Artificial Intelligence in Personalized Rheumatology: From E-health to Precision Medicine

Dear Colleagues,

Digital health is a hot topic in rheumatology, and the SARS-CoV-2 pandemic has greatly accelerated e-health transition and telemedicine. The challenge now facing rheumatologists is synthesizing insights from current advances in digital health into a framework for exploiting precision medicine relying on clinical patient biometric data gathered with biomedical devices.

Big data analytics offer many significant benefits for patient care, such as the identification of new biomarkers for early diagnosis and monitoring of therapeutic treatments. In addition, the integration of different data (omics, microbiome, histopathological or imaging data, etc.) based on Artificial Intelligence (AI)  can enhance the understanding of the mechanisms of rheumatologic diseases and provide guidance for personalized treatments and the identification of novel therapeutic targets and strategies.

The healthcare field continues to evolve as machine learning (ML), and AI technologies become more advanced in the digital age. Integrating AI into Medicine allows for many benefits, including automating tasks and integrating complex information to deliver better treatments and faster diagnosis, all at a lower cost. From hospital care to clinical research, AI applications have the potential to revolutionize how the health sector works to reduce spending and improve patient outcomes.

The emerging applications to the heath of ML and complex network algorithms are not intended to outperform clinicians in decision making, but to expand access to current medical standards and assist clinicians. In rheumatology, AI can represent a step toward precision medicine, leading to patient profiling and treatment personalization. To date, many research groups are focusing their interest on ML applied to rheumatology. Recent evidence has shown that ML algorithms are capable of accurately predicting sustained remission in patients with rheumatoid arthritis on biologic agents and of grading synovitis on histopathological photomicrographs automatically.

This Special Issue aims to gather the most interesting novel manuscripts on ML methods in rheumatology, which are a candidate to be adopted in routine clinical practice. In this Special Issue, we will explore recent advances in personalized rheumatology, from classification tasks for the prognosis of rheumatic diseases to treatment personalization.

Prof. Dr. Sabina Tangaro
Prof. Dr. Florenzo Iannone
Dr. Vincenzo Venerito
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning
• Artificial Intelligence
• rheumatology
• e-Health