Clinical Research Directory

The Impact of Artificial Intelligence Electrocardiography on Occlusion Myocardial Infarction Management Under the Value-Based Payment System

This trial will prospectively evaluate the impact of integrating AI-ECG within the pay-for-performance program on improving the diagnosis, treatment, and clinical outcomes of occlusion myocardial infarction patients by promoting accurate and timely diagnoses through financial incentives.

Comparing Original Patient Educational Materials vs. AI-Simplified Materials to Improve Patient Comprehension and Health Literacy

Poor health literacy and patient comprehension have been associated with adverse health outcomes. Patient educational materials (PEMs) are articles that are intended to assist patients in their understanding of a given medical condition. Given that the average American adult reads at the 8th grade level, the American Medical Association and the Center for Disease Control recommend PEM be written at the 6th grade level. However, literature has found the majority of PEMs to be written significantly higher than the 8th grade level. In order to improve their readability, a number of studies have displayed the effectiveness of large language models (LLMs) such as ChatGPT to simplify the text of a given PEM. Despite the improvement in readability, the effectiveness of these simplified PEMs on improving patient comprehension of the AI augmented material has yet to be investigated. The purpose of our study is to test whether the improvement in readability found in AI-simplified PEMs corresponds to a greater understanding of the material compared to the original PEM. Understanding if AI-simplified PEM truly improves comprehension could further support this use case for AI and aid providers and healthcare organizations in improving the health literacy of their patients. This study aims to answer the following question: Do AI simplified PEMs improve the comprehension of pediatric orthopaedic conditions? Researchers will compare AI-simplified PEMs to their original, unmodified counterparts in order to see if there is any difference in post reading comprehension of the participants. Participation in the study will include: * A brief baseline survey (e.g. demographics and educational attainment) * A randomly assigned reading of either the original PEM or the AI simplified version. * A 10 question post-reading multiple choice quiz

Screening Mammography: Single Reading by One Radiologist With AI vs. Double Reading by Two Radiologists (AI-BCSQ)

A randomized prospective study comparing the evaluation of mammography images in a breast cancer screening programme by a single radiologist with AI support versus standard double reading by two radiologists without AI support.

Application Evaluation Research on the Artificial Intelligence-assisted Support System for the Diagnosis of Colorectal Tubular Adenoma Lesions

This study is a prospective,multi-center and observational clinical study.Investigators would like to innovatively construct a "trinity" database of colorectal tubular adenomas based on white light - magnifying chromo - pathological images.It simulates the decision - making logic of doctors, and based on the multimodal endoscopic LAFEQ method previously proposed, develop a multimodal deep - learning diagnostic model for colon adenomas and an interpretable risk prediction model for intestinal adenomas. While achieving high - precision auxiliary treatment decisions, clearly present the decision - making basis, and break through the limitation of poor interpretability of previous medical imaging AI models.

Lymphoedema Diagnosis and Treatment

A domain-specific, custom-trained large language model for the differential diagnosis and treatment planning of lymphedema, lipedema, and venous insufficiency.

Behavioral Parent Training With and Without AI Support for Children With Disruptive Behaviors

The goal of this clinical trial is to learn if adding an artificial intelligence (AI) application called to standard Behavioral Parent Training (BPT) helps families with children who have disruptive behavior problems. It will also help researchers understand if the app is easy to use and helpful for parents. The main question it aims to answer is: \- Is it feasible and acceptable for parents to use the AI app alongside their therapy sessions? The secondary questions it aims to answer are: * Does the app help reduce children's disruptive behaviors and irritability more than therapy alone? * Does using the app help lower stress, anxiety, and depression levels for the parents? Researchers will compare: 1. Standard BPT: Parents receive 8 weekly group training sessions (online). 2. BPT plus ParenteAI: Parents receive the same 8 weekly sessions plus 24/7 access to an AI virtual assistant for personalized support. Participants will: * Attend 8 weekly group training sessions. * Complete surveys about their child's behavior and their own well-being at baseline, after group training sessions 4 and 8, and 3 and 6 months after finalizing the group training. * If in the experimental group, use the ParenteAI app to get real-time coaching and support for managing their child's behavior at home. * Provide feedback on their experience and satisfaction with the program.

Developing a Multimodal Cancer Pain Database to Support AI-Based Automatic Pain Assessment

The goal of this observational study is to collect short video and sound recordings of people with cancer to create a secure database that can be used in future research to develop an artificial intelligence (AI) tool for pain assessment. The main aim is to build a large, high-quality collection of audiovisual data showing how people with cancer express themselves when they do and do not have pain. Participants will include adults with cancer who are admitted to the oncology ward for pain treatment and a control group admitted for chemotherapy who have no pain. After giving consent, participants will: * Be recorded on video (from the shoulders up) for up to 60 seconds while reading a short sentence and describing their pain or daily experience. * Complete a short questionnaire about their mood and pain expression. * Allow researchers to collect some information from their medical record, such as their pain score, medications, and cancer type. These recordings will be securely stored and used to create a database for future AI research. No medical tests, new treatments, or extra hospital visits are involved. This study will provide the foundation for developing future AI-based tools that could support doctors and patients in monitoring and managing pain more accurately and easily.

AI-Assisted Analgesia Copilot System

The primary objective of the SEASCAPE project is to design, develop, and to apply a clinical implementation tool of a machine learning (ML) and artificial intelligence (AI)-based co-pilot system for the real-time monitoring and control of nociception during general anesthesia (GA). The ultimate clinical purpose is to optimize individualized pain management by achieving precise titration of intravenous opioids (specifically remifentanil), thereby minimizing the incidence of over- and under-dosing. This optimization is projected to enhance patient outcomes, reduce opioid-related complications, and improve overall cost-effectiveness of anesthetic procedures. The main scientific question guiding this work is: Can a novel algorithm be generated and validated to provide superior analytical precision for analgesic management by reliably differentiating genuine nociceptive responses from confounding physiological variables-such as inadequate neuromuscular blockade or changes in depth of anesthesia-thereby significantly improving the clinical decision-making framework for intraoperative nociception control? This project addresses the recognized challenge in anesthesiology: defining an objective measure to quantify nociception and antinociception during GA. Study Population: Patients scheduled for elective surgical procedures requiring general anesthesia (GA). Existing Intervention: The standard anesthetic regimen includes continuous intravenous infusion of the remifentanil for intraoperative analgesia, typically governed by a Target Controlled Infusion (TCI) system utilizing a pharmacokinetic/pharmacodynamic (PK/PD) model (Eleveld TCI model). Project Focus: The research seeks to improve the accuracy and efficacy of this existing analgesic strategy by integrating a multivariate patient data stream with the newly developed SEASCAPE co-pilot AI. This aims to refine the remifentanil dose predictions beyond the current TCI model's capabilities, personalized system.

Effect of Guided Imagery and AI-Assisted Video on Pain During Uterine Involution Assessment in Postpartum Women: A Randomized Controlled Trial

Uterine involution, the process by which the uterus returns to its pre-pregnancy size during the postpartum period, can cause severe cramp-like pain in postpartum women. Although pharmacological methods are common for managing this pain in the literature, their side effects limit their use in this population. This project aims to fill a significant gap in the literature by combining guided imagery, a non-pharmacological technique, with evolving artificial intelligence (AI) technologies. While the effectiveness of guided imagery in cesarean and labor pain is well-known, its impact on pain and physiological parameters during uterine involution-specifically through AI-supported visualization-will be evaluated for the first time within the scope of this study. The primary research question of the project is: "Do guided imagery and AI-supported video applications have a healing effect on the pain levels and vital signs of postpartum women during the evaluation of uterine involution?" This research is a three-arm, randomized controlled experimental study to be conducted at Istanbul Atlas University Hospital. The sample size of the study was determined using the G\*Power 3.1 program. As a result of the a priori power analysis performed by selecting the "ANOVA: Fixed effects, omnibus, one-way" statistical test under the "F tests" family, the significance level was set at α = 0.05, the statistical power (1-β) at 0.80, and the effect size (f) at 0.25 (medium effect). The analysis indicated a total sample size of 159 participants for the three groups, with 53 participants required in each group. To account for potential sample attrition, a 10% addition was made to each group, resulting in a planned study with 59 participants per group and a total of 177 participants. "Guided Imagery" and "AI-Supported Video" applications will be utilized as intervention methods. In the guided imagery group, an expert-approved original audio script titled "The Emotional Journey of Mother and Baby" will be played to the women via over-ear headphones during the uterine involution assessment. In the AI-supported video group, a video generated with AI prompts based on the same script will be shown using virtual reality (VR) goggles during the assessment. This will allow for a comparative analysis of the effects of visual and auditory stimuli on pain during the uterine involution evaluation. Participants' pain levels will be measured using the VAS scale, and physiological data (pulse, blood pressure, SpO₂) will be recorded by monitoring at the 60th, 120th, and 180th seconds of the involution process. Considering the moderating effect of anxiety on pain threshold and vital signs, the 'Postpartum Specific Anxiety Scale' will be administered to all participants before the intervention. Data will be analyzed using ANOVA and Kruskal-Wallis tests in the SPSS 23 program.

Research on Construction and Verification of Multimodal Medical Imaging Large Model

With the accumulation of multimodal clinical data such as medical imaging and electronic health records (EHRs), efficient utilization of multi-source information to achieve precise diagnosis and intelligent decision-making has become a core direction of medical artificial intelligence (AI). Although traditional unimodal algorithms have yielded outcomes in specific tasks, their inability to model the semantic correlations among imaging, textual, and laboratory data leads to insufficient stability and limited interpretability of diagnostic results, making it difficult to meet the needs of comprehensive decision-making in complex clinical scenarios. In recent years, multimodal large models have demonstrated excellent cross-modal understanding and knowledge transfer capabilities in natural images and general vision-language tasks, providing a new paradigm for medical AI. However, direct application in medical scenarios still faces challenges: first, the medical semantic system differs significantly from general language models, hindering the accurate representation of disease characteristics and imaging details; second, the complex morphology of lesions and uneven sample distribution in medical data increase the difficulty of model generalization; third, clinical data involves privacy, so data security and ethical compliance serve as prerequisites for research. The research on medical multimodal large models aims to integrate multi-source heterogeneous medical data, establish a unified semantic representation and reasoning mechanism, and realize full-process intelligent analysis including disease identification and lesion localization. This approach can not only improve the efficiency and accuracy of clinical diagnosis but also provide clinicians with interpretable and traceable auxiliary decision support, boasting broad application prospects. Based on the hospital's clinical data resources and the research team's algorithmic foundation, this study intends to construct a multimodal large model system for medical imaging diagnosis, enabling closed-loop intelligent analysis from multimodal information fusion to diagnostic report generation. The research will strictly adhere to medical ethical standards, protect patients' right to information, right to privacy, and data security. Before the official launch of the project, ethical review must be passed, and relevant regulations shall be followed to ensure the unity of scientific research and ethics, laying a compliant foundation for subsequent clinical validation and promotion.

throMboembolic Risk Associated To High atrIal Fibrillation riSk

Cardiovascular diseases are the leading cause of mortality from treatable conditions in the European Union and the second from preventable causes, with a standardized mortality rate of 257.8 deaths per 100,000 inhabitants. In 2022, more than 1.11 million deaths in individuals under 75 years could have been avoided. Atrial fibrillation (AF) and major adverse cardiovascular events (MACE) are highly prevalent in the elderly and generate substantial healthcare costs. AF significantly increases the risk of MACE and is projected to rise markedly in the coming decades. In Europe, AF prevalence is expected to increase 2.5-fold over the next 50 years, with a lifetime risk of 1 in 3-5 individuals after age 55. AF-related strokes are projected to increase by 34%, and ischemic strokes in individuals over 80 are expected to triple between 2016 and 2060. Additionally, a 27% increase is anticipated among stroke survivors who subsequently develop AF or related conditions. AF substantially impacts morbidity, mortality, and disease progression, and early detection and treatment are crucial to prevent severe outcomes. European action plans (2018-2030) and the 2024 ESC/ESO guidelines emphasize early detection and management of AF in primary care. Although several AF prediction models exist, their integration into clinical practice remains challenging. AF represents a clinical continuum, with thrombotic risk present even before arrhythmia onset. High-risk patients for AF also show a high incidence of MACE, defined as a composite of myocardial infarction, stroke, systemic embolic events, and cardiovascular death. The proposed strategy involves developing and clinically validating an Artificial Intelligence (AI) model to improve early thrombotic risk prediction in patients at high risk of AF, using MACE as the primary outcome. This model aims to outperform the traditional CHA₂DS₂-VASc score by incorporating both classical and emerging clinical factors. The estimated timeline from clinical validation to commercialization is approximately 48 months. AI-based prediction is expected to enable personalized treatment, reduce the incidence of MACE, hospitalizations, and disability, and improve cost-effectiveness, ultimately decreasing the social and economic burden of AF and stroke in Europe.

Artificial Intelligence - Assisted Model for Optimal Timing of Surgery in Advanced Ovarian Cancer

This study integrates data from the randomized controlled SUNNY trial (RCT) and real-world (RWD) data, and employs multimodal data fitting to construct a medical artificial intelligence model to identify the clinical characteristics of patient subgroups suitable for primary debulking surgery (PDS) or interval debulking surgery (IDS), and the cutoff values for selecting different timings of surgery for advanced ovarian cancer.

Self-administered Remote Neurological Examination Using Mobile Application in Patients With Brain Tumors

The goal of this observational study is to evaluate the feasibility and accuracy of a self-administered remote neurological examination using the "Iskhaa" mobile application in patients with brain tumors aged above 5 years who are able to follow app-based instructions. The main questions it aims to answer are: 1. Development of a mobile application equipped with symptom assessment and recording videos as patients perform specific neurological tasks. 2. Development and validation of the AI model to detect functional changes and predict subsequent neurological deterioration. Participants will: 1. Use the Iskhaa mobile application to perform guided self-neurological examinations following pre-recorded video instructions. 2. Complete EORTC QLQ-C30 and BN20 questionnaires for quality of life assessment. 3. Record and upload videos (e.g., speech, walking, limb movements) using their mobile camera for analysis. 4. In Phase 1 (onsite), 100 participants will use the app under supervision to ensure usability and accuracy. 5. In Phase 2 (offsite), 500 participants will use the app independently at home for monthly self-assessments, with reminders and follow-up support. 6. Continue routine clinic visits every 3-6 months and imaging every 6-12 months as per standard clinical care. The study will compare app-recorded data with physician assessments to determine agreement and validity of remote neurological monitoring using artificial intelligence analysis.

A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension

This study aims to validate the use of an artificial intelligence-enabled electrocardiogram (AI-ECG) to screen for elevated PAP. We hypothesize that the AI-ECG model can early identify patients with pulmonary hypertension in high-risk patients, prompting further evaluation through echocardiography, potentially resulting in improving cardiovascular outcomes.

AI-Driven Model Impact on Patient Engagement in Medically Assisted Reproduction

Infertility is a globally significant medical condition, profoundly impacting individuals and couples both emotionally and physically. The multifaceted nature of in vitro fertilization (IVF) treatment demands active patient participation, with engagement playing a pivotal role in treatment success and satisfaction. However, suboptimal engagement can lead to challenges such as not initiating treatment, missed appointments, medication errors, dropping out and heightened stress levels, all of which may adversely affect clinical outcomes. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized healthcare, offering innovative solutions for personalized patient care. In IVF, AI-ML models hold the potential to enhance patient engagement by delivering tailored communication, reminders, and educational support, but also improved prognostication by providing personalized and accurate predictions of treatment outcomes. These capabilities enable patients to make more informed decisions and enhance their adherence to treatment protocols.This protocol outlines a prospective evaluation of an AI-ML model, specifically the Univfy PreIVF report, developed to improve patient engagement in IVF care. Recently, a retrospective, multicenter study reported improved IVF utilization rates among patients counselled using the Univfy PreIVF Report. The current study will prospectively assess the model's effectiveness in addressing individual patient needs and creating a supportive treatment environment. Specifically, this study will measure adherence to providers' recommendation of treatment protocols. By analyzing the impact of these interventions, this research aims to provide robust evidence for the integration of AI-ML technologies in reproductive medicine, paving the way for broader implementation and improved patient outcomes.

Development and Pre-validation of a Machine Learning-based Prediction Algorithm for Early Functional Recovery in Patients Undergoing Hip and Knee Replacement Surgery

The goal of this observational study is to develop and pre-validate a machine learning algorithm to predict early recovery of mobility in patients undergoing hip or knee joint replacement surgery. The primary research question is: Can a machine learning model accurately classify patients with faster versus slower recovery of autonomous mobility in the first days after joint replacement surgery? Patients who have undergone elective hip or knee arthroplasty and received post-operative physiotherapy will have their clinical and perioperative data collected retrospectively (2020-2023) and prospectively (March 2026-December 2027). The algorithm will be trained on retrospective data and tested prospectively to evaluate its predictive performance for early mobilization and length of hospital stay.

Impact of AI-Based Research Training on Nursing Students

The goal of this clinical trial is to find out whether an artificial intelligence (AI)-powered research training course can improve nursing students' research skills, attitudes toward artificial intelligence, and readiness to use AI in research and education. The main questions this study aims to answer are: Does AI-powered research training improve nursing students' understanding of research methods? Does this training improve nursing students' attitudes toward artificial intelligence? Does the course increase nursing students' readiness and confidence to use artificial intelligence in research-related activities? Researchers will compare nursing students who take an AI-powered research training course with students who receive usual education without AI-based training. Participants will: Be randomly assigned to either the AI-powered research training group or the usual education group Complete online questionnaires about research skills, attitudes toward artificial intelligence, and readiness to use AI Attend assessments at three time points: before the course, immediately after the course, and three months later The AI-powered research training course includes structured sessions on research methods and the responsible use of artificial intelligence tools for literature review, research design, data analysis support, and academic writing. The results of this study may help improve research education and support the safe and effective use of artificial intelligence in nursing education and research.

AI-Generated Video Feedback to Improve Technical Skills in Coronary Artery Bypass Grafting

This study aims to evaluate whether targeted video feedback generated by an artificial intelligence (AI)-based surgical performance assessment model can support improvement in technical skills among cardiac surgeons performing coronary artery bypass grafting (CABG). This is a single-group, self-controlled, pre-post interventional study. Participating surgeons will submit a baseline CABG surgical video, which will be assessed by both an AI model and blinded human expert raters using standardized scoring criteria. Based on the AI assessment, surgeons will receive personalized video feedback highlighting operative steps associated with lower technical performance. After a one-month self-directed review period, a follow-up CABG surgical video will be submitted and evaluated using the same process. Changes in human-rated technical skill scores between baseline and follow-up will be used to assess the potential educational impact of AI-generated video feedback.

The Effect of Artificial-Intelligence-Assisted Videos on Breastfeeding Self-Efficacy, Motivation, and LATCH Scores in First-Time Mothers

As of 2024, nearly half (48%) of infants under six months worldwide are exclusively breastfed, approaching the global target of 50%. Building on this progress, the World Health Organization has extended the target to 60% by 2030, emphasizing the need for innovative, scalable, and supportive interventions to strengthen breastfeeding practices. Breastfeeding has well-established benefits for infant growth, immunity, and long-term health, while also reducing maternal postpartum complications and chronic disease risks. Early postpartum support, particularly within the first hours after birth, is critical for successful and sustained breastfeeding. However, in busy clinical settings, providing continuous and individualized support can be challenging, especially for primiparous women who may experience low confidence, pain, and insufficient guidance. This randomized controlled trial aims to evaluate the effect of an artificial intelligence (AI)-supported relaxing breastfeeding video on breastfeeding self-efficacy, breastfeeding motivation, and LATCH scores among primiparous women. Unlike instructional videos, the AI-based video is designed to promote emotional relaxation, instinctive breastfeeding perception, and maternal confidence during the early postpartum period. The study adopts a two-arm randomized controlled experimental design. The population consists of primiparous women who deliver vaginally at Ağrı Training and Research Hospital postpartum unit between February and June 2026. A priori power analysis (α=0.05, power=0.95) indicated a minimum sample size of 38 participants; considering a 20% attrition rate, a total of 46 women (23 per group) will be recruited. Eligible participants include primiparous, Turkish-speaking women without postpartum or neonatal complications. Women who undergo cesarean delivery, have medical or psychiatric conditions preventing breastfeeding, or whose newborns require intensive care will be excluded. Participants will be randomized into intervention and control groups using an online randomization tool. All participants will receive a standardized 5-minute breastfeeding education based on the Turkish Ministry of Health breastfeeding counseling guidelines. In addition to standard care, the intervention group will watch a 10-minute AI-supported relaxing video at the 2nd and 6th postpartum hours during breastfeeding. The video will be displayed via tablet while the mother is in a comfortable breastfeeding position. The control group will receive standard care only. The AI-generated video will be produced using Kling AI, a generative video platform that enables controlled text-to-video workflows. To ensure ethical and cultural sensitivity, the video will not include real human or animal breastfeeding images. Instead, it will feature abstract, metaphorical visuals (e.g., pastel silhouettes, minimalist line art, or flat illustrations) that convey calmness, bonding, rhythm, and instinctive closeness. The final version will be selected following expert review and pilot testing with three postpartum women. Low-level white noise (\<60 dB) will accompany the video to enhance maternal relaxation and infant comfort. Data collection tools include a demographic information form, the Breastfeeding Self-Efficacy Scale-Short Form, the Primipara Breastfeeding Motivation Scale, and the LATCH Breastfeeding Assessment Tool. Breastfeeding observations and LATCH scoring will be conducted by an independent midwife blinded to group allocation. Statistical analyses will include descriptive statistics, paired and between-group comparisons, and repeated-measures analyses where appropriate. Ethical approval will be obtained from the relevant institutional ethics committee, and written informed consent will be secured from all participants. The findings are expected to contribute novel evidence on the role of AI-supported emotional and relaxing digital interventions in enhancing early postpartum breastfeeding outcomes and maternal confidence.

AI-Supported Empathy Mapping to Enhance Communication and Grit in Pediatric Nursing Students

The nurse-patient communication environment in pediatric care is characterized by high uncertainty and complexity. Due to children's limited language development and emotional regulation abilities, coupled with parents' high level of involvement, nursing students often experience anxiety, lack of confidence, and avoidance behaviors, which negatively affect their clinical learning outcomes and the establishment of therapeutic relationships. Therefore, providing effective communication support strategies is essential in pediatric nursing education. This study aims to implement an instructional scaffolding model using artificial intelligence (AI)-generated empathy maps to enhance the communication skills, empathy performance, and grit of nursing students during pediatric clinical practicums when encountering communication challenges. A mixed-methods research design was adopted, and the participants were third-year nursing students enrolled in a pediatric nursing practicum course. The teaching intervention included AI-assisted generation of age-appropriate communication strategies, the construction of a grit-oriented empathy map, small group scenario-based exercises, and the application of learned strategies in clinical settings. Quantitative data were collected using pre- and post-intervention assessments, including an empathy scale, a communication skills scale, and a grit scale, to evaluate changes in learning outcomes. Qualitative data, including reflective journals, clinical observations, and focus group interviews, were analyzed to explore students' learning processes and strategy adaptations. Triangulation was applied to strengthen the validity of the findings. It is anticipated that this teaching model will enhance students' understanding of pediatric patients' emotional needs, strengthen their communication strategy application and clinical interaction quality, and promote persistence and adaptability in challenging situations. Through evidence-based teaching practice, this study is expected to provide a feasible and scalable innovative instructional model that supports the effective integration of AI into clinical nursing education, thereby improving pediatric nursing competence and the quality of care for children.

AI Ethical Assessment in Scientific Resreach

Artificial Intelligence (AI) is transforming scientific research by enabling computers to perform tasks that require human intelligence - such as data analysis, pattern recognition, prediction, and language processing. AI is now integrated into every stage of the research process: from idea generation and literature review to data collection, analysis, and publication. Using tools like machine learning, deep learning, and natural language processing (NLP), researchers can analyze massive datasets, detect hidden trends, and accelerate discoveries across fields like medicine, biology, chemistry, and social sciences.

Study of Predicting Lymph Node Metastasis of High-risk Prostate Cancer by Artificial Intelligence Multi-omics Analysis

The pathological-omics and imaging-omics in this study are combined to construct an artificial intelligence (AI) model that can predict whether high-risk prostate cancer patients may have lymph node metastasis. The model determines whether the patient has lymph node metastasis based on the MRI results and the pathological section image information of the case combined with clinical data before radical resection of the prostate. This study is a multicenter, prospective clinical study to verify the model's ability to predict whether high-risk prostate cancer patients may have lymph node metastasis.

Using Artificial Intelligence to Guide Fluid Therapy During Major Cancer Surgery: A Randomized Controlled Trial

The goal of this clinical trial is to learn if using artificial intelligence to guide intravenous fluid therapy during major cancer surgery can help keep blood pressure more stable compared with usual care in adult patients undergoing major cancer surgery. The main questions it aims to answer are: * Does artificial intelligence-guided fluid therapy reduce hypotensive events during surgery? * Does this approach improve recovery and reduce complications after major cancer surgery? Researchers will compare artificial intelligence-guided fluid therapy with standard fluid management to see if the artificial intelligence-guided approach provides better support during surgery. Participants will: * Undergo major cancer surgery under general anesthesia * Receive either artificial intelligence-guided fluid management or standard fluid management during surgery * Be monitored during and after surgery as part of routine clinical care * Be followed after surgery to assess recovery and possible complications

Structured Handoff Using Intelligent Framework for Transitions Trial

Inpatient general medicine attendings will be randomized to have an LLM feature turned on to provide a draft of an off-service handoff within Carelign (an EHR-adjacent provider communication tool). Providers who have access to this feature will be clearly instructed that if they use the LLM-generated draft, they must review and edit it as necessary before finalizing. The study will assess measures of documentation burden (as it relates to writing handoff) - including time spent writing handoff - and work exhaustion in both intervention and control groups.