Table 1 Challenges and suggested solutions

Data Quality and Bias

AI models often trained on biased datasets from high income countries or specific populations.

Use diverse, representative datasets for training AI models.

Biased outputs can exacerbate healthcare disparities.

Implement bias detection and mitigation strategies in AI development.

Transparency and Explainability

AI systems often operate as “black boxes,” making decisions difficult to understand.

Develop interpretable AI models to ensure transparency.

Lack of transparency undermines trust and accountability in clinical practice.

Implement explainability tools to provide insights into AI decision making processes.

Automation Bias and Human Factors

Risk of overreliance on AI generated recommendations, even when inaccurate.

Train healthcare providers to critically assess AI recommendations.

Overdependence on AI may lead to degradation of clinical skills.

Encourage a balanced approach, combining AI assistance with human expertise.

Ethical and Legal Concerns

Issues include patient consent, data privacy, and the potential for AI to cause harm.

Establish ethical guidelines and legal frameworks specific to AI in healthcare.

Rapid AI advancements often outpace existing legal standards.

Regularly update legal frameworks to keep pace with AI developments.

Privacy and Security

AI systems are vulnerable due to complex data handling, increasing cyber threat risks.

Implement robust data protection measures and encryption.

Anonymization challenges and risk of reidentification.

Use advanced anonymization techniques and regular security audits.

Machine learning models susceptible to adversarial attacks.

Develop AI specific cybersecurity protocols to mitigate risks.

Decentralized AI processing complicates compliance with GDPR, HIPAA, and other regulations.

Ensure strict adherence to data protection regulations and focus on organizational security.

Insider threats and the opaque nature of AI models further complicate security efforts.

Implement strong access controls and transparency measures in AI model development.