Artificial intelligence in rare disease diagnosis and treatment

• Published in: Clinical and translational science Vol. 16; no. 11; pp. 2106 - 2111
• Main Authors: Wojtara, Magda, Rana, Emaan, Rahman, Taibia, Khanna, Palak, Singh, Heshwin
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.11.2023; Wiley
• Subjects: Algorithms; Artificial Intelligence; Clinical outcomes; Comorbidity; Coronaviruses; COVID-19; Decision making; Delivery of Health Care; Diagnosis; Health care; Humans; Huntingtons disease; Machine Learning; Medical diagnosis; Medical prognosis; Medical treatment; Patients; Precision Medicine; R&D; Rare diseases; Rare Diseases - diagnosis; Rare Diseases - therapy; Research & development; Respiratory distress syndrome; Scleroderma; United States > US
• ISSN: 1752-8054; 1752-8062; 1752-8062
• DOI: 10.1111/cts.13619

Artificial intelligence (AI) utilization in health care has grown over the past few years. It also has demonstrated potential in improving the efficiency of diagnosis and treatment. Some types of AI, such as machine learning, allow for the efficient analysis of vast datasets, identifying patterns, and generating key insights. Predictions can then be made for medical diagnosis and personalized treatment recommendations. The use of AI can bypass some conventional limitations associated with rare diseases. Namely, it can optimize traditional randomized control trials, and may eventually reduce costs for drug research and development. Recent advancements have enabled researchers to train models based on large datasets and then fine‐tune these models on smaller datasets typically associated with rare diseases. In this mini‐review, we discuss recent advancements in AI and how AI can be applied to streamline rare disease diagnosis and optimize treatment.