Publications by authors named "J Mark Eddy"
Article Synopsis
- Biomedical research is increasingly integrating artificial intelligence (AI) and machine learning (ML) to tackle complex challenges, necessitating a focus on ethical and explainable AI (XAI) due to the complexities of deep learning methods.
- The NIH's Bridge2AI program is working on creating new flagship datasets aimed at enhancing AI/ML applications in biomedicine while establishing best practices, tools, standards, and criteria for assessing the data's AI readiness, including legal and ethical considerations.
- The article outlines foundational criteria developed by the NIH Bridge2AI Standards Working Group to ensure the scientific rigor and ethical use of AI in biomedical research, emphasizing the need for ongoing adaptation as the field evolves.
Background: Considering the lack of successful treatment options and poor prognosis for cirrhosis and cirrhosis-induced HCC, new platforms to investigate antifibrotic therapies are urgently needed. Precision-cut liver slice (PCLS) is a powerful ex vivo culture model that can supplement and potentially replace the traditional models.
Methods: PCLS were prepared from 4 different murine cirrhotic models (choline-deficient, l-amino acid-defined, high-fat diet, thioacetamide, diethylnitrosamine, and carbon tetrachloride) and compared with in vivo murine experiments, in vitro hepatic stellate cells, and human cirrhotic PCLS.
Nontuberculous mycobacterial pulmonary disease (NTM-PD) is increasing in incidence globally and challenging to manage. The 2020 multisociety treatment guideline and the 2022 consensus recommendations provide comprehensive evidence-based guides to manage pulmonary diseases caused by the most common NTM. However, with >190 different NTM species that may require different multidrug regimens for treatment, the breadth and complexity of NTM-PD remain daunting for both patients and clinicians.
View Article and Find Full Text PDFMotivation: Software is vital for the advancement of biology and medicine. Impact evaluations of scientific software have primarily emphasized traditional citation metrics of associated papers, despite these metrics inadequately capturing the dynamic picture of impact and despite challenges with improper citation.
Results: To understand how software developers evaluate their tools, we conducted a survey of participants in the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI).