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Potential Of Using AI In The Healthcare Industry

AI is becoming more advanced at doing what humans do, but more efficiently, quickly, and inexpensively. AI and robotics have enormous potential in healthcare. AI and robotics are increasingly becoming a part of our healthcare ecosystem, just as they are in our daily lives. 

Artificial Intelligence (AI) has the potential to revolutionise healthcare delivery. It has the potential to increase productivity and efficiency in care delivery, allowing healthcare systems to provide more and better care to more people. AI can help improve healthcare practitioners’ experiences, allowing them to spend more time on direct and reducing burnout. We need a large number of Medical Dataset to build effective and efficient AI healthcare models.

What is AI in healthcare?

The application of machine learning (ML) algorithms and other cognitive technologies in medical contexts is referred to as AI in healthcare. In its most basic form, Artificial Intelligence (AI) occurs when computers and other machines replicate human cognition and are capable of learning, thinking and making decisions or taking actions. The use of machines to analyse and act on medical data, usually with the objective of predicting a specific outcome, is what AI in healthcare is all about. 

Because of its fundamental role in a productive, thriving society, healthcare is one of the most critical sectors in the wider landscape of big data. The application of AI to healthcare can literally mean the difference between life and death. AI can help doctors, nurses, and other healthcare professionals with their daily tasks. AI in healthcare can improve preventive care and quality of life, generate more accurate diagnoses and treatment plans, and result in overall better patient outcomes. By analysing data from government, healthcare and other sources, AI has the potential to play an important role in global public health as a tool for combating epidemics and pandemics. 

AI technologies that can help in the healthcare industry

1. Machine Learning: Machine learning is a statistical technique for fitting models to data and ‘learning’ from data through training models. One of the most common types of AI is machine learning.  Precision medicine is the most popular application of classical machine learning in healthcare, predicting which treatment protocols are likely to be successful on a patient based on numerous patient variables and the treatment. The vast majority of machine learning and precision medicine applications require AI Training Dataset with known outcome variables (eg. disease onset), this is known as supervised learning. 
2. NLP: Natural Language Processing or NLP encompasses applications such as speech recognition, text analysis, translation, and other linguistic aims.  The most common use of NLP in healthcare involves the generation, comprehension, and classification of clinical documentation and published research. NLP systems can analyse unstructured clinical notes on patients, create reports (for example, on pathological exams) transcribe patient interactions, and perform conversational AI. 
3. Rule-based expert system: Expert systems based on collections of ‘if-then’ rules were the leading AI technology in the 1980s, and they were widely employed commercially at the time. They were widely used in healthcare for ‘clinical decision support’ purposes over the last few decades, and are still actively used today. Today, electronic health record(EHR) providers include a set of regulations in their systems. 
4. Physical robots: Physical robots are well-known at this time, with over 200,000 industrial robots installed globally each year. They carry out predefined duties such as lifting, relocating, welding, or assembling goods in settings such as factories and warehouses, as well as transporting supplies in hospitals. Surgical robots, first  allowed in the united states in 2000, give surgeons “superpowers” by enhancing their capacity to sight, make precise and less invasive incisions, stitch wounds, and so on. However, important judgements are still made by human surgeons. Gynecologic surgery, prostate surgery, and head and neck surgery are all common uses for robotic surgery. 
5. RPA: This technology conducts organised digital administration duties, such as those involving information systems as if they were human users following a script or set of rules. When compared to other types of AI, they are less expensive, easier to programme, and more transparent in their actions. Robotic Process Automation (RPA) does not actually include robots, but rather a computer applications running on servers. 

To operate as a semi-intelligent user of the systems, it combines workflow, business rules, and ‘presentation layer’ interaction with information systems. They are employed in healthcare for repetitive operations such as prior authorization, updating patient information, and billing. They can be used in conjunction with other technologies, such as image recognition, to extract data from faxed photographs and feed it into transactional systems. 

How AI can transform the Healthcare industry?

AI is transforming the healthcare business in a variety of ways, some of which are as follows:

1. Early Detection: AI is already being used to detect diseases more precisely and in their early stages, such as cancer. Consumer wearables and other medical devices, along with AI, are also being used to monitor early-stage heart disease, allowing doctors and other caregivers to better monitor and diagnose potentially life-threatening episodes at earlier, more curable diseases. 
2. Diagnosis: Deepmind health at Google is collaborating with clinicians, researchers, and patients to solve real-world healthcare problems. The technique blends machine learning and systems neuroscience to create neural networks that imitate the human brain with powerful general-purpose learning algorithms. 
3. Decision making: Improving care necessitates the integration of big health data with appropriate and timely decisions, and predictive analysis can help support clinical decision-making and actions while also prioritising administrative tasks. 
4. Research: One of the more recent applications of AI in healthcare is drug research and discovery. It is possible to significantly reduce both the time to market for more drugs and their costs by directing the latest advances in AI to streamline the drug discovery and drug repurposing process. 
5. Training: One of the more recent applications of AI in healthcare is a drug discovery and research. By directing the latest advances in AI to streamline the drug discovery and drug repurposing processes, there is the potential to significantly reduce both the time to market for new drugs and their costs. 

How can GTS help you?

We understand that you require premium medical datasets as well as secure data services to meet your machine learning algorithm requirements. We create training data at Global Technology Solutions to provide the necessary support for medical datasets. We provide Image Data Collection services such as data annotation, tuberculosis x-ray dataset, data collection, and protected transcription to support your healthcare datasets for machine learning.