**4.3 AI for progress monitoring**

Monitoring is required, useful, and remotely done is advantageous. Technology is useful for all this. Fast data transmission for favourable timely actions is the ultimate aim. AI can make all this expertly efficient.

#### *4.3.1 Future progress*

The advancements of biomedical sensing and healthcare information technology have resulted in data-rich environments in hospitals. Analysis of all this data for actionable in sights is possible with AI. AI systems need to be developed for real time fast alerts to mitigate crisis leading to increased patient safety. The shortage of doctors, long duty hours for monitoring, right doctors for right patients etc will be all benefited. *Prevention Strategies for Patient Safety in Hospitals: Methodical Paradigm, Managerial… DOI: http://dx.doi.org/10.5772/intechopen.106836*

#### **4.4 AI for prevention applications**

Clinical deterioration in the hospital is common and has to be energetically assessed and prevented.

ICU treatment involves multitude of data, both clinical and laboratory investigations. The Pediatric Logistic Organ Dysfunction (PELOD) score is based on the relative severities among Organ Dysfunctions (ODs) and the degree of severity of each OD using logistic regression [54]. A machine learning model, using random forest classifier, has achieved better performance than this [55]. Ensemble learning algorithms, used by advanced practitioners of machine learning, are useful with high final accuracy. Hence in matters of health these should be utilized.

#### *4.4.1 Future progress*

Future work should focus on inferring and predicting based on new categories of data, including biometric sensors like continuous telemetry, motion activity sensors, novel biomarkers, and relevant patient-reported measures [49].

#### **4.5 AI for professional standards**

Excellence & competency ensures correct treatment, free from errors and side effects. AI augmentation of human performance is likely to be of widespread use [56]. AI technology can provide decision support to clinicians seeking to find the best diagnosis and treatment for patients [57]. This coupled with doctor's competence should lead to professional standards and patient safety of highest order.

#### *4.5.1 Future progress*

Systematic analysis for sophisticated advancements is regularly required [58]. Machine Learning has capabilities of reading, processing, and interpreting the available data (structured and unstructured) at enormous scale and volume. New evidence can be synthesized, all for patient treatment perfect.

New evidence is accumulating at a fast pace. It is difficult for doctors to keep pace. Systematic Reviews and Meta-Analysis requires humongous efforts. AI applications can be developed for automated Systematic Reviews and Meta-Analysis. Concise results of these will be useful for all. Progress features promise exciting future [59].

AI has the potential to revolutionize the teaching and practice of Surgery. The ways for this are (i) AI analysis of population and patient-specific data for improvements in each phase of surgical care (iii) AI enabling and making easy access of surgical experience repositories for sharing of knowledge. This includes collection of massive amounts of operative video and EMR data across many surgeons. This will lead to a future optimized for the highest quality patient care [56].
