Biosensors, Artificial Intelligence Biosensors, False Results and Novel Future Perspectives

Area of Science:

• Biomedical Engineering
• Medical Diagnostics
• Artificial Intelligence

Background:

• Medical biosensors are fundamental to diagnostics.
• Artificial Intelligence (AI) has significantly advanced diagnostic capabilities.
• False results remain a challenge in all diagnostic methods.

Purpose of the Study:

• To critically review biosensor types and their potential sources of false results.
• To analyze AI's role in biosensor diagnostics and associated misdiagnoses.
• To provide expert opinions and future perspectives for overcoming diagnostic inaccuracies.

Main Methods:

• State-of-the-art literature review of biosensor technologies.
• Analysis of AI algorithms applied to biosensor data.
• Synthesis of expert insights on diagnostic error reduction.

Main Results:

• Common biosensor types and their specific failure modes leading to false results are detailed.
• AI-driven diagnostic systems also exhibit potential for misdiagnosis, with contributing factors identified.
• A comprehensive overview of current challenges in biosensor and AI biosensor accuracy is presented.

Conclusions:

• Understanding the origins of false results in biosensors and AI is crucial for enhancing diagnostic reliability.
• Future research should focus on mitigating identified error sources.
• Expert consensus points towards integrated strategies for improved accuracy in AI-assisted biosensing.