In the medicine and health areas, the advent of big data and artificial intelligence brings about enormous opportunities and challenges. Challenges include but are by no means limited to access to and quality of big data, the mechanics of data warehousing, and indeed how to make sense of big data to gain useful insights. Artificial Intelligence describes computational approaches to deal with these questions, to perform computational analyses of complex medical and health data that are beyond human cognitive capacities and which require modern data-mining technologies. The opportunities of big data in health and medicine promise new cures, better patient outcomes and satisfaction, as well as a deeper understanding of disease biology, among many others.
This section hosts papers fostering technological developments, pioneering examples, as well as contributions discussing and reviewing these developments in health and medicine. There is a genuine need to grasp both the opportunities and challenges for these new evidence-generating (big data) approaches – approaches that promise to complement systematic reviews of literature as the core of Evidence-based Medicine and Health Care. Quality assurance of data, data-mining, and evidence interpretation will be central to making this a pilar of treatment (Medicine) and prevention (Public Health) of disease, and thus for positive study and patient outcomes.
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Medicine and Public Health welcomes submissions of the following article types: Clinical Study Protocol, Clinical Trial, Code, Correction, Data Report, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review, Specialty Grand Challenge and Technology Report.
All manuscripts must be submitted directly to the section Medicine and Public Health, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
Articles published in the section Medicine and Public Health will benefit from the Frontiers impact and tiering system after online publication. Authors of published original research with the highest impact, as judged democratically by the readers, will be invited by the Chief Editor to write a Frontiers Focused Review - a tier-climbing article. This is referred to as "democratic tiering". The author selection is based on article impact analytics of original research published in all Frontiers specialty journals and sections. Focused Reviews are centered on the original discovery, place it into a broader context, and aim to address the wider community across all of Big Data.
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