Advancing Personalized Healthcare Through AI and Computer Audition Technologies

Over the past decades, artificial intelligence has increasingly integrated into the healthcare sector, enhancing diagnostics, decision-making, and treatments through extensive data analysis. Computer audition, powered by advancements in signal processing, machine learning, and deep learning, offers a groundbreaking approach for non-invasive, cost-effective healthcare services using human speech and body sounds. With the rise of Internet-of-Things (IoT) technologies, there is an unprecedented opportunity to tailor healthcare services more closely to individual needs through intelligent auditory systems. Despite considerable progress, the integration of these technologies into personalized healthcare applications is ongoing, with significant scope for exploration and innovation.

This Research Topic aims to propel the interdisciplinary integration of healthcare, artificial intelligence, and acoustic signal processing. By fostering research into novel methodologies and the development of innovative applications, this initiative seeks to create a platform for the advancement of reliable and sustainable AI-driven healthcare solutions tailored to individual patient needs. The ultimate objective is to enhance personal health management and improve outcomes through sophisticated machine learning and computer audition interventions.

To refine the current landscape of AI in personalized healthcare, this Research Topic focuses on a defined range of intelligent auditory technologies. Articles are invited that explore, but are not limited to, the following areas:

- Prototypes of computer audition systems for healthcare applications
- Development of new acoustic databases for healthcare research
- Advanced computer audition methodologies for healthcare applications
- Development and application of audio/speech foundation models in healthcare
- Leveraging large language models in healthcare
- Multimodal data processing, including audio/speech and other biosignals, for healthcare applications
- Trustworthy machine learning techniques for healthcare auditory systems
- Counteracting adversarial attacks in healthcare computer audition
- Robust machine learning models for dependable healthcare applications
- Analytical discussions on sustaining AI in healthcare through computer audition

This scope ensures that the research gathered addresses both the technological advancements and the practical challenges in the field, advancing the knowledge and application of AI in personalized healthcare.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Classification
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Classification
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Registered Report
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Intelligent audition, Healthcare, Trustworthiness, Dependability, Sustainability

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.