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Application of Artificial Intelligence in Medical Diagnostics: Applications and Implications in the Healthcare Sector
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Artificial Intelligence (AI) has emerged as a transformative innovation in the medical diagnostic sector. This study explores the application and implications of AI in healthcare services at RSUD Dr. H. Abdul Moeloek, Bandar Lampung. Using a qualitative case study method, data were obtained through in-depth interviews and participatory observation. The results show that AI contributes significantly to improving diagnostic accuracy and speed, particularly in radiological imaging. However, limitations in technological infrastructure and system integration were found to hinder its optimal use. Furthermore, the readiness of human resources remains a critical factor. Although there is optimism among medical staff, a lack of technical training has led to gaps in understanding and utilization. Ethical and legal concerns also emerged, especially regarding responsibility in case of misdiagnosis and the protection of patient data. The absence of specific regulations and digital ethics protocols presents a major barrier to AI adoption. This research concludes that while the implementation of AI in medical diagnostics shows promising outcomes, it still faces institutional and regulatory challenges. Strengthening digital literacy among healthcare workers, developing standard operating procedures, and building a secure infrastructure are essential. Collaboration between hospitals, academic institutions, and government bodies is needed to create an inclusive and ethical AI-based healthcare ecosystem.
PT. Seulanga System Publisher
Title: Application of Artificial Intelligence in Medical Diagnostics: Applications and Implications in the Healthcare Sector
Description:
Artificial Intelligence (AI) has emerged as a transformative innovation in the medical diagnostic sector.
This study explores the application and implications of AI in healthcare services at RSUD Dr.
H.
Abdul Moeloek, Bandar Lampung.
Using a qualitative case study method, data were obtained through in-depth interviews and participatory observation.
The results show that AI contributes significantly to improving diagnostic accuracy and speed, particularly in radiological imaging.
However, limitations in technological infrastructure and system integration were found to hinder its optimal use.
Furthermore, the readiness of human resources remains a critical factor.
Although there is optimism among medical staff, a lack of technical training has led to gaps in understanding and utilization.
Ethical and legal concerns also emerged, especially regarding responsibility in case of misdiagnosis and the protection of patient data.
The absence of specific regulations and digital ethics protocols presents a major barrier to AI adoption.
This research concludes that while the implementation of AI in medical diagnostics shows promising outcomes, it still faces institutional and regulatory challenges.
Strengthening digital literacy among healthcare workers, developing standard operating procedures, and building a secure infrastructure are essential.
Collaboration between hospitals, academic institutions, and government bodies is needed to create an inclusive and ethical AI-based healthcare ecosystem.
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