Which type of AI is currently being used in healthcare today?

20-Apr-2023

AI and Machine learning is the key drivers facilitating groundbreaking advances in Healthcare, business, and several other Industries. The integration of AI in the healthcare industry has been revolutionary, transforming and reshaping how healthcare personnel make diagnoses, monitor, and treat patients. Artificial Intelligence enhances the medical field by enabling healthcare professionals to bring about accurate diagnostics and personalized therapies. AI in healthcare is largely helpful in the quick analysis of massive amounts of clinical documentation that allows medical and healthcare providers to detect symptoms and trends that would otherwise go unnoticed. 

AI in healthcare has an array of possible uses, from scanning radiological images for early diagnosis to predicting outcomes from electronic health information. The healthcare industry, without a doubt, is becoming faster, smarter, and more efficient in providing treatment all across the globe. The superior the innovation, the more advanced and efficient treatment facilities are.  Let us go into the details of how AI in healthcare is changing the landscape and how it is increasingly used in Healthcare today. 

Major Uses of AI in Healthcare

1. Deep Learning and Neural Network for Predictive Diagnosis

The most pervasive application of classical machine learning in healthcare is precision medicine which significantly helps in predicting the type of treatment protocols that are likely to be successful on a patient based on the context of aggregated patient traits and treatment procedures. Neural network technology has been well-established in the healthcare industry for several decades and has been used for categorization applications such as predicting whether a patient will get a specific disease. Recognizing possibly malignant tumors in radiography pictures is a typical application of deep learning in healthcare. 

Deep learning is rapidly being used in radiomics, or the discovery of clinically significant patterns in imaging data that go beyond what the human eye can see. Oncology-oriented image analysis frequently employs both radiomics and deep learning. A combination of deep learning and radiomics promises a higher level of accuracy than just providing computer-aided detection,  generally known as CAD.

2. Surgical Robots

Developed to perform predefined tasks, physical robots have been largely employed and becoming more collaborative with humans. Robots are also becoming more intelligent as more sophisticated AI capabilities are implanted into their operating systems and are trained to work by streamlining through an intended task. This intervention is also significantly used in the healthcare industry. The uses of a physical robot powered by AI in healthcare are pervasive. These robots have brought about disruptive transformations in the healthcare sector, from analyzing data from large medical records to facilitating minimal or non-invasive surgeries. They are capable of providing insight by analyzing data from past treatment processes. The innovative surgical robots assist healthcare professionals by enhancing their capacity to sight, make precise and less invasive incisions, stitch wounds, and so on. A good number of robotic surgery are now being implemented in numerous surgeries including head and beck, gynecology, and prostrate surgeries. 

3. Virtual Nursing Assistants

AI application in the healthcare industry not only gives innovative techniques but also helps save huge resources and investments. The arrival of virtual nursing assistance has facilitated easy interaction with patients and directed them to the most effective treatment and care setting. This service is available round the clock, answers any queries in real time, monitors patients, and enables more regular communication between patients and care providers. The introduction of the Virtual nursing method can significantly cost cut up to US$20 billion annually as they help patients do away with unnecessary hospital re-visits and readmissions. 

4. More Automated and Systematized Administrative Tasks 

The administrative section also experiences a significant impact with the incorporation of AI in Healthcare. The traditional administrative desk jobs are automated to a great extent and have more efficacy. It has become more of a necessity. For instance, in the US caregivers or nurses, spend about 25 percent of their productivity and work time on regulatory and administrative tasks. The AI-powered assistance can be leveraged in a range of healthcare applications, such as claims processing, order testing, administering drugs, making chart notes, clinical recording, revenue cycle management, and medical records management. The healthcare industry can save up to US$18 billion by automating administrative duties.

5. Enhanced Clinical Judgement and Diagnosis

AI in Healthcare offers far-reaching and promising use cases. One of the most crucial interventions of AI in healthcare is its potential to provide predictive analysis in medicine. The growing concern across nations centered around the high prevalence of cancer-related deaths can now be easily mitigated by leveraging the predictions projected by AI revolutions. Healthcare organizations and several other companies that specialize in the detection and treatment of certain tumors make recommendations based on their genetic profiles. Healthcare practitioners find it complicated and time taking to comprehend all cancer genetic variants and their responses to novel medications and regimens. This predicament in the medical field has been largely improved by the implementation of AI in healthcare.

Importance of Machine Learning in Healthcare

Just as disruptive as AI is, Machine learning is equally significant for the growth and advancement of the healthcare industry. Machine learning and Deep learning being part and parcel of Artificial Intelligence are remarkably important and by large leverage to make healthcare facilities more efficient and develop precise inventions for curing diseases. 

Machine Learning in Healthcare is particularly critical for its algorithms that assist healthcare businesses and professionals in making sense of the huge sets of healthcare data aggregated on daily basis via electronic health records. It also assists in discovering patterns and insights in medical data that would be impossible to discover manually.

With the widespread use of machine learning in healthcare,  healthcare professionals are equipped with the sophistication of taking a more predictive approach to precision medicine, resulting in a more unified system that consequentially enhances care delivery, better patient outcomes, and more efficient patient-based operations. 

Numerous remarkable high-level instances of machine learning and medical concepts are applied in medicinal research and study today. Some of the most common applications of Machine learning in the healthcare sector  and highly beneficial for healthcare professionals are: 

• Automating medical billing

• Clinical decision support 

• Advanced clinical practice guidelines

Some Benefits of Machine Learning

• Improving diagnosis

• Developing new treatments

• Enhanced Drug discovery 

• Improving clinical trials

• Reducing costs

• Improving care

The healthcare industry will continue to see a lot of growth in the next coming years as ML and AI continue to become more sophisticated, leading to more innovations.

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