• AI safety and efficacy assessments. As the integration of generative AI into healthcare advances, it is crucial to ensure the safety and efficacy of these models through rigorous human assessments.
Utilizing artificial intelligence (AI), SLAID analyzes source material, generates customized presentation outlines based on specified contexts, and evaluates recorded presentations for alignment with the outline and cognitive demand levels. SLAID is a software tool that leverages AI and Natural Language Processing (NLP) to assist users in creating and analyzing presentations. Key features include document upload/search, AI-powered document analysis, contextual presentation outline generation, an editable checklist, integrated recording tools, AI-driven presentation analysis, feedback and reporting, and secure video handling.
University of Pittsburgh research has developed Perepex, an AI-powered educational platform designed to facilitate video-based learning and assessment between teachers and students. By leveraging AI for real-time transcription, cognitive analysis, and personalized feedback, Perepex aims to improve educational outcomes and streamline the assessment process. Perepex is a software platform that utilizes AI to enhance video-based learning and assessment.
This approach uses artificial intelligence (AI) and machine learning (ML) digital twin (DT) concepts, aided by density functional theory (DFT) modelling to identify dopants, fabricate electrodes and other components with precision, control, and cost-efficiency compared to current slurry- or spray-based approaches. h-VSSCSLS/M uses AI, ML and DT approaches to produce higher energy and power density LELIB electrodes and entire ASSLIBs with precise thickness, porosity, grain size, composition and phase control.
University of Pittsburgh researcher has developed spatiAlytica, an AI-powered system designed for the seamless analysis and visualization of single-cell or spot-based spatial transcriptomics data.
University of Pittsburgh researchers are developing novel software, spatiAlytica, an AI-powered user-friendly system for analysis of spatial transcriptomics (ST) data. An AI-powered software, spatiAlytica, is being developed to assist biologists with analysis of ST data. spatiAlytica is designed to work as an AI informatician.
• AI safety and efficacy assessments. As the integration of generative AI into healthcare advances, it is crucial to ensure the safety and efficacy of these models through rigorous human assessments.
A University of Pittsburgh researcher is developing artificial intelligence (AI) software to transform computed tomographic pulmonary angiography (CTPA) into non-contrast CT (NCCT) scans. This software will then be used to train an AI algorithm to detect pulmonary embolisms (PE) on routinely acquired NCCT scans. This novel software and AI algorithm could substantially improve the detection of life-threatening PE, offering a more accessible, efficient, and potentially safer method of diagnosis.
University of Pittsburgh researchers have developed system techniques integrating artificial intelligence (AI) and smartphone sensing, namely PTEase, that can be used as a pulmonary telemedicine device by patients to accurately evaluate pulmonary disease conditions and provide clinically relevant information out of clinic. The development of PTEase and the associated mouthpiece uses acoustic signals transmitted from the smartphone and the associated detected reflections along with AI to accurately determine the CSA of a patient’s airway segments.
University of Pittsburgh researchers have designed a closed-loop multimodal artificial intelligence (AI) assisted neuromodulation device to treat heart failure (HF). Assisted by artificial intelligence, this device aims to restore healthy autonomic balance in patients with HF with a view to halting progression of HF and improving the outcomes and quality of life for patients within this currently unmet clinical need. AI based on feedback from biomarkers, including ECG and blood pressure will be acquired and analyzed in real time, and will personalize the SCS and VNS parameters to provide autonomic balance, thereby reducing the risk of worsening HF or fatal ventricular arrhythmias.
University of Pittsburgh researchers are developing a novel artificial intelligence (AI) assisted non-invasive diagnostic tool to diagnose the severity of deep vein thrombosis (DVT) and predict those most at risk of developing post thrombotic syndrome (PTS). This novel approach will use non-invasive analysis to measure mechanical changes in the vein wall after DVT and use AI to predict a patient’s risk of developing PTS. All mechanical and clinical information will be used to develop a machine learning model based on the data collected from patients to produce an AI tool capable of classifying the severity of DVT and PTS.
University of Pittsburgh researchers have developed system techniques integrating artificial intelligence (AI) and smartphone sensing, namely PTEase, that can be used as a pulmonary telemedicine device by patients to accurately evaluate pulmonary disease conditions and provide clinically relevant information out of clinic. The development of PTEase and the associated mouthpiece uses acoustic signals transmitted from the smartphone and the associated detected reflections along with AI to accurately determine the CSA of a patient’s airway segments.
Utilizing advances in artificial intelligence (AI) and machine learning (ML), it is possible to predict changes in aneurysms or cancerous tumors over time, allowing clinicians better understanding of the risks to patients and thereby taking interventions where required. This novel approach uses AI to predict future changes in growth shape and morphology, and to determine the historical changes allowing clinicians to accurately predict risk of rupture or metastasis.
It is increasingly used in patient management, clinical research, and the development of artificial intelligence (AI) tools to support clinical decision making. Novel software has been developed to collect data from existing EHR databases for use in Azure Healthcare API with the potential to improve healthcare efficiency, clinical research, and AI tool development using large existing databases. • AI tool development using Azure Healthcare API.
