Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
On August 15, 2026, a peer-reviewed study reported a hybrid ensemble machine learning framework for assessing landscape ecological vulnerability to riverbank erosion. Using random forest, multilayer perceptron and bagging classifiers with multicollinearity-selected environmental and geomorphological parameters, the bagging ensemble achieved 0.97 AUC and 0.91 accuracy, mapping over half the area into high or very high vulnerability zones in Bihar and West Bengal.
By August 2026, researchers had used single-cell RNA sequencing of lung adenocarcinoma patients treated with neoadjuvant immunotherapy to map resistance-associated heterogeneity, identifying a malignant Cluster 2 enriched in non-responders with upregulated KRT17, S100A2, and CST6, and built a CoxBoost combined with survivalSVM prognostic model validated across seven independent cohorts.
This scoping review examined 41 empirical studies published from 2015 to March 2026 on AI for cancer symptom management in adult survivors. Twenty-one studies focused on model development using mainly unstructured electronic health record data, 18 on AI-enabled delivery using patient-reported inputs, and 2 on both, employing natural language processing, machine learning, and conversational AI for detection, monitoring, triage, decision support, personalised management, education and counselling.
Researchers applied an explainable AI framework to four years of routine air-quality and meteorological data from a single urban monitoring station to move beyond concentration-only analysis of PM10. The best ensemble model reached R2=0.913, and SHAP-based clustering revealed ten recurrent environmental settings linked to enhancement, reduction, or transitional PM10 behavior.
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.
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.
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.
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.
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