When it comes to the field of biosensors and biosensing technology, there are many exciting applications of artificial intelligence (AI) and deep learning (DL). AI and DL can be used to process the vast amounts of data generated by biosensors. They can help identify patterns, trends, and anomalies, thereby improving data interpretability and accuracy. DL techniques can assist in identifying complex biological features such as protein structures, DNA sequences, cell images, and more. This technology is invaluable for rapidly and accurately identifying and classifying biological samples. Real-time Monitoring and Diagnostics: Biosensors integrated with AI can enable real-time monitoring and diagnostics. For instance, intelligent biosensors combined with deep learning can monitor a patient's physiological parameters and provide immediate health assessments. AI can utilize data from biosensors to build models that predict disease progression, identify potential health risks, and offer personalized medical advice. Optimizing Sensor Design: DL algorithms can optimize the design of biosensors, improving their sensitivity, specificity, and stability, thus enhancing their performance and accuracy.
In the realm of biosensors and biosensing technology, despite remarkable advancements driven by AI and deep learning, a persistent challenge lies in the lack of clear, understandable explanations for the functioning of these systems. The integration of artificial intelligence with biosensors has indeed revolutionized medical diagnostics and monitoring. However, a significant hurdle remains: the interpretability of the results generated by these systems. While AI excels at processing vast amounts of complex data from biosensors, the rationale behind the decisions or outcomes often lacks transparent explanations. This lack of interpretability hampers the full comprehension of how these systems arrive at conclusions or predictions. For instance, when a biosensor coupled with AI identifies a certain pattern or anomaly in biological data, it might be challenging to precisely understand the exact factors or features that led to that determination. This issue is particularly critical in medical applications where clear explanations are vital for healthcare practitioners to trust and effectively use these technologies. For instance, a physician might hesitate to rely on a diagnostic recommendation from an AI-driven biosensor if the rationale behind the recommendation isn't adequately explained or understood. The quest for improving interpretability in biosensing technologies involves ongoing research efforts.
The aim of this topic is to provide high-quality, up-to-date approaches to analyze and process output signals of medical biosensors. The key point is to develop efficient medical biosensors technology based on explainable models.
Articles may address but are not limited to the following topics:
• Explainable Machine Learning in Medical Biosensors
• Explainable Deep Learning in Medical Biosensors.
• Explainable Models for Biosensing Data Analysis
• Explainable Models for Biosensing Imaging Technology
• User-Friendly Interfaces for Biosensor Data Interpretation.
• Human-AI Interaction in Medical Diagnostics:
• Explaining AI-Driven Automatic Biomedical Diagnosis
• Optimization-Driven Biomedical Data Enhancement
• Explainable Model for Biomedical Engineering Data Analysis
• Explainable Methods for Biomedical Engineering Foundation Model
Keywords:
artificial intelligence, deep learning, biosensors, biosensing
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
When it comes to the field of biosensors and biosensing technology, there are many exciting applications of artificial intelligence (AI) and deep learning (DL). AI and DL can be used to process the vast amounts of data generated by biosensors. They can help identify patterns, trends, and anomalies, thereby improving data interpretability and accuracy. DL techniques can assist in identifying complex biological features such as protein structures, DNA sequences, cell images, and more. This technology is invaluable for rapidly and accurately identifying and classifying biological samples. Real-time Monitoring and Diagnostics: Biosensors integrated with AI can enable real-time monitoring and diagnostics. For instance, intelligent biosensors combined with deep learning can monitor a patient's physiological parameters and provide immediate health assessments. AI can utilize data from biosensors to build models that predict disease progression, identify potential health risks, and offer personalized medical advice. Optimizing Sensor Design: DL algorithms can optimize the design of biosensors, improving their sensitivity, specificity, and stability, thus enhancing their performance and accuracy.
In the realm of biosensors and biosensing technology, despite remarkable advancements driven by AI and deep learning, a persistent challenge lies in the lack of clear, understandable explanations for the functioning of these systems. The integration of artificial intelligence with biosensors has indeed revolutionized medical diagnostics and monitoring. However, a significant hurdle remains: the interpretability of the results generated by these systems. While AI excels at processing vast amounts of complex data from biosensors, the rationale behind the decisions or outcomes often lacks transparent explanations. This lack of interpretability hampers the full comprehension of how these systems arrive at conclusions or predictions. For instance, when a biosensor coupled with AI identifies a certain pattern or anomaly in biological data, it might be challenging to precisely understand the exact factors or features that led to that determination. This issue is particularly critical in medical applications where clear explanations are vital for healthcare practitioners to trust and effectively use these technologies. For instance, a physician might hesitate to rely on a diagnostic recommendation from an AI-driven biosensor if the rationale behind the recommendation isn't adequately explained or understood. The quest for improving interpretability in biosensing technologies involves ongoing research efforts.
The aim of this topic is to provide high-quality, up-to-date approaches to analyze and process output signals of medical biosensors. The key point is to develop efficient medical biosensors technology based on explainable models.
Articles may address but are not limited to the following topics:
• Explainable Machine Learning in Medical Biosensors
• Explainable Deep Learning in Medical Biosensors.
• Explainable Models for Biosensing Data Analysis
• Explainable Models for Biosensing Imaging Technology
• User-Friendly Interfaces for Biosensor Data Interpretation.
• Human-AI Interaction in Medical Diagnostics:
• Explaining AI-Driven Automatic Biomedical Diagnosis
• Optimization-Driven Biomedical Data Enhancement
• Explainable Model for Biomedical Engineering Data Analysis
• Explainable Methods for Biomedical Engineering Foundation Model
Keywords:
artificial intelligence, deep learning, biosensors, biosensing
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.