What is clinical setting
Lets discuss Safety in Clinical Setting.. Clinical setting means the location or area in which the training of nursing students occurs or clinical practice of nursing student materializes. Clinical setting might be a doctor’s clinic, emergency ward, hospital department or any other site where the clinical practices achieves. A medical student requires the training of medical practices followed by experiments. There is a high risk of human error in nursing the clinical practices as well as in experiments including diagnosis, treatment and surgery.
How AI can be beneficial for its safety measure
Clinical setting consists of many steps including training, practice, diagnose, treatment, surgery and care of the patients. All these steps require the human skills and retain the high risk of human error. AI play an important role in the clinical setting. A rapidly accelerating number of academic research studies have demonstrated the various applications of AI in healthcare, including algorithms for interpreting chest radiographs, detecting cancer in mammograms, analyzing computer tomography scans, identifying brain tumors on magnetic resonance images, and predicting development of Alzheimer’s disease from positron emission tomography.
AI models have the potential to condense unwarranted variation in clinical practice, improve efficiency and prevent avoidable medical errors that affects almost every patient during his life. By providing novel systems and tools to support patients and augment healthcare staff, AI could enable better care to the patient in the community. AI tools could support patients in playing a better role in managing their own health, primary care physicians by allowing them to confidently manage a greater range of complex disease, and specialists by offering superhuman diagnostic performance and disease management.
How AI remove many risk
But the question arises in the user mind with the name of AI is “How the system will accurate and safe. AI had removed the many risk factors of clinical setting in many areas. As the clinical setting involves different task like the training, practice, diagnose, treatment surgery and care. By using the augmented reality (branch of AI), training of nursing students can be achieved in virtual environment without the involvement of patient. Students can also train by giving the mistaken surgery example and train them on different situation.
In this situation there is no risk of wasting the human life. Students also practice the training example in virtual environment. as the biological data is much complex, so there is difficult to analyze it manually to diagnose the disease of the patient. If the genomic or medical imaging data will be analyzed manually then there is much chance of false diagnose. AI can manipulate the genomic, medical imaging and sequencing data without any risk of poor diagnose. Treatment and surgery are the fundamental step of clinical setting that require the trained neurosurgeons and proper surgical instruments. By the burden of many operation and multiple task, it is not possible for the neurosurgeons to properly concentrate on surgery. AI can be trained a system that guide the nursing student or neurosurgeons. Also, the autonomous robot can do the surgery, although it requires some directions from neurosurgeon.
Challenges of Safety in Clinical Setting
The safe and timely transformation of artificial intelligence inventions into appropriately regulated and clinically validated system is still challenging. Many factors involve in the transformation of AI research into clinical system like the data we used for training the system is differ from the real-world data. Also, the validation measures of the system often do not imitate the clinical applicability. Like the accuracy of the system and area under the curve (validation measure) do not reflect that the system will be clinically applicable. Moreover, the clinical data is not readily available for the training of the AI system. Data often soiled and need to limped or bring into appropriate format which is very difficult. After removing all the challenges, the human adoption barriers are substantial.
Applications of Safety in Clinical Setting
Diagnosis of Disease:
As the biological data become increasing to date, so it is difficult to analyze the biological data and diagnose the disease of the patient. Biological data analysis task is very time consuming and requires the human knowledge. AI playing an important role in bioinformatics, medical image segmentation, and genomics. By using the medical complex and highly amount data with AI, the diagnose of disease, tumor, disease type and many other diagnoses can be achieved without any surgery and human risk.
Drug recommendation system:
the advancement in medical field id very fast but the proper use of advancement is not satisfactory. Sometime it is due to the abuse of knowledge. As the abundant of medicine have been discover but the standard of prescribe drug is not maintained. AI can accurately prescribe the medicine without any human risk and false diagnose.
Autonomous robot surgery:
Among other rapidly developing medical spheres, robotic surgery is another achievement in clinic practices. The Autonomous robot is the fully trained system that automatically perform surgery of the patient without the involvement of neurosurgeon. Autonomous robot has ability to learn, navigate and successful surgery
Virtual medical assistance:
a real-life medical problem is virtual medical assistance that get the symptoms of the disease and prescribe the disease or other health care tips. It can solve the problem of bed shortage in hospitals. It also helps the people that have insufficient health care facilities.
Conclusion of Safety in Clinical Setting
AI playing important role in many areas of clinical setting and also overcome the risk factor of human error. By the collaboration of medical data and artificial intelligence there have many advancements that serve the people. But the safe and timely transformation of artificial intelligence inventions into appropriately regulated and clinically validated system is still challenging. Robust clinical evaluation, clinical metrics rather than the technical accuracy is essential for the artificially intelligent system for clinical settings. Human adoption behavior should be considered at time of planning the AI model for clinical settings. To accelerate the use of artificially intelligent model in clinical setting, there is need to design the clinically validate the system and easily regulated in medical field.
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