Signals, in order.

This research aims to develop AI-based models that predict treatment engagement and outcomes for patients in digital psychotherapy. It will use a large, real-world dataset to train the algorithms and will incorporate responsible AI practices by describing, accounting for, and mitigating bias related to mental disturbance severity and other sensitive attributes such as gender, ethnicity, and socio-demographic status.

Source: ClinicalTrials.gov

This research project in the United States aims to develop machine learning models that predict patients’ BMI and complications following bariatric surgery, forming the CABS-Score. It also seeks to predict remission rates for diabetic (DM II) patients after surgery. The resulting models will be made publicly available through a web-based application.

Source: ClinicalTrials.gov

The study evaluates an AI algorithm designed to optimize mechanical ventilation settings in ICU patients, aiming to reduce organ damage by learning from deviations between automated recommendations and expert adjustments. It highlights that mechanical ventilation, while life-saving, can be harmful if not set correctly, and that current practices often fall short of guidelines. The research proposes that AI can improve patient outcomes by tailoring ventilator and extracorporeal lung support settings to individual clinical characteristics.

Source: ClinicalTrials.gov

The study evaluates the feasibility of a new method for self-recordable ECGs using a smartwatch coupled with an image processing algorithm. The long-term goal is to establish this method and potentially integrate it into telemedical care.

Source: ClinicalTrials.gov

Idiopathic pulmonary fibrosis, non‑specific interstitial pneumonia, and chronic obstructive pulmonary disease are severe, progressive, irreversible pulmonary disorders with modest therapeutic response and poor prognosis. Prompt and accurate diagnosis is essential to enable early care that can delay disease progression and prolong survival. Artificial intelligence–assisted digital lung auscultation and deep‑learning automation of lung ultrasound offer alternative, objective methods for earlier diagnosis of these conditions.

Source: ClinicalTrials.gov

The study aims to create a predictive modeling service for postoperative complications and prolonged hospital stay in patients with psychiatric diseases undergoing colorectal surgery. It also investigates how preoperative risk factors, psychiatric and psychosomatic diseases affect colorectal surgery outcomes and complications such as anastomosis insufficiency using predictive modeling techniques. The resulting service will be publicly available as a web-based application.

Source: ClinicalTrials.gov

This research examines whether women experience more changes in melanocytic nevi during and after pregnancy compared to age‑matched non‑pregnant women. It also evaluates the psychological impact of using total body mapping and dermoscopic examination supported by artificial intelligence during pregnancy.

Source: ClinicalTrials.gov

The PANIC study is a research project based in the United States that seeks to develop a machine‑learning application for accurately predicting which patients are at risk of anastomotic insufficiency following colon and colorectal surgery. The goal is to provide a preoperative risk assessment tool to improve surgical outcomes.

Source: ClinicalTrials.gov

The study analyzes driving behavior of individuals under the influence of alcohol using a validated research driving simulator. Based on the simulator’s driving variables, the investigators aim to establish algorithms capable of discriminating sober and drunk driving patterns using deep machine learning classifiers.

Source: ClinicalTrials.gov

This research compares 2D and 3D imaging techniques with routine clinical care for early melanoma detection in a prospective, large-scale real‑world dataset collected in Switzerland. The study evaluates the effectiveness of VECTRA imaging technology in improving diagnostic accuracy. Data were gathered from a real-world clinical setting across Switzerland.

Source: ClinicalTrials.gov