The implementation of artificial intelligence technology is expanding in a variety of healthcare sectors. Machine learning methods are being investigated at all stages of the therapeutic route, from pre-surgical risk assessment and intraoperative management through complications and outcome prediction. In light of the rapid expansion of ai implementation, it is critical to understand how these technologies might be leveraged to provide safer, more practical, and cost-effective care.
Ai has contributed to substantial progress in decision-making, quality of care, and precision medicine, but several legal and ethical issues need to be addressed before its widespread application in clinical practice. For the uninitiated physician, ai should be viewed as a revolutionary statistical instrument that, unlike traditional statistics, derives its source material from real-world databases and registries rather than idealized cohorts of patients and is capable of processing massive volumes of data. In this sense, ai is intended to aid decision-making rather than replace critical thinking or surgical skill development. As with any new technology, the therapeutic utility of ai must be demonstrated and verified against existing methodologies.
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