Table 1.

Tangible examples of AI improving RD diagnosis and management

Title	Description
Symptoms checker website	AI-based applications allow patients or physicians to input presenting symptoms to determine the probabilistic differential diagnosis based on various RD databases. ⁶²
RD diagnosis support using facial features	Gurovich and colleagues ⁶³ proposed a novel facial recognition algorithm called DeepGestalt, powered by FDNA inc., that classifies distinct facial features from photos of individuals with rare neurodevelopmental and congenital disorders. The phenotypic analysis algorithm is trained on 100,000 images and can distinguish between 200 different rare syndromes achieving 91% accuracy in identifying the correct diagnosis in the top 10 suggestions. ⁶³
AI for RD drug repurposing	Lee and colleagues ¹²¹ used the URSAHD (unveiling RNA sample annotation for human diseases) machine learning framework to apply genetic and molecular information from hundreds of complicated conditions to medication repurposing. Similarly, Ekins and colleagues created an end-to-end ML algorithm that leverages ⁶⁴ large quantities of screening data to predict bioactivities for therapeutic targets and molecular properties with increased levels of accuracy.
AI-based genetic devices	The ML algorithm “Xrare” proposed by Li and colleagues ⁶⁵ jointly uses phenotypic and genetic evidence for the identification of causative gene variants in RDs and the prioritization of these diagnoses.
AI-based Human-on-a-chip and Clinical-Trial on-a-Chip drug development	Almost 95% of RDs do not have an approved treatment option ⁶⁶ . Recent AI systems have shown great promise in promoting drug development or repurposing of available therapeutics. For example, the human-on-a-chip (HoaC) or organ-on-a-chip ^{58 , 67} is a novel CNN-based model that mimics human organ functionality in interconnected in-vitro bioengineered micro-physiological systems. This medical imaging technology can recapitulate clinical trials on a chip (CToCs) ⁶⁸ lowering the costs and recruitment barriers related to conducting RD clinical trials (scarce number of RD patients) ⁵⁷ . This technology promotes research and development of novel PET radioactive tracers for tumor detection and treatment by introducing human phenotypic models early in the drug discovery process.
AI and PET Enables Orphan Drug Development	AI-based PET kinetic modelings have the potential to yield valuable data regarding the pharmacokinetics of investigational pharmaceuticals with a small number of participants.
Radiophenomics: AI-based medical imaging for recognition of rare disease manifestation	TB-PET powered with AI capabilities has enormous potential to identify imaging patterns suggestive of rare diseases phenotypes. Therefore, using image-based phenotyping (radiophenomics) the AI can suggest a possibility of a rare disease pattern to the physician.
AI-based medical imaging management of rare diseases	Pretest probability is the likelihood of having a condition before the results of a diagnostic test are known ⁶⁹ . AI algorithms can be trained to understand the pre-test probability and incorporate it into screening and management in individuals with rare diseases. The accuracy of the probability scale generated by AI and the ideal threshold used on the image may vary depending on the pre-test probability.