How AI is set to transform patient outcomes and surgical practices

By Pooja Toshniwal PahariaMay 15 2024Reviewed by Lily Ramsey, LLM In a recent review in Nature Medicine , researchers investigated using artificial intelligence in surgery, highlighting improvements in preoperative, intraoperative, and postoperative care.Background AI is making significant advances in healthcare, including foundation model architectures, wearable technologies, and surgical data infrastructures.

Preoperative surgical applications of AI Artificial intelligence is utilized in surgical planning and patient selection, notably in preoperative imaging, to aid in early diagnostic evaluations and planning. Model-free methods for reinforcement learning have shown potential for detecting and removing tumor tissue while limiting the effects on functioning biological tissues during neurosurgery.

Diagnosis is a highly developed aspect of surgical artificial intelligence, with model generalizability and accuracy finding early clinical use. Task-specific breakthroughs provide more precise tumor staging, potentially improving surgical planning. AI in surgical decision-making helps enhance surgical resection margins, shorten operating times, and increase efficiency. A recent patient-agnostic transfer-learned neural network uses quick nanopore sequencing to provide accurate intraoperative diagnoses in under 40 minutes.

While there has been progress in decreasing postoperative duration of stay, enabling early discharge, and facilitating functional recovery using minimally invasive-type surgical methods, early return to routine activities, improved postoperative evaluation, and early warning provision systems, data-based innovations are lacking in the postoperative period.

Source: Tech Daily Report (techdailyreport.net)

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