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Advancing Point-of-Care Testing by Application of Machine Learning Techniques and Artificial Intelligence.

Craig M Lilly¹, Apurv V Soni², Denise Dunlap³

¹Department of Medicine, UMass Chan Medical School, Worcester, MA; Department of Anesthesiology and Department of Surgery, UMass Chan Medical School, Worcester, MA; Graduate School of Biomedical Sciences, UMass Chan Medical School, Worcester, MA; UMass Memorial Health, Worcester, MA.

|August 25, 2024

Summary

This summary is machine-generated.

Artificial intelligence (AI) offers potential for healthcare, particularly in diagnosis and management. Effective collaboration and clear terminology are crucial for developing AI solutions in clinical settings.

Keywords:

computer-assisted decision tree decision-making deep learning informatics

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Area of Science:

Healthcare technology
Artificial intelligence in medicine

Background:

Artificial intelligence (AI) shows promise for improving diagnosis and management of various health conditions.
Point-of-care testing expands healthcare access beyond traditional settings.
Effective collaboration is key to addressing clinical challenges.

Purpose of the Study:

To highlight the potential of artificial intelligence in healthcare.
To emphasize the importance of collaboration in developing AI solutions.
To advocate for clear terminology in research for better collaboration.

Main Methods:

Literature review on AI in healthcare and point-of-care testing.
Analysis of collaboration models between developers, clinicians, and end-users.
Discussion on the role of standardized terminology in research.

Main Results:

AI is recognized for its potential to enhance diagnosis and management of diseases.
Point-of-care testing improves healthcare accessibility.
Collaboration and clear communication are vital for successful clinical problem-solving using AI.

Conclusions:

AI holds significant promise for transforming healthcare delivery.
Interdisciplinary collaboration and standardized terminology are essential for realizing AI's full potential in clinical practice.