Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
In a retrospective study of 102 patients at a tertiary care center in India, researchers used k-means clustering on eight biopsychosocial baseline variables to derive three AUD profiles. By the August 2026 publication date, they reported Late-Onset, High-Functioning, and Severe groups with differing 3-month abstinence rates corroborated by GGT levels and bootstrap-assessed cluster stability.
Researchers developed and validated machine learning models to predict posttraumatic epilepsy onset at 2, 5, and 10 years after first TBI documentation in 107,987 post-9/11 US veterans, using only routine preinjury clinical data up to the month of injury. An optimized random forest achieved AUCs of 0.75 to 0.73 across horizons on held-out test data and enabled high-risk stratification.
Researchers compared two ways to allocate scarce Medicaid care-management phone outreach each month for 164,063 beneficiaries in Washington and Virginia. Using a causal forest to estimate individualized treatment effects, they found targeting the top decile by predicted effect prevented 13.3 acute events per 2000 members per month, compared with 2.5 events under conventional top-decile risk targeting.
By August 2025, a scoping review of 73 studies published between 2015 and 2024 examined AI in sports biomechanics, focusing on wearable technology, motion analysis, and injury prevention. It reported that convolutional neural networks reached 94% agreement with experts, computer vision was within 15 mm of marker-based systems, and integrated AI systems were associated with a 23% reduction in reinjury rates.
Researchers examined why fashion designers adopt Artificial Intelligence Generated Content, which is described as increasingly used in creative design. Using the Stimulus-Organism-Response framework combined with Self-Determination Theory, they surveyed 318 Chinese fashion-design practitioners and analyzed 21 items with PLS-SEM to link perceived risk, social influence and facilitating conditions to autonomy, competence, relatedness and behavioral intention.
A peer-reviewed survey published December 26, 2023 reviewed literature on fairness and bias in AI, focusing on sources such as data, algorithm, and human decision biases and the emerging issue of generative AI bias in synthetic media across healthcare, employment, criminal justice, and credit scoring.
On 2026-08-16, a peer-reviewed paper in Discover Artificial Intelligence described rising energy use and emissions from growing deep learning workloads in contemporary data centres and presented EcoSchedAI, a carbon-aware job scheduling framework intended to bring carbon awareness into actual machine learning operational processes.
A faculty author describes personalizing a written assignment to reduce students copying and pasting from generative AI outputs, then analyzes 81 paper grades from the course to assess impact.
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