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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: Brief Research Report, Clinical Trial, Community Case Study, Conceptual Analysis, Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Policy and Practice Reviews, Review, Specialty Grand Challenge, Study Protocol, Systematic Review and Technology and Code.
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.
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