AI News Archive: July 19, 2026 — Part 6
Sourced from 500+ daily AI sources, scored by relevance.
- AIRE
AIREPMS - Easier Tennant and Property management
- East Asia Insights
East Asia Parliament Members Archive
- License Free Walkie Talkie suppliers
+91-80732-86105, +91-93425-84959
- Media Manager for Laravel
Upload files from anything in Laravel
- Jeread
Read, write & share stories that inspire
- Keepvio
Organize tasks, notes, and projects in one workspace.
- GetSpaceHygiene
See what's eating your storage. You choose, you delete
- Amitry Product Category Slider
Native WooCommerce sliders built for performance
- Arth Vani
Arth Vani – The Economic Voice
- Couple Counseling in Chandigarh
Couple Counseling in Chandigarh
- Gamedexy
Mobile game UI and UX flows, made searchable
- Kabraso Multi-Purpose Cooperative
A Multi-Purpose Cooperative Resource Hub
- Job Lobster
Your AI agent hunts the jobs. You earn the offer.
- Copper LPBF RFQ Review
Structured intake for copper metal 3D printing projects
- Filmory – Pro Film Camera
Turn everyday photos into timeless film memories.
- 7Vault: Secure Offline Vault
Private offline vault for passwords, files & notes
- DocuHonyaku
Japan-ready document translation, layout intact
- MoveToZero
Break up sitting with guided walking intervals
- AI Resources Directory
Learn, build and collaborate with artificial intelligence
- EchoScribe
Private, on-device transcription for meetings, interviews
- ProfitSearcher
Discover high-growth SaaS before they go mainstream
- YourOnlineClock
Free online timers, stopwatch & Pomodoro
- Re-QR Invoice Validator- KSA
Offline Invoice QR Validation for Professionals.
- Bonx
Business Management Software for Installation Companies
- Locker
The all-in-one workspace for students.
- AffiliateTree
Give followers a code, not a bio-link maze
- E-Invoicing Solution For UAE Mandate
CherrieBS provides UAE E-invoicing intergration for ERPs.
- Daily Detective
A new murder mystery to solve every day
- One-Click Answers
Snap a question and get an answer with AI.
- Video Background Changer
AI Video Background Changer
- EmpowerLeap
The AI execution coach for solo founders
- SceneFlow
PDF to Video Converter – Turn PDFs into Videos with AI
- NeuroHub
Generate ALL
- VitaMind AI
AI health insights beyond your smartwatch.
- Tura
Macro execution for fewer coding-agent turns
- LogicMint
Turn a sentence into a hosted app—with code you own
- tonvio
Create realistic AI voiceovers in minutes
- AiLotteryPredictor
Real data. Real models. Honest predictions.
- Artelligence OS
Next-Gen Local AI Intelligence OS for macOS
- ChatLodge - Save & Search Your AI Chats
Search by meaning over saved AI chat answers or any text
- ezHumanizer
ezHumanizer
- ClipMatch
Turn your camera roll into social-ready videos.
- ImageToVideo AI.org
Turn text or images into video with any AI model.
- FashionMix FIND
Find any clothing item from a single photo.
- PDFSummarizer.net
Free AI summarizer for documents, e-books, slides & images
- Learning to read a second language establishes a parallel L2 representation alongside the native one in the VWFA
Learning to read a second language requires the brain to incorporate a new writing system into an already established native-language reading network, yet how this process reshapes the Visual Word Form Area (VWFA) remains poorly understood. Using fMRI with a passive viewing paradigm, we systematically investigated how VWFA responses to Chinese (L1) and English (L2) words evolved across three groups of native Mandarin-speaking children at distinct stages of L2 literacy acquisition: L2 pre-readers, L2 beginning readers, and L2 advanced readers. We combined univariate activation analyses, representational similarity analysis, and supervised machine learning classification to investigate two theoretical accounts of VWFA reorganization: the Overlay Model, which predicts stable L1 responses as L2 responses emerge, and the Competing Model, which predicts competitive reallocation of neural resources from L1 to L2. We found that although the VWFA already exhibited robust selective responses to L1 words, L2-word selectivity was absent in L2 pre-readers but emerged robustly in beginning readers, with L1-word selectivity remaining stable throughout. Representational similarity analysis further revealed that robust L2 word representations within the VWFA emerged only after children began learning to read L2, while L1 word representations remained stable across all three groups. Finally, supervised machine learning analyses successfully discriminated among the three L2 literacy groups on the basis of L2 but not L1 activation patterns, indicating that VWFA responses to L2 alone were sufficient to capture children's stages of L2 literacy acquisition, whereas responses to L1 were insensitive to L2 learning experience, providing no support for the Competing Model's prediction of competitive neural reallocation away from L1. Together, these findings support the Overlay Model, demonstrating that the VWFA incorporates a new writing system within its existing cortical resources without compromising L1 print processing.
- Validating Artificial Intelligence Guidance for Ultrasound Acquisition and Remote Interpretation
Background: Venous thromboembolism (VTE), including deep vein thrombosis (DVT), remains a major global health burden. Diagnostic pathways rely on ultrasound but are limited by availability and prolonged time-to-imaging. Novel artificial intelligence (AI) guidance systems have been designed to enable non-ultrasound-trained operators to acquire proximal lower extremity compression ultrasounds for remote clinician interpretation. Methods: This multicenter, double-blinded, prospective, nonrandomized study evaluated the performance of an AI guidance system (ThinkSono Guidance, ThinkSono, GmbH). Patients underwent AI-guided ultrasound(s) and standard of care ultrasound(s). Primary and secondary endpoints were image quality, sensitivity and specificity for proximal DVT, and prioritization specificity, a measure of specificity in identifying patients requiring standard of care ultrasound after AI-guided scan. Results: Of 634 recruited subjects, 594 were analyzed, with 67 DVTs across 700 scans. 86.83% of AI-guided scans achieved diagnostic image quality. Triage sensitivity was 92.86%, triage specificity 39.12%, prioritization specificity 97.96%. Standard of care ultrasounds could be avoided in 35.32% of patients. Total median AI-guided scan and review time was 7.57 minutes. Conclusions: Clinician-reviewed AI-guided scans were rapid, sensitive for DVT, and specific for prioritizing patients requiring standard of care ultrasounds. These findings suggest AI-guided ultrasound may be a scalable triage strategy to expand DVT evaluation access, particularly in resource-constrained and after-hours settings
- Development and external validation of deep learning models for spontaneous preterm birth prediction from mid-trimester cervical ultrasound
Preterm birth is the leading cause of neonatal death. Despite sustained efforts to identify high-risk women in the mid-trimester, accurate prediction remains difficult. Quantitative cervical ultrasound texture has been proposed as a predictor of spontaneous preterm birth. However, earlier models were developed in small single-centre samples and were not externally validated. We developed image-texture (Local Binary Patterns with a Random Forest), deep-learning (Vision Transformer), clinical-variable, and multimodal models to predict spontaneous preterm birth on the prospective GARBH-Ini cohort. We then externally validated our best models on an independent cohort scanned on a different ultrasound machine. Our best overall model reached an internal-test area under the receiver-operating-characteristic curve of 0.71 (95% CI 0.60, 0.82), but performed modestly at 0.52 (95% CI 0.38, 0.64) externally. The deep-learning and multimodal models did not perform better. Discrimination appeared higher in a clinically high-risk subgroup at the 34-week threshold. These estimates were imprecise because of few cases and need to be confirmed in future studies. Among the several likely reasons for the modest external performance is the heterogeneity of preterm birth. Predicting distinct preterm-birth subtypes separately, and integrating additional biomarkers and data domains, might improve model performance. Keywords: preterm birth; cervical ultrasound; prediction model; external validation; deep learning
- A framework for human-artificial intelligence co-learning for disease activity labeling using electronic health records
Objective To develop and evaluate a framework for human-AI interaction. This approach, SHARE (Synergistic Human-Agent REasoning system) was designed to support scalable phenotyping of complex outcomes accurately, robustly and reproducibly from real-world electronic health record (EHR) data to support real-world evidence (RWE) generation. Methods and Analysis Using rheumatoid arthritis (RA) disease activity as the use-case, we studied a multi-institutional EHR-based RA cohort of 3,167 patients. Expert reviewers and a disease activity agent labeled notes using the same review guideline. The agent combined embedding-based informative-note filtering, structured evidence extraction, and evidence-based integrated reasoning to assign disease activity categories with supporting evidence, rationale, confidence, and ambiguity flags. To support scalable deployment, we evaluated a budget-tiered configuration using GPT-5 Nano for high-volume evidence extraction, o4-mini for final reasoning, benchmarking against a GPT-5.4 high reasoning effort configuration applied at every step. Note-level discrepancies were adjudicated by reviewers into final co-produced labels that were used to refine labels and inform agent development. The main outcome measure was the mean absolute error (MAE) of the initial and final agent vs the final co-produced labels. The agreement between agent- and reviewer-flagged ambiguous notes, per-note cost and compute time across configurations were also tested. Results Expert reviewers labeled 626 notes from 273 patients; human-AI adjudication revised 127 (20%) of these initial labels and added 60 newly labeled notes, yielding a 686-note co-produced reference. Against this reference, the final agent's accuracy improved from a mean absolute error of 0.406 to 0.291 with co-learning, and its ambiguity flag agreed with expert ambiguity designations with 92.1% accuracy. Applied across the cohort, the agent labeled 101,691 notes; the budget tiered configuration matched the accuracy of GPT-5.4 at high reasoning effort while reducing estimated cost by 69% and compute time by 70%. Conclusion Adopting a framework for human-AI co-learning, SHARE, improved the overall quality of gold-standard labels, identified ambiguous cases for further review, and supported accurate and standardized chart reviews of disease activity at a scale infeasible for manual review. SHARE's resource efficiency provides a transferable approach to incorporate complex phenotypes in RWE studies.
- Transient Apical Sparing in Hypertensive Heart Disease Explained by Laplace's Law
Background: Apical sparing of left ventricular longitudinal strain (LS) is an echocardiographic clue to cardiac amyloidosis but may also occur in hypertensive heart disease (HHD). Objectives: To determine whether apical sparing in HHD is associated with regional left ventricular wall stress estimated according to Laplace's law. Methods: We retrospectively studied 1,559 patients with HHD, 47 with light-chain cardiac amyloidosis (ALCA), and 409 normotensive controls. Artificial intelligence-assisted echocardiography quantified segmental LS, wall thickness, and cavity radius at the basal, midventricular, and apical levels. Wall stress was estimated as mean blood pressure (MBP) x radius/(2 x wall thickness). Apical sparing was defined as a relative regional strain ratio (RRSR)[≥]1.0. Results: Apical sparing was present in 14 patients with HHD (0.9%), 13 with ALCA (27.7%), and no controls. Among HHD patients with apical sparing, RRSR decreased from 1.11{+/-}0.13 to 0.72{+/-}0.10 after antihypertensive treatment (P<0.001), accompanied by reduced wall stress and improved basal and midventricular LS, with resolution of apical sparing in all 14 patients. In the overall HHD cohort, changes in MBP and left ventricular mass index were independently associated with changes in RRSR. In an exploratory analysis of HHD patients with apical sparing, a reduction in basal wall stress was associated with a reduction in RRSR ({beta}=0.267 for {bigtriangleup}RRSRx100, 95% CI 0.023-0.511; P=0.036). In ALCA, favorable hematologic response was the only determinant of RRSR reduction. Conclusions: Apical sparing in HHD was uncommon but reversible and may represent a load-sensitive deformation pattern associated with regional wall stress, consistent with Laplace's law.