A Study on Ethical Considerations in Automated Lung Ultrasound Analysis

Artificial intelligence has transformed lung ultrasound analysis in medical imaging, enhancing both accuracy and efficiency. However, this technological evolution introduces a complex array of ethical challenges that are pivotal to address. The use of AI tools in healthcare raises concerns about bias, accountability, transparency, and patient privacy. This paper provides an in-depth exploration of these ethical issues, particularly emphasizing how biases in AI algorithms could potentially lead to disparities in healthcare outcomes. It examines the difficulties in attributing responsibility for misdiagnoses in the context of AI, highlighting the blurred lines between clinician and algorithmic decision-making. Furthermore, the paper addresses the critical need for transparent AI systems that remain interpretable to healthcare professionals, balancing the intricacies of deep learning models with the necessity for clarity in medical decision-making. Privacy concerns are also paramount, given the sensitive nature of patient data used in training and implementing these AI systems. This paper proposes an ethical framework for using AI in lung ultrasound diagnostics, based on a thorough review of current practices and research. This framework emphasizes interdisciplinary collaboration and the development of standardized guidelines to ensure ethically responsible integration of AI, thereby safeguarding patient trust and upholding the quality of care in the era of digital medicine.