AIMC Topic: Biomedical Research
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Artificial Intelligence in Health Care: A Rallying Cry for Critical Clinical Research and Ethical Thinking.
Clinical oncology (Royal College of Radiologists (Great Britain))
May 1, 2025
Artificial intelligence (AI) will impact a large proportion of jobs in the short to medium term, especially in the developed countries. The consequences will be felt across many sectors including health care, a critical sector for implementation of A...
The role of artificial intelligence in cardiovascular research: Fear less and live bolder.
European journal of clinical investigation
Apr 1, 2025
BACKGROUND: Artificial intelligence (AI) has captured the attention of everyone, including cardiovascular (CV) clinicians and scientists. Moving beyond philosophical debates, modern cardiology cannot overlook AI's growing influence but must actively ...
Enrichment Analysis and Deep Learning in Biomedical Ontology: Applications and Advancements.
Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih
Mar 31, 2025
Biomedical big data, characterized by its massive scale, multi-dimensionality, and heterogeneity, offers novel perspectives for disease research, elucidates biological principles, and simultaneously prompts changes in related research methodologies. ...
A Hands-On Introduction to Data Analytics for Biomedical Research.
Function (Oxford, England)
Mar 24, 2025
Artificial intelligence (AI) applications are having increasing impacts in the biomedical sciences. Modern AI tools enable uncovering hidden patterns in large datasets, forecasting outcomes, and numerous other applications. Despite the availability a...
Mapping intellectual structure and research hotspots of cancer studies in primary health care: A machine-learning-based analysis.
Medicine
Mar 21, 2025
In the contemporary fight against cancer, primary health care (PHC) services hold a significant and critical position within the healthcare system. This study, as one of the most detailed investigations into cancer research in primary care, comprehen...
Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.
Cancer medicine
Mar 1, 2025
INTRODUCTION: Artificial intelligence (AI) has significant potential to improve health outcomes in oncology. However, as AI utility increases, it is imperative to ensure that these models do not systematize racial and ethnic bias and further perpetua...
From statistics to deep learning: Using large language models in psychiatric research.
International journal of methods in psychiatric research
Mar 1, 2025
BACKGROUND: Large Language Models (LLMs) hold promise in enhancing psychiatric research efficiency. However, concerns related to bias, computational demands, data privacy, and the reliability of LLM-generated content pose challenges. GAP: Existing st...
Ethical considerations on the use of big data and artificial intelligence in kidney research from the ERA ethics committee.
Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
Feb 28, 2025
In the current paper, we will focus on requirements to ensure big data can advance the outcomes of our patients suffering from kidney disease. The associated ethical question is whether and how we as a nephrology community can and should encourage th...
Artificial Intelligence-Assisted Matching of Human Postmortem Donors to Ocular Research Projects.
Advances in experimental medicine and biology
Jan 1, 2025
The scarcity of human ocular samples with short postmortem intervals (PMIs) is a significant issue in ophthalmic research and drug discovery. A contributing factor is that eye banks must manually match donor data to prospective research project crite...
Large language models as an academic resource for radiologists stepping into artificial intelligence research.
Current problems in diagnostic radiology
Jan 1, 2025
BACKGROUND: Radiologists increasingly use artificial intelligence (AI) to enhance diagnostic accuracy and optimize workflows. However, many lack the technical skills to effectively apply machine learning (ML) and deep learning (DL) algorithms, limiti...
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