Artificial intelligence (AI)-aided disease prediction

Announcing a new article publication for BIO Integration journal. In this review article the authors Chenxi Liu, Dian Jiao and Zhe Liu, from Tianjin University, Tianjin, China consider artificial intelligence (AI) aided disease prediction.

Artificial intelligence (AI) is widely used in clinical medicine and is increasingly applied to the fields of AI-aided image analysis, AI-aided lesion determination and AI-assisted healthcare management.

In this article the authors discuss the emerging applications of AI-related medicine and AI-assisted visualized medicine, including novel diagnostic approaches, metadata analytical methods, and versatile AI-aided treatment applications in preclinical and clinical uses. New progress and breakthroughs in AI-aided disease prediction exhibit tremendous potential for clinical use in the future.

Article reference: Chenxi Liu, Dian Jiao and Zhe Liu, (AI)-aided Disease Prediction. BIO Integration, 2020,


BIO Integration is fully open access journal which will allow for the rapid dissemination of multidisciplinary views driving the progress of modern medicine.

As part of its mandate to help bring interesting work and knowledge from around the world to a wider audience, BIOI will actively support authors through open access publishing and through waiving author fees in its first years. Also, publication support for authors whose first language is not English will be offered in areas such as manuscript development, English language editing and artwork assistance.

BIOI is now open for submissions; articles can be submitted online at:

Please visit to learn more about the journal.

Editorial Board:

BIOI is available on the IngentaConnect platform ( and at the BIO Integration website (

Submissions may be made using ScholarOne (

There are no author submission or article processing fees.

Follow BIOI on Twitter @JournalBio; Facebook ( and LinkedIn (

ISSN 2712-0074

eISSN 2712-0082

Keywords: Artificial intelligence, clinical medicine, deep learning, disease prediction, visualized medicine