Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Published in November 2019, this perspective review argues that breakthrough data collection in biology and medicine requires new analysis strategies. The authors contend that machine learning and multiscale modeling are complementary and demonstrate how their integration can produce physics-aware predictive models that handle massive, heterogeneous datasets.
On July 15, 2021, Nature published the AlphaFold study describing a redesigned neural network that predicts the three-dimensional structure a protein will adopt based solely on its amino acid sequence. The authors reported validation in CASP14, where the model regularly achieved atomic accuracy even when no homologous structure was available and performed competitively with experimental structures.
Published February 20, 2026, this peer-reviewed survey in Cognitive Computation reviews XAI-driven data mining for self-defending IoT systems. It describes how IoT expansion in smart cities, healthcare, and industrial automation creates need for real-time, scalable security, and how XAI methods aim to detect anomalies and support automated decisions with transparent reasoning.
In a four-month randomized trial reported April 30, 2026, 155 Chinese EFL students were split into a control group of 80 and an experimental group of 75 that received robot-assisted language instruction. Questionnaires at the start and end measured classroom engagement and willingness to attend classes.
On August 24, 2026, a commentary in the Journal of Veterinary Diagnostic Investigation argued that veterinary diagnostic laboratories should decide what AI outputs are permitted to do in service, not just how accurate models are. It defines service entry as the moment an output can influence triage, interpretation, draft reports, or result release, and proposes a standard operating procedure covering intended use, reviewer and signer roles, disclosure, input compatibility, refusal conditions, pathologist override, QC monitoring, and stop rules.
This umbrella review synthesized 27 systematic reviews with over 14 million participants to examine AI applications in mental health care between 2021 and 2025. It found AI improved early detection and risk stratification for depression, anxiety, stress, PTSD and suicidal ideation, and that chatbots and mobile platforms expanded access and engagement.
As of November 2025, this peer-reviewed review examines agentic AI in newsrooms that can autonomously plan, decide, and generate content. Analyzing 46 sources from 2015-2025, it finds current evaluations emphasize technical accuracy and efficiency while neglecting trust, governance, and collaboration.
Researchers used a Causal Forest causal machine learning model on a retrospective cohort of gastric cancer patients treated between 2007 and 2017 to estimate who benefits from adjuvant chemotherapy. The model identified lymph node ratio as the most dominant predictor of benefit, with a significant interaction at a 0.25 threshold in a propensity-matched Stage II-III cohort.
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