Proponents of artificial intelligence (AI) technology have suggested that in the near future, AI software may replace human radiologists. Although assimilation of AI into the specialty has occurred more slowly than predicted, developments in machine learning, deep learning, and neural networks suggest that technologic hurdles and costs will eventually be overcome. However, beyond these technologic hurdles, formidable legal hurdles threaten the impact of AI on the specialty. Legal liability for errors committed by AI will influence the ultimate role of AI within radiology and also influence whether AI remains a simple decision support tool or develops into an autonomous member of the health care team. Additional areas of uncertainty include the potential application of products liability law to AI and the approach taken by the U.S. FDA in potentially classifying autonomous AI as a medical device. The current ambiguity of the legal treatment of AI will profoundly influence development of autonomous AI given that vendors, radiologists, and hospitals will be unable to reliably assess their liability associated with implementing such tools. Advocates of AI in radiology and health care in general need to lobby for legislative action to better clarify the liability risks of AI in a way that does not deter technologic development.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.2214/AJR.21.27224 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Digital Representation of Patients as Medical Digital Twins: Data-Centric Viewpoint.
JMIR Med Inform
January 2025
INSERM U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 30 Bd Jean Monnet, Nantes, 44093, France, 33 2 40 08 74 10.
Precision medicine involves a paradigm shift toward personalized data-driven clinical decisions. The concept of a medical "digital twin" has recently become popular to designate digital representations of patients as a support for a wide range of data science applications. However, the concept is ambiguous when it comes to practical implementations.
View Article and Find Full Text PDFAiGPro: a multi-tasks model for profiling of GPCRs for agonist and antagonist.
J Cheminform
January 2025
School of Systems Biomedical Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, 06978, Seoul, Republic of Korea.
G protein-coupled receptors (GPCRs) play vital roles in various physiological processes, making them attractive drug discovery targets. Meanwhile, deep learning techniques have revolutionized drug discovery by facilitating efficient tools for expediting the identification and optimization of ligands. However, existing models for the GPCRs often focus on single-target or a small subset of GPCRs or employ binary classification, constraining their applicability for high throughput virtual screening.
View Article and Find Full Text PDFAssociation of healthy sleep patterns with incident sepsis: a large population-based prospective cohort study.
Crit Care
January 2025
Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, China.
Background: The role that sleep patterns play in sepsis risk remains poorly understood.
Objectives: The objective was to evaluate the association between various sleep behaviours and the incidence of sepsis.
Methods: In this prospective cohort study, we analysed data from the UK Biobank (UKB).
Assessing the disconnect between student interest and education in artificial intelligence in medicine in Saudi Arabia.
BMC Med Educ
January 2025
Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.
Background: Although artificial intelligence (AI) has gained increasing attention for its potential future impact on clinical practice, medical education has struggled to stay ahead of the developing technology. The question of whether medical education is fully preparing trainees to adapt to potential changes from AI technology in clinical practice remains unanswered, and the influence of AI on medical students' career preferences remains unclear. Understanding the gap between students' interest in and knowledge of AI may help inform the medical curriculum structure.
View Article and Find Full Text PDFAI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios.
BMC Pregnancy Childbirth
January 2025
Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of Utah Health, 30 N. Mario Capecchi Dr., Level 5 South, Salt Lake City, UT, 84132, USA.
Background: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR.
Methods: Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!