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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Controlled Cortical Impact Model of Mouse Brain Injury with Therapeutic Transplantation of Human Induced Pluripotent Stem Cell-Derived Neural Cells

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Potential Applications and Ethical Considerations for Artificial Intelligence in Traumatic Brain Injury Management.

Kryshawna Beard^1,2, Ashley M Pennington^1,3, Amina K Gauff^1,3

¹Traumatic Brain Injury Center of Excellence, Silver Spring, MD 20910, USA.

|November 27, 2024

Summary

Artificial intelligence (AI) can help analyze healthcare data for better clinical decisions and traumatic brain injury (TBI) management. Ethical considerations and regulations are crucial as AI in medicine advances.

Keywords:

TBI artificial intelligence diagnostic machine learning neuromonitoring

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Controlled Cortical Impact Model for Traumatic Brain Injury

Controlled Cortical Impact Model for Traumatic Brain Injury

Published on: August 5, 2014

Area of Science:

Medical Informatics
Neurology
Artificial Intelligence

Background:

Artificial intelligence (AI) offers powerful tools for analyzing complex healthcare data.
Traumatic brain injury (TBI) is a heterogeneous neurological condition with diagnostic and treatment challenges.

Purpose of the Study:

To provide an overview of AI techniques in medical research.
To describe AI applications in traumatic brain injury (TBI) diagnosis and management.
To discuss ethical considerations and guidelines for AI in medicine.

Main Methods:

Narrative review of existing literature on AI in medical research.
Focused review of studies on AI applications in TBI.
Analysis of ethical concerns and regulatory guidelines for AI in healthcare.

Main Results:

AI shows promise in identifying patterns for clinical decision-making, medical education, and research planning.
Emerging evidence supports AI's utility in aiding TBI diagnosis and management.
Methodological and ethical challenges require careful monitoring and regulation.

Conclusions:

AI holds significant potential for advancing medical research and clinical practice, particularly in complex conditions like TBI.
Responsible development and implementation of AI in medicine are essential.
Adherence to established guidelines is necessary for the ethical application of AI in healthcare and research.