Читать книгу Smart Systems for Industrial Applications - Группа авторов - Страница 37

AI, together with AR, has vast clinical and surgical applications in healthcare. The unsupervised models allow the system to recognize the patterns followed by the initiation of the algorithm based on previous patterns. In addition, reinforcement learning algorithms use positive and negative rewards or punishments in their learning methodologies [30]. Whether the relationship between input and output is linear or not, the programs go through more decision-making layers to deduce a mathematical rule to create outputs based on specific inputs. The disciplines of medicine that rely on deep learning that include radiology and pattern recognition have become more precise than human intervention methods [31]. Deep learning algorithms are applied in finding out malignancy and improving neonatal imaging and neurologic imaging qualities.

The AI models are used to forecast the readmission and delayed discharge [32]. There are various lung cancer models used to aid in the prediction, diagnosis, and planning of treatments [33]. The prediction of survival rate after surgery is modeled for cervical cancer patients [34]. From applications like simple prognostic tools to big and complex models, AI is used. There is also a saying that AI models are superior to traditional regression models for outcome prediction [34]. All the way, virtual AI is yet to reach its high potential in gynecology. There are various opportunities that exist to improve the treatment and diagnosis, especially in gynecologic oncology.