Advanced Methodologies for Technological Implementation for Ethical Considerations in AI Powered Healthcare Systems
Maham, Akhlaq (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024060621529
https://urn.fi/URN:NBN:fi:amk-2024060621529
Tiivistelmä
This thesis examined the integration of artificial intelligence (AI) into healthcare systems, focusing on its role, ethical considerations, model development, challenges, and future implications. It explored how AI revolutionizes disease detection, enhances patient care, streamlines administrative tasks, improves drug discovery, facilitates remote monitoring, and enhances medical imaging interpretation. The critical importance of data in healthcare and the imperative of data privacy were addressed, emphasizing the need for robust security measures and compliance frameworks.
Ethical challenges in AI-powered healthcare systems were discussed, along with regulatory frameworks and privacy laws. The thesis propose the "PERBE" ethical framework to guide the responsible development of AI-powered healthcare systems. Model development using privacy-preserving techniques such as pseudonymization and encryption was presented, along with implementation and testing outcomes.
Identified limitations and challenges, including data bias in AI models, were discussed, highlighting the necessity for diverse data collection and rigorous analysis practices. Future implications of AI in healthcare were explored, emphasizing robust data processing techniques, fair and unbiased algorithms, regulatory compliance, patient-centric AI, and ethics screening in AI models.
The conclusion underscored the importance of addressing ethical considerations to ensure patient-centered, equitable, and responsible use of AI in healthcare.
Ethical challenges in AI-powered healthcare systems were discussed, along with regulatory frameworks and privacy laws. The thesis propose the "PERBE" ethical framework to guide the responsible development of AI-powered healthcare systems. Model development using privacy-preserving techniques such as pseudonymization and encryption was presented, along with implementation and testing outcomes.
Identified limitations and challenges, including data bias in AI models, were discussed, highlighting the necessity for diverse data collection and rigorous analysis practices. Future implications of AI in healthcare were explored, emphasizing robust data processing techniques, fair and unbiased algorithms, regulatory compliance, patient-centric AI, and ethics screening in AI models.
The conclusion underscored the importance of addressing ethical considerations to ensure patient-centered, equitable, and responsible use of AI in healthcare.