Table 3.

Challenges and Solutions for AI Adoption in Healthcare.

Challenge	Description	Proposed Solutions
Data Privacy	Concerns over the security of patient data	Implementing blockchain for secure data storage Strengthening data encryption
Dataset Bias	Lack of diverse training data leading to biased AI models	Ensuring diverse datasets Conducting bias audits
Lack of Explainability	Difficulty in interpreting AI model decisions	Developing interpretable AI models Using explainable AI (XAI) techniques [71]
Integration Issues	Difficulty integrating AI with legacy systems	Use of APIs for compatibility [72] Gradual system modernization
Regulatory Compliance	Navigating complex regulations for AI in healthcare	Collaborating with regulators Staying updated on guidelines
High Costs	Significant investment required for AI technologies	Leveraging cloud-based solutions Exploring public-private partnerships
Staff Resistance	Reluctance among healthcare staff to adopt new technologies	Providing training programs Highlighting AI benefits
Limited Infrastructure	Inadequate technological infrastructure to support AI	Investing in IT upgrades Utilizing cloud computing resources
Data Interoperability	Challenges in sharing and accessing patient data across systems	Adopting standardized data formats Implementing health information exchanges