Moderate (1 source)

Nine machine learning models using baseline frailty index predicted motoric cognitive risk syndrome, with higher frailty linked to increased MCR risk in adults aged 45 and older.

Researchers analyzed 3388 CHARLS participants aged 45 and older across 2011 to 2015 to examine frailty and motoric cognitive risk syndrome. They built nine machine learning models using baseline frailty index to predict MCR risk and tracked changes in frailty status. By follow-up end, 127 participants (3.74%) developed MCR, with higher baseline FI, total FI, and change in FI associated with increased risk.

Impact 30%49

Evidence 25%95

Scale 20%35

Confidence 15%87

Recency 10%97

Updated Aug 31, 2026 · TRV-2026-0936

GainHealth· Stable· Evidence: Moderate (1 source)

Models trained on routinely collected social care records predicted future hospital admissions and all-cause mortality with meaningful discriminative performance reaching AUROC 0.893.

On 2026-08-29, a peer-reviewed study reported machine learning models trained on pseudonymised social care records from 27,590 adults in Oxfordshire to predict future care plan needs, hospital admissions, and all-cause mortality across multiple horizons, finding meaningful discrimination for clinical outcomes.

Impact 30%49

Evidence 25%95

Scale 20%35

Confidence 15%87

Recency 10%97

Updated Aug 31, 2026 · TRV-2026-0935

AI problems · 631

ProblemHealth· Stable· Evidence: Moderate (1 source)

SPECT imaging features added limited prognostic value and implementing models on real-world data did not significantly close the gap between prognostic modeling and clinical implementation.

Researchers developed and validated Random Forest and Gradient Boosting models to predict Hoehn and Yahr scores 5 years after 123I-ioflupane SPECT imaging, using harmonized data from 343 real-world patients and 134 PPMI patients with 83 overlapping features. Models using 2 years of clinical follow-up achieved the highest accuracy, driven by early H&Y scores, gait severity, and select imaging features.

Impact 30%49

Evidence 25%95

Scale 20%35

Confidence 15%87

Recency 10%90

Updated Jul 27, 2026 · TRV-2026-0574

ProblemEducation· Stable· Evidence: Moderate (1 source)

Integrating AI into education creates challenges that require comprehensive educator training and curriculum adaptation to align with societal structures.

A peer-reviewed discussion published February 2024 examines AI integration in education, arguing that personalized learning and support for diverse requirements including special needs students depends on developing AI literacy, prompt engineering proficiency, and critical thinking, while requiring educator training and curriculum adaptation.

Impact 30%49

Evidence 25%95

Scale 20%35

Confidence 15%87

Recency 10%90

Updated Jul 26, 2026 · TRV-2026-0573

ProblemScience· Stable· Evidence: Moderate (1 source)

Without careful dissection of how biases are encoded into AI health technologies, underlying health dataset limitations risk perpetuating existing health inequalities at scale.

On Dec 18, 2024, The Lancet Digital Health published the STANDING Together consensus recommendations, developed through a systematic review, stakeholder survey, Delphi process with 194 voters from 25 countries, public consultation, and international interviews involving over 350 representatives from 58 countries. The process produced 29 recommendations in two parts covering documentation of health datasets and use of health datasets.

Impact 30%49

Evidence 25%95

Scale 20%35

Confidence 15%87

Recency 10%90

Updated Jul 24, 2026 · TRV-2026-0551

ProblemHealth· Stable· Evidence: Moderate (1 source)

Federated learning deployments in smart healthcare remain vulnerable to adversarial attacks, data poisoning, and model inversion, plus practical barriers of heterogeneous data, scalability, and system interoperability.

This peer-reviewed review from December 2024 examines federated learning as a decentralized approach for smart healthcare, where institutions collaborate on machine learning without sharing raw patient data, integrated with IoT devices, wearables, and remote monitoring for real-time predictive analytics.

Impact 30%49

Evidence 25%95

Scale 20%35

Confidence 15%87

Recency 10%90

Updated Jul 24, 2026 · TRV-2026-0550

Recomputed live from the record · Sep 15, 2026, 10:18 PM