About this Research Topic
Advances in computing, such as machine learning, natural language processing, and cognitive computing, are driving automation across multiple sectors. These computational advancements have the potential to revolutionize medicine, a field traditionally reliant on highly skilled human interaction. Among the envisioned impacts of these systems are the identification of patients at risk of adverse events, cost reduction in both patient care and hospital administration, and heightened efficiency in the medical workforce. Nevertheless, the field of medicine presents distinctive challenges to application of these automations, such as stringent privacy requirements, fragmented care models, brittle datasets, and a general lack of familiarity with these emerging technologies among medical professionals. In navigating these challenges, it is crucial for innovations to not only address technical aspects but also meet the high expectations related to privacy, interoperability, and the unique nuances of medical practice. The integration of computational methods in medicine holds significant promise but necessitates a careful approach to ensure alignment with the complex nature of healthcare practices and uphold the highest standards of patient care.
Our goal is to identify new innovations and expand upon existing practices in advanced computing within medical practice. These improvements should help to optimize healthcare outcomes, navigate the intricate challenges associated with the integration of computational methods into medicine, explore the bioethics of computing in healthcare, and address the prevailing lack of familiarity within the medical community. Ultimately, this project aspires to contribute valuable insights and innovative solutions, fostering the incorporation of advanced computing technologies into the intricate landscape of medical care to improve patient care and advance the work of healthcare professionals.
This research topic seeks to elucidate innovations in the introduction and integration of advanced computing within medical care. Any Frontiers article types are welcome to be submitted. Suggested topic areas include but are not limited to:
1. Development: Addresses the need for new models or analyses of datasets that improve healthcare outcomes. Examples include studies decreasing mortality rates, pinpointing patients susceptible to adverse events, and curbing healthcare costs or inefficiencies.
2. Integration: Addresses the multifaceted challenges inherent in incorporating computational methods into medical practice. Examples include ensuring user interfaces or dashboards, interoperability, or toolsets for analyzing data.
3. Bioethics of Computing in Healthcare: Addresses ethical considerations and novel dilemmas associated with the application of computational advancements in healthcare. Examples include questions of privacy, consent, and the responsible use of technology. 4. Education: Addresses the lack of familiarity or mistrust towards computing within the medical community or investigates strategies for cultivating comfort and proficiency with these tools. Examples include educational initiatives, effective use of existing curricula or methods, or other education tailored to medical professionals.
Our goal is to identify new innovations and expand upon existing practices in advanced computing within medical practice. These improvements should help to optimize healthcare outcomes, navigate the intricate challenges associated with the integration of computational methods into medicine, explore the bioethics of computing in healthcare, and address the prevailing lack of familiarity within the medical community. Ultimately, this project aspires to contribute valuable insights and innovative solutions, fostering the incorporation of advanced computing technologies into the intricate landscape of medical care to improve patient care and advance the work of healthcare professionals.
This research topic seeks to elucidate innovations in the introduction and integration of advanced computing within medical care. Any Frontiers article types are welcome to be submitted. Suggested topic areas include but are not limited to:
1. Development: Addresses the need for new models or analyses of datasets that improve healthcare outcomes. Examples include studies decreasing mortality rates, pinpointing patients susceptible to adverse events, and curbing healthcare costs or inefficiencies.
2. Integration: Addresses the multifaceted challenges inherent in incorporating computational methods into medical practice. Examples include ensuring user interfaces or dashboards, interoperability, or toolsets for analyzing data.
3. Bioethics of Computing in Healthcare: Addresses ethical considerations and novel dilemmas associated with the application of computational advancements in healthcare. Examples include questions of privacy, consent, and the responsible use of technology. 4. Education: Addresses the lack of familiarity or mistrust towards computing within the medical community or investigates strategies for cultivating comfort and proficiency with these tools. Examples include educational initiatives, effective use of existing curricula or methods, or other education tailored to medical professionals.
Keywords: artificial intelligence; machine learning; outcomes
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.
