Improving Clinical Diagnostics and Patient Care through Artificial Intelligence and Biosensor Technologies

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Healthcare
• Biosensor Technology

Background:

• Biosensors are crucial for detecting physiological signals in medical diagnosis.
• Advancements in Artificial Intelligence (AI) and machine learning (ML) offer potential improvements in biosensor efficacy and precision.
• Current biosensor applications include early disease detection and continuous patient monitoring.

Purpose of the Study:

• To analyze the advantages of AI and ML in biosensor technology.
• To highlight contemporary developments and applications of biosensors in medical diagnosis.
• To address challenges and ethical considerations hindering AI-enhanced biosensing.

Main Methods:

• Perspective analysis of AI and ML integration in biosensors.
• Review of current biosensor technology and medical diagnostic applications.
• Discussion of barriers to AI adoption and ethical/legal implications.

Main Results:

• AI and ML substantially improve the accuracy and precision of biosensor-based physiological signal detection.
• AI-enhanced biosensors show promise for early disease detection and continuous monitoring.
• Key challenges include data quality, AI technique relevance, data privacy, and legal issues.

Conclusions:

• AI-enhanced biosensing systems have the potential to significantly improve healthcare outcomes.
• Overcoming current challenges can facilitate the widespread adoption of AI in personalized medicine.
• Further research into innovative approaches is needed to fully realize the potential of AI in biosensing.