Leveraging artificial intelligence to summarize abstracts in lay language for increasing research accessibility and transparency.

Objective: Returning aggregate study results is an important ethical responsibility to promote trust and inform decision making, but the practice of providing results to a lay audience is not widely adopted. Barriers include significant cost and time required to develop lay summaries and scarce infrastructure necessary for returning them to the public. Our study aims to generate, evaluate, and implement ChatGPT 4 lay summaries of scientific abstracts on a national clinical study recruitment platform, ResearchMatch, to facilitate timely and cost-effective return of study results at scale.

A multi-stage transfer learning strategy for diagnosing a class of rare laryngeal movement disorders.

Background: It remains hard to directly apply deep learning-based methods to assist diagnosing essential tremor of voice (ETV) and abductor and adductor spasmodic dysphonia (ABSD and ADSD). One of the main challenges is that, as a class of rare laryngeal movement disorders (LMDs), there are limited available databases to be investigated. Another worthy explored research question is which above sub-disorder benefits most from diagnosis based on sustained phonations.

Decoding phonation with artificial intelligence (DeP AI): Proof of concept.

Objective: Acoustic analysis of voice has the potential to expedite detection and diagnosis of voice disorders. Applying an image-based, neural-network approach to analyzing the acoustic signal may be an effective means for detecting and differentially diagnosing voice disorders. The purpose of this study is to provide a proof-of-concept that embedded data within human phonation can be accurately and efficiently decoded with deep learning neural network analysis to differentiate between normal and disordered voices.

Using Wayfinding Data to Understand Patient Travel Within a Medical Center.

Navigating through parking lots, public areas, and hallways is a stressful task for patients visiting large medical centers. Little is known about the patient experience from when they arrive at a medical center to when they check-in at their clinic. In a pilot study, we used requests for wayfinding directions from a mobile application to form a network of patient movement through the Vanderbilt University Medical Center (VUMC).

FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Data.

Secure and scalable data sharing is essential for collaborative clinical decision making. Conventional clinical data efforts are often siloed, however, which creates barriers to efficient information exchange and impedes effective treatment decision made for patients. This paper provides four contributions to the study of applying blockchain technology to clinical data sharing in the context of technical requirements defined in the "Shared Nationwide Interoperability Roadmap" from the (ONC).