The rapid growth and decreasing cost of Next-generation sequencing (NGS) technologies have made it possible to conduct routine large panel genomic sequencing in many disease settings, especially in the oncology domain. Furthermore, it is now known that optimal disease management of patients depends on individualized cancer treatment guided by comprehensive molecular testing. However, translating results from molecular sequencing reports into actionable clinical insights remains a challenge to most clinicians. In this review, we discuss about some representative systems that leverage artificial intelligence (AI) to facilitate some processes of clinicians' decision making based upon molecular data, focusing on their application in precision oncology. Some limitations and pitfalls of the current application of AI in clinical decision making are also discussed.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8786281 | PMC |
| http://dx.doi.org/10.1042/ETLS20210220 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Investigation of the Relationship Between Intolerance of Uncertainty, Metacognitions, Maladaptive Coping, and Fear of Cancer Recurrence via Moderated Mediation Model.
Psychooncology
January 2025
Department of Psychology, Maltepe University, İstanbul, Turkey.
Objective: In recent years, many studies have investigated the triggers, perpetuating factors, and outcomes of Fear of Cancer Recurrence (FCR), highlighting its complexity with multiple dimensions that encompass both antecedents and consequences. In this sense, the cognitive approach to FCR has explored variables such as metacognition, maladaptive coping strategies, and intolerance of uncertainty (IU). On the other hand, the findings of a restricted number of studies investigating the relationship between FCR and stated variables appear to be inconsistent.
View Article and Find Full Text PDFCrosslinked Versus Non-Crosslinked Resorbable Collagen Membranes for Periodontal Regeneration: A Multicenter, Randomized, Double-Blind, Non-Inferiority Clinical Trial.
J Periodontal Res
January 2025
Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Aim: This study aimed to evaluate and compare the results of combination therapy involving bone grafting and two different resorbable collagen membranes in 1-, 2- and 3-wall infrabony defects.
Methods: A total of 174 patients with infrabony defects (≥ 7 mm periodontal probing depth) were randomized to receive deproteinized bovine bone mineral (DBBM) with either a native porcine non-crosslinked collagen membrane (N-CM, control, n = 87) or a novel porcine crosslinked collagen membrane (C-CM, test, n = 87). Clinical parameters, including periodontal probing depth (PPD), clinical attachment level (CAL), and gingival recession (GR), were recorded at baseline, 12 weeks, and 24 weeks.
Nutritional intelligence in the food system: Combining food, health, data and AI expertise.
Nutr Bull
January 2025
Queen's University Belfast, Belfast, UK.
Transformative change is needed across the food system to improve health and environmental outcomes. As food, nutrition, environmental and health data are generated beyond human scale, there is an opportunity for technological tools to support multifactorial, integrated, scalable approaches to address the complexities of dietary behaviour change. Responsible technology could act as a mechanistic conduit between research, policy, industry and society, enabling timely, informed decision making and action by all stakeholders across the food system.
View Article and Find Full Text PDFAddressing evidence needs during health crises in the province of Quebec (Canada): a proposed action plan for rapid evidence synthesis.
BMC Health Serv Res
January 2025
Department of School and Social Adaptation Studies, Faculty of Education, Université de Sherbrooke, Sherbrooke, Canada.
Background: The COVID-19 pandemic necessitated the rapid availability of evidence to respond in a timely manner to the needs of practice settings and decision-makers in health and social services. Now that the pandemic is over, it is time to put in place actions to improve the capacity of systems to meet knowledge needs in a situation of crisis. The main objective of this project was thus to develop an action plan for the rapid syntheses of evidence in times of health crisis in Quebec (Canada).
View Article and Find Full Text PDFDeveloping a decision support tool to predict delayed discharge from hospitals using machine learning.
BMC Health Serv Res
January 2025
Department of Industrial Engineering, Dalhousie University, PO Box 15000, Halifax, B3H 4R2, NS, Canada.
Background: The growing demand for healthcare services challenges patient flow management in health systems. Alternative Level of Care (ALC) patients who no longer need acute care yet face discharge barriers contribute to prolonged stays and hospital overcrowding. Predicting these patients at admission allows for better resource planning, reducing bottlenecks, and improving flow.
View Article and Find Full Text PDFWant 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!