Artificial intelligence in rare disease diagnosis and treatment

Abstract

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.

Artificial intelligence in drug repurposing for rare diseases: a mini-review

“…Through this mini-review, we have been able to characterize the proportion represented by rare diseases in the literature describing the application of AI for drug repurposing. Compared to the other diseases categorized (e.g., COVID-19, cancer, or neurodegenerative diseases), rare diseases only represent 2.63% of the overall literature, which can also be found regarding the broader use of AI in rare diseases, where, compared to other application domains (e.g., disease diagnosis, gene identification, and drug discovery), drug repurposing is also a relatively less prominent area of focus ( 27 , 28 ). This low representation of rare diseases in the literature on AI-driven drug repurposing may be attributed to several factors, including the complexity of rare diseases, the small number of patients affected by rare diseases, the limited availability of data, the variability in symptoms, the genetic diversity among patients, or even the lack of funding priorities in rare diseases, making research in rare diseases not only challenging but also economically unappealing.…”

The Impact of Artificial Intelligence on Optimizing Diagnosis and Treatment Plans for Rare Genetic Disorders

“…They enhance our understanding of these conditions, aiding in the identification of precise treatment targets. Furthermore, AI leverages large datasets from quantitative structure-activity relationship (QSAR) modeling and high-throughput screening to advance therapeutic development, including the design of novel compounds with improved properties [46]. High-throughput screening campaigns yield an extensive corpus of data, and this wealth of information has notably culminated in the discovery of pharmaceuticals like riluzole, effectively employed in the treatment of amyotrophic lateral sclerosis [47].…”

“…Notably, they play a pivotal role in the early identification and resolution of issues pertaining to treatment response. Significant strides in therapeutic and monitoring tools are pivotal to the care of individuals with RDs [46]. Second-generation AI tools demonstrate adaptability in the formulation of treatment regimens based on patient responses, electronic data, and patient-reported outcomes.…”

“…They provide timely reminders for medication dosages, incorporate non-pharmacological therapeutic modalities, and optimize treatment timing based on response patterns [22]. For instance, given the variable response of patients with Gaucher disease to therapies, this monitoring framework serves as a crucial deterrent against the development of long-term complications [46].…”

Performance of Artificial Intelligence (AI)-Powered Chatbots in the Assessment of Medical Case Reports: Qualitative Insights From Simulated Scenarios

“…AI-powered chatbots, fueled by sophisticated natural language processing and machine learning algorithms, offer various potential applications in the medical field such as identifying research topics, assisting professionals in clinical and laboratory diagnosis, providing updates to healthcare professionals, and developing virtual assistants for patient health management [ 3 ]. Furthermore, AI demonstrates substantial promise within research-focused domains, such as rare diseases, encompassing target identification, biomarker discovery, preclinical optimization, patient recruitment, real-world data analysis, and precision medicine approaches across developmental stages [ 4 ]. Nevertheless, their integration into medical decision-making processes necessitates rigorous evaluation to ensure patient safety and effective communication.…”