About this Research Topic
Data science and artificial intelligence (AI) are having a major impact on healthcare. While advances in the sharing and analysis of medical data result in better and earlier diagnoses and more patient-tailored treatments, data management is also affected by trends such as increased patient-centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The way in which health services are delivered is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science and AI, researchers can provide new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at an individual and population level.
This Research Topic focuses on how AI is used in healthcare, and on related topics such as data sharing, data management, and bioethical issues. For example, AI is gaining a leading role in data processing supporting clinical practice but AI-based decisions are biased. The increasing use of AI in healthcare gives a lot of new and interesting possibilities, but it also causes issues around trust (the "black box" problem) and privacy. This issue intends to show how AI will impact healthcare and discuss both advantages and disadvantages, and what solutions there are to solve potential problems.
Data science is an interdisciplinary field that applies techniques such as artificial intelligence to create value, based on extracting knowledge and insights from available big data. This collection especially welcomes contributions that address (one of) the following topics:
- Data management in AI healthcare applications, including current, emerging and future applications (e.g., medical visualization);
- Use of devices beyond the traditional healthcare system to aid data collection (e.g., wearables);
- Barriers to the application of AI in healthcare, such as data bias from under-represented populations as well as policies around data sharing and open access vs proprietary platforms;
- Management of big data, including use of FAIR Guiding Principles for scientific data management and stewardship;
- Regulatory, legal, and ethical issues related to using of AI such as data governance, data protection, privacy, and bioethics (e.g. GDPR and AI);
- Distributed learning and use of federated data systems.
We welcome various types of papers, such as Perspective, Review, Original Research, etc.
We anticipate that the reader of this Research Topic will become familiar not only with the applications (current/ future) but also with the challenges, barriers, and bioethical issues that may impede widespread adoption or use of AI in healthcare.
This Research Topic focuses on how AI is used in healthcare, and on related topics such as data sharing, data management, and bioethical issues. For example, AI is gaining a leading role in data processing supporting clinical practice but AI-based decisions are biased. The increasing use of AI in healthcare gives a lot of new and interesting possibilities, but it also causes issues around trust (the "black box" problem) and privacy. This issue intends to show how AI will impact healthcare and discuss both advantages and disadvantages, and what solutions there are to solve potential problems.
Data science is an interdisciplinary field that applies techniques such as artificial intelligence to create value, based on extracting knowledge and insights from available big data. This collection especially welcomes contributions that address (one of) the following topics:
- Data management in AI healthcare applications, including current, emerging and future applications (e.g., medical visualization);
- Use of devices beyond the traditional healthcare system to aid data collection (e.g., wearables);
- Barriers to the application of AI in healthcare, such as data bias from under-represented populations as well as policies around data sharing and open access vs proprietary platforms;
- Management of big data, including use of FAIR Guiding Principles for scientific data management and stewardship;
- Regulatory, legal, and ethical issues related to using of AI such as data governance, data protection, privacy, and bioethics (e.g. GDPR and AI);
- Distributed learning and use of federated data systems.
We welcome various types of papers, such as Perspective, Review, Original Research, etc.
We anticipate that the reader of this Research Topic will become familiar not only with the applications (current/ future) but also with the challenges, barriers, and bioethical issues that may impede widespread adoption or use of AI in healthcare.
Declared COIs: Tim Hulsen and Milan Petkovic are employees of Philips Research. Saumya Shekhar Jamuar is a co-founder of Global Gene Corp.
Keywords: AI, Personalized Healthcare, Data Management, Data Wrangling, Big Data
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.
