SAWERA HAİDER
Eurasian Journal of Family Medicine - 2023;12(3):171-172
Dear Editor Artificial Intelligence (AI) has the potential to revolutionize healthcare by making it more accessible and adaptive. The use of AI technologies, including large language model tools (LLMs), offers exciting possibilities for improving health outcomes and supporting healthcare professionals, patients, researchers, and scientists. However, it is crucial to approach the integration of AI in healthcare with caution, taking into consideration the lessons learned and potential risks highlighted by experts. One of the key considerations raised in the field of AI in healthcare is the potential for biased data used to train AI systems. Biased data can lead to the generation of misleading or inaccurate health information, exacerbating existing disparities and hindering equitable access to care. To mitigate this, it is important to ensure that AI systems are trained on diverse and representative datasets, reducing biases and promoting inclusiveness and equity (1). Ensuring the reliability and accuracy of AI-generated responses, particularly in LLMs, is another critical aspect that requires attention. Although LLMs can produce responses that appear authoritative and plausible, there is a risk of these responses being completely incorrect or containing serious errors, especially in the context of health-related information. Rigorous evaluation, expert supervision, and transparent quality assurance mechanisms are necessary to ensure the reliability of AI-generated insights and prevent potential harm to patients (2,3) The protection of sensitive health data and the preservation of patient privacy are paramount in the development and deployment of AI technologies. It is crucial to establish robust consent procedures, implement secure data storage practices, and prioritize data protection measures. Striking the right balance between data accessibility and privacy protection is essential to maintain public trust and ensure the responsible use of AI in healthcare (4,5).
