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HealthEvidence-backed gain

Machine learning-based prediction of postoperative nausea and vomiting after spinal anesthesia: A retrospective observational study

Postoperative nausea and vomiting (PONV) is a frequent and serious complication after surgery. PONV also reduces patient satisfaction with surgery under spinal anesthesia and increases medical costs due to prolonged hospitalization. The purpose of this study is to apply artificial intelligence (AI) machine learning analysis to identify risk factors for PONV in patients undergoing surgery with spinal anesthesia. This retrospective study used artificial intelligence to analyze data of adult patients (aged ≥20 year…

PLOS One

Evidence-backed gain

Dynamic prediction of HIV-related incomplete immune reconstitution: A multicenter, large cohort study using advanced joint modeling

Both readings

Evaluating Artificial Intelligence Translation Tools for Language Equivalence of Oncology-Informed Consent Forms From English to Spanish

Both readings

AI as a Therapist, Companion, and Romantic Partner: Emerging Roles, Benefits, and Risks for Mental Health in Participatory Medicine

Evidence-backed gain

AI makes a feature film in 14 days: Higgsfield shares its $500,000 filmmaking process with creators

Higgsfield AI has released the full production pipeline behind its 95-minute AI-generated action film, allowing creators to access the prompts, character models and workflows used to make the movie. The startup says it completed the film in 14 days for $500,000.

Evidence-backed problem

Suno Lost To GEMA: Why The Ruling Should Worry AI Music Companies

A German court ruled AI music company Suno infringed copyright by using works represented by GEMA without permission and downloaded songs by stream-ripping

Evidence-backed gain

Game, set, Chat: how tennis players use AI to scout opponents and run their lives

Not so long ago, Emma Raducanu was on her phone when she found herself wondering what her comprehensive usage of ChatGPT said about her own character. “I use Chat a lot,” Raducanu says, laughing. “Every small thing I do it and I got this idea. So, you know how Spotify do a Spotify Wrapped? I asked ChatGPT to make me a Chat Wrapped, and it was giving me a rundown on my personality. “I was like: ‘It’s a little bit too accurate.’ It was very clear, very concise. No nonsense, straight into the question. I thought: ‘…

Both readings

Quality of AI-Generated Patient Education for Pre- and Post-Operative Tracheostomy Care

Objective To evaluate the accuracy, completeness, clarity, source transparency, and readability of leading AI chatbot responses to patient questions about tracheostomy and to determine whether AI tools can reliably support patient education where high-quality guidance is critical for safety. Study design Cross-sectional content analysis. Setting Virtual study environment using publicly accessible AI platforms, with expert evaluation conducted via Qualtrics-based distribution. Methods Twelve frequently asked ques…

Health

Development and external validation of a multimodal artificial intelligence mortality prediction model of critically ill patients using multicenter data

Background Early prediction of in-hospital mortality in critically ill patients can aid clinicians in optimizing treatment. The objective was to develop a multimodal deep learning model, using structured and unstructured clinical data, to predict in-hospital mortality risk among critically ill patients after their initial 24 hour intensive care unit (ICU) admission. Methods We used data from MIMIC-III, MIMIC-IV, eICU, and HiRID. A multimodal model was developed on the MIMIC datasets, featuring time series compon…

Health

The FERM guild: a differentially correlated microbial module drives hypertension via metabolic flux perturbations

Hypertension is a major risk factor for cardiovascular diseases, with changes in gut microbiota composition and function being closely associated with its onset and progression. However, the high inter-individual variability in gut microbiota complicates the identification of pathogenic mechanisms using traditional methods. In contrast, the smaller variability in gut microbial metabolites offers a more reliable and consistent basis for cross-individual comparisons. Parsimonious flux balance analysis (pFBA), inte…

Science

SolenopsisDetector: development of an automatic detection system for fire ants using computer vision and deep learning

Fire ants (Solenopsis spp. Westwood) pose a major ecological and economic threat, mainly due to the invasive potential of certain species. Current identification methods are highly dependent on taxonomic expertise, which can slow down decision-making. The development of an automated detection system could therefore support the identification process. We present SolenopsisDetector (SolenopD), an automated system for identifying Solenopsis ants using computer vision and deep learning. Following taxonomic practice,…

Health

Accuracy of Artificial Intelligence based chatbots in reporting jaw lesions from multimodal radiographic images: A cross-sectional study

Objectives The current study aimed to quantify the diagnostic accuracy of commonly utilized chatbots including Gemini, Copilot, Claude, and specialized architectures like Manus in the detection and differential diagnosis of various jaw lesions, while concurrently evaluating the clinical safety and fidelity of the information they provide. Materials and methods Cone beam computed tomography (CBCT) dataset from 97 patients presented with jaw lesions were collected and anonymized. Panoramic 2D views were reconstruc…

Health

Long-term prediction of epilepsy following traumatic brain injury among veterans using routine clinical data

Objective Despite elevated risk for epilepsy following traumatic brain injury (TBI), there are limited tools to assess epilepsy risk following TBI using routine clinical data. The objective of this study was to develop and validate a machine learning approach to predict the onset of posttraumatic epilepsy (PTE) over varying time horizons following TBI, using only routine clinical data collected up to the month of TBI documentation. Methods This retrospective longitudinal cohort study included post-9/11 US vetera…

Health

AI-Assisted Electrocardiogram Interpretation Improves ST-Elevation Myocardial Infarction Diagnostic Accuracy Among Advanced Practice Providers: A Prospective Randomized Crossover Study

Timely and accurate diagnosis of ST-elevation myocardial infarction (STEMI) is critical in military operational environments where evacuation may be delayed. Although artificial intelligence (AI) electrocardiogram (ECG) tools have demonstrated high diagnostic performance, their effectiveness among advanced practice providers (APPs) remains untested. This study evaluated whether AI-ECG interpretation by Queen of Hearts (QoH) AI software by PMcardio improves STEMI diagnostic accuracy, clinician confidence, and tim…

Health

Prognostic risk modeling based on integrated multi-omics analysis identifies CRY2 as a key regulator in tumor immunity and patient survival in colorectal cancer

Colorectal cancer (CRC) exhibits substantial metabolic heterogeneity. This study developed a robust prognostic signature integrating ferroptosis- and lipid metabolism-related genes to investigate the role of CRY2 in CRC progression. Transcriptomic and clinical data from the TCGA-COAD and GSE39582 cohorts were analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to identify disease-associated gene modules. A machine learning framework was subsequently applied to construct and optimize the…