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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

401
LifestyleStableModerate evidence · 1 source

Explainable and domain-adaptive prediction models for refrigerant charging in air conditioning systems within industrial processes: Validation on six AC sensor datasets demonstrates that AC-RPX achieves state-of-the-art accuracy and F1 scores, significantly outperforming conventional deep learning and domain adaptation baselines.

Accurate prediction of refrigerant deficiency in consumer air conditioning (AC) systems is critical for optimizing energy efficiency and operational stability. However, existing data-driven models often suffer from significant performance degradation due to domain shift across different AC types and a lack of explanatory transparency.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Sep 3, 2026 · TRV-2026-0968

68Index score
402
HealthStableModerate evidence · 1 source

Methodological Reporting Quality of Artificial Intelligence-Based Orthopedic Studies: A Literature Analysis: Work in this area would benefit from closer attention to generalizability, clinical relevance and the existing reporting guidelines.

Objective We set out to examine how completely artificial intelligence (AI)-based orthopedic studies report their methods. Methods A PubMed search covering 2023-2025 was run with a predefined strategy.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Sep 3, 2026 · TRV-2026-0967

68Index score
403
BusinessStableModerate evidence · 1 source

The accuracy of the system was validated via twice-weekly manual cycle count. The model uses artificial intelligence and various algorithms to recommend inventory optimizations based on transaction data and notably requires no electronic health record integration.

Purpose Ambulatory clinics manage high-cost medications with little visibility into quantity or movement, leaving unrealized opportunities for inventory optimization. Automated dispensing cabinets, common in inpatient settings, address this issue but require significant capital investment, forcing clinics into complex workflows to balance demand for high-cost medications with minimizing waste.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Sep 3, 2026 · TRV-2026-0966

68Index score
404
ClimateStableModerate evidence · 1 source

To enhance prediction accuracy across a broad concentration range, a machine learning model was introduced to develop a high-precision quantitative analysis method for Fe 3+ .

The concentration of iron ions is a crucial indicator for assessing water quality. In this study, nitrogen-doped carbon dots (NCDs) were synthesized using a microwave-assisted method with citric acid and urea as precursors, thereby establishing a fluorescence sensing platform for the detection of alkaline pH and Fe 3+ .

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Sep 2, 2026 · TRV-2026-0960

68Index score

AI problems · 771

401
ScienceStableModerate evidence · 1 source

Application of AI to implementation faces persistent risks of insufficient or biased data, equity and access barriers, and concerns around data security, trust and ethics requiring oversight.

The paper reviews persistent bottlenecks in moving evidence-based interventions into routine care and describes how data science and AI methods can help extract and synthesize implementation materials, analyze context, and support stakeholder engagement and adaptation, illustrated with a live precision oncology project and the ImpleMATE platform.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 23, 2026 · TRV-2026-0517

67Index score
402
HealthStableModerate evidence · 1 source

LLM-based multiturn chatbots for cancer patients and caregivers lack consistent reporting on safety risks and mitigation, and lack evaluation of conversational continuity and memory.

As of July 2026, a scoping review of literature from January 2022 to January 2026 identified only eight studies describing LLM-based chatbots that support multiturn dialogue for patients with cancer and informal caregivers. Most were prototype systems using ChatGPT-based models, some with retrieval-augmented generation, designed for information provision or emotional support.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 22, 2026 · TRV-2026-0508

67Index score
403
PolicyStableModerate evidence · 1 source

Small and medium-sized enterprises continue to face significant challenges in effective AI adoption, with ten critical barriers across technology, organization, and environment including data access, skill shortages, cultural resistance, infrastructure limitations, and weak governance.

This peer-reviewed conceptual analysis examines why small and medium-sized enterprises struggle to adopt AI despite its transformative potential. Using the technology-organization-environment framework combined with diffusion of innovations attributes, it identifies ten critical challenges across data access, skills, culture, infrastructure, and governance, and pairs them with context-sensitive solutions.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 22, 2026 · TRV-2026-0500

67Index score
404
PolicyStableModerate evidence · 1 source

AI systems face major risks across lifecycle stages including reliability failures and bias, and optimizing trustworthiness forces trade-offs such as fairness versus efficiency or privacy versus transparency.

As of its July 2025 publication, this peer-reviewed review examined how trust in AI systems can be systematically measured, analyzing frameworks including the NIST AI Risk Management Framework, the AI Trust Framework and Maturity Model, and ISO/IEC standards around fairness, transparency, privacy and security.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 22, 2026 · TRV-2026-0499

67Index score

Recomputed live from the record · Oct 13, 2026, 4:18 AM