Two built-in ethical checkpoints aligned with four core pillars of AI ethics and professional codes ensure that client resources and treatment plans produced with generative AI maintain safety, reliability and professionalism.
It stands out by integrating ethical oversight directly into both prompt design and output review, a feature often missing in general AI guidelines.
• Examining academic work for AI generated text.
A University of Pittsburgh researcher has developed a method, Turing Auditing, to accurately detect the presence of text produced by generative artificial intelligence (GenAI) in a work product.
Through the combination of proprietary forensic and investigative reading techniques alongside commercially available AI tools, Turing Auditing can be used to assess any written work product (e.
- Novel AI Application: The system uses a custom fine-tuned generative AI to both identify issues in machine learning models and initiate improvements, a feature that is considered completely novel.
This invention is a research tool that uses AI to monitor and improve machine learning models for predicting adverse events and preventing medication errors in health systems.
Its most significant advantage is a custom generative AI model that can identify issues in machine learning models and automatically initiate improvements.
NeuroSmart is an AI-driven clinical decision support system (CDSS) designed to optimize stroke management by providing personalized, real-time diagnostic and treatment recommendations.
js, allows clinicians to input patient data and receive AI-generated recommendations with confidence scores and visual analytics.
- Enhances clinical decision-making with AI-driven insights tailored to patient-specific factors.
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.
Developed using a bank of human and non-human-primate (NHP) eyes, this novel approach uses artificial intelligence (AI) to determine mechanical nonlinearity (a measure of intraocular pressure (IOP) dependent stiffness) using optical coherence tomography (OCT) images and can predict glaucoma progression.
An AI-based glaucoma prediction tool has been developed.
This novel AI-based tool will assess OCT images to identify eyes most susceptible to developing glaucoma through prediction of the retinal fiber layer (RNFL) thickness allowing for early intervention and personalized treatment strategies.
- Leverages emerging AI technologies to improve communication and translation of care seeker goals.
The first phase, "Proof of Concept," will focus on identifying how to determine care seeker goals using human-centered design and AI.
The Goalistic™ Health Platform is a paradigm-shifting project that uses AI software and human-centered design to help patients define their health goals and integrate those goals into clinical care.
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.
AI-based IsAb2.0 for antibody design.
University of Pittsburgh researchers have developed PCAP–Backdoor, software capable of attacking packet capture (PCAP) datasets through a “backdoor,” bypassing deep learning (DL)-based intrusion detection systems (IDS).
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.