Revolutionizing diabetic retinopathy screening and management: The role of artificial intelligence and machine learning

Table 5 Challenges and solutions for artificial intelligence/machine learning implementation in diabetic retinopathy screening

Challenge	Ref.	Description	Potential solution
Data standardization and interoperability	Mandl et al[76], 2024	Difficulty in integrating AI tools with diverse electronic health record systems	Develop universal data standards and use fast healthcare interoperability resources APIs
		Clinicians need AI tools that seamlessly integrate into their existing workflows without adding complexity or disrupting patient care
		Ensuring scalability, security, and ongoing technical support are critical considerations
Ethical and regulatory concerns	Goldberg et al[77], 2024	Issues related to data privacy, algorithmic bias, and lack of clear regulatory guidelines	Promote diverse datasets, establish clear regulatory frameworks, and ensure data security
Scalability and maintenance	Marvasti et al[78], 2024	Challenges in deploying AI systems across large healthcare networks	Use cloud-based platforms for scalability and provide ongoing technical support

AI: Artificial intelligence; APIs: Application programming interfaces.