Truace
Subscribe
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
Show filters and sorting

Download every matching row, not just this page:Export CSVExport JSON

AI gains · 933

173
HealthStableModerate evidence · 1 source

Unsupervised hierarchical clustering integrated BMI, activity, comorbidities, HAQ and TNF pathway genetics to identify three RA subgroups with differing TNFi response rates, including a better-prognosis cluster with 73.5% response.

Researchers applied unsupervised hierarchical clustering to 294 rheumatoid arthritis patients to integrate clinical, demographic, and genetic data related to Tumor Necrosis Factor inhibitor response. By publication date 2026-08-17 they reported distinct responder characteristics and identified three subgroups ranging from 73.5% response to 82.9% therapeutic failure.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 18, 2026 · TRV-2026-0822

72Index score
174
HealthStableModerate evidence · 1 source

Generative AI models like ChatGPT and DALL-E are being applied and deployed in healthcare for medical imaging, drug discovery, personalized treatment, and clinical operations, including specific use cases such as visual snow syndrome diagnosis and molecular drug optimization.

This January 2024 IEEE Access review surveys generative AI in healthcare, describing models including ChatGPT, DALL-E, Bard, and seven healthcare-customized LLMs such as Med-PaLM, BioGPT, and DeepHealth. It catalogs applications from medical imaging and drug discovery to personalized treatment, simulation and training, clinical trial optimization, and medical chatbots, and details four real-world scenarios employing GAI: visual snow syndrome diagnosis, molecular drug optimization, medical education, and dentistry.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
89

Updated Aug 14, 2026 · TRV-2026-0760

72Index score
175
ClimateStableModerate evidence · 1 source

Prospective early stopping with patience 10 preserved chest radiograph classification performance while lowering total training emissions by up to 38% and raising carbon efficiency by up to 76% compared to fixed 20-epoch training.

In a study published August 12, 2026, researchers quantified CO2eq emissions for training ResNet-50, DenseNet-121 and EfficientNet-B0 on 128,907 chest radiographs for 20 epochs. They found validation loss minima at median epochs 2 to 4, meaning most emissions occurred after the best checkpoint, and compared retrospective selection, prospective early stopping, and fixed-epoch training on AUC and energy use.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 14, 2026 · TRV-2026-0753

72Index score
176
HealthStableModerate evidence · 1 source

Large language models directed patients with musculoskeletal complaints to currently practicing, specialty-appropriate providers in the requested city, with ChatGPT achieving 100% appropriateness in the tested queries.

Researchers prompted ChatGPT, DeepSeek, and Gemini with standardized musculoskeletal complaints for Lynchburg, VA and Trumbull, CT, and judged whether recommended physicians were currently practicing locally in the relevant specialty and whether phone numbers were correct. By the August 13, 2026 publication date, ChatGPT was appropriate in all 17 recommendations, while Gemini and DeepSeek were appropriate in 43% and 40% of recommendations respectively.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 14, 2026 · TRV-2026-0752

72Index score

AI problems · 770

173
HealthNewly addedModerate evidence · 1 source

Dataset diversity, external validation, interpretability, and generalizability limitations across populations and imaging protocols currently prevent clinical adoption of AI histopathology tools for melanoma.

By October 2026, a review in the International Journal of Dermatology examined how computational pathology and convolutional neural networks applied to H&E whole-slide images could improve melanoma diagnosis and risk stratification beyond current AJCC staging, including integration with transcriptomic and spatial proteomic data.

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

Updated Oct 2, 2026 · TRV-2026-1247

68Index score
174
HealthNewly addedModerate evidence · 1 source

DTI-based AI models for Alzheimer's diagnosis suffer substantial performance degradation in multicenter or external validation, limiting clinical translation.

A 2026 review in Reviews in the Neurosciences examined 98 studies from 2010 to 2026 on diffusion tensor imaging-based AI for Alzheimer's disease, using an Input-Model-Fusion framework to assess modality choice, algorithm paradigms, and fusion strategies.

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

Updated Oct 2, 2026 · TRV-2026-1244

68Index score
175
HealthNewly addedModerate evidence · 1 source

Counselors reported low confidence in chatbots for sensitive result disclosure and major concerns about patient comprehension and information accuracy.

A peer-reviewed survey of 217 genetic counselors and students in North America examined current uptake of AI chatbots in genetic counseling. While 76.5% reported using general chatbots outside clinical settings, only 8.8% reported using or recommending clinical genetics chatbots, mainly for at-risk family communication and patient education.

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

Updated Oct 1, 2026 · TRV-2026-1236

68Index score
176
HealthNewly addedModerate evidence · 1 source

AI assistance reduced specificity because of increased false-positive classifications, particularly among indeterminate thyroid cases.

In a 100-slide archival study, two cytopathologists and one cytologist reviewed thyroid fine-needle aspiration cases using microscopy and two AI-assisted whole-slide imaging modes powered by the AIxTHY algorithm. By the October 2026 publication date, the authors reported improved agreement with expert consensus for higher-risk Bethesda categories and a 40% to 65% reduction in review time.

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

Updated Oct 1, 2026 · TRV-2026-1235

68Index score

Recomputed live from the record · Oct 11, 2026, 9:40 PM