Healthcare Datasets Are Now The Key factor For Medical Advancements


Introduction

Have you seen a feature in the apple watch that can take the ECG of a person with the help of an app? This data can be further used by a doctor to monitor your heart health. How amazing is that? Who would have thought that a watch on your wrist can help monitor your heart? All of these and more can be achieved through AI and machine learning. 

The impact of artificial intelligence in healthcare is increasing day by day and it is not slowing down anytime soon. This is because AI can help in many areas including drug discovery, diagnosis, and more. The future of AI in healthcare is bright enough, and many healthcare companies have already started to work and implement it. In this article, we will learn about what is AI in healthcare, what areas can be improved through AI, and how it is used in healthcare, application, and more. 

What is AI in Healthcare?

AI in healthcare is a term that is used to describe the applications of the machine and deep learning algorithms to understand, interpret and take actions using medical data. Basically, the use of AI is to analyze the data and act on it, to predict a particular outcome.

AI can process a huge amount of patient data and it can help doctors deliver more accurate diagnoses and treatment plans. To train the AI to perform these functions, and more, there is a need for top-notch medical datasets. AI can help doctors predict what treatment or procedure is likely to be successful based on their makeup and treatment framework.

Areas in which AI can improve

Here are areas which can be improved by using AI.

Medical Diagnosis: In medical diagnosis, artificial intelligence helps in decision making, management, automation, administration and workflow. It can be used to diagnose cancer, triage critical imaging findings, and acute flag abnormalities, assist radiologists in prioritizing life-threatening cases, diagnose cardiac arrhythmias, predict stroke outcomes, and assist with chronic disease management. 

Drug Discovery: Small-molecule drug discovery can benefit from AI in four ways: access to new biology, improved chemistry, higher success rates, and a faster and cheaper discovery process. Many challenges of traditional R&D can be addressed through AI. 

Clinical trials: To gather, normalize, analyze, and harness the growing masses of data that fuel modern therapy development, AI-powered capabilities such as data integration and interpretation, pattern recognition, and evolutionary modelling are required.

Artificial intelligence (AI) enables innovations critical for transforming clinical trials, such as seamlessly combining phase I and phase II trials, developing novel patient-centered endpoints, and collecting and analyzing Real-World Data.

Pain management: Pain management is a common focus, and scientific studies show that virtual reality can help patients with acute pain reduce stress and anxiety.

Imagine a child has to take an injection. And if the child will move too much or make noises, it can distract the doctor, also the child will experience pain. In order to combat this, the child can wear a VR headset that distracts them from the pain and they won’t be experiencing too much pain.


How is AI used in the healthcare field?

These are some ways AI is used in the healthcare field. 

Patient care and operations: Artificial intelligence is on track to completely transform healthcare in the future. We can expect to see dramatic changes in patient health outcomes and hospital operational efficiency as AI is integrated into the work of both medical professionals and hospital systems.

Improvement in Health outcomes: Every day, doctors and nurses use patient consultations, lab test results, imaging scans, and other data to make dozens of critical decisions about patient care. We can expect AI to be used more frequently in the future to scan this data, compare it to hundreds of thousands of other cases, and make diagnostic and treatment recommendations.

Preventive tool: AI's applications in inpatient care are already numerous, with experts predicting that AI will help doctors diagnose and treat a variety of illnesses, injuries, and diseases. Another emerging medical application of AI is in preventive medicine. Many exciting examples of AI being used as an early preventive intervention tool by researchers already exist, such as detecting type 1 diabetes, discovering Alzheimer's disease indicators, and predicting breast cancer.

Decision Support: AI in healthcare could be useful in clinical decision support, allowing doctors to make better decisions faster by recognizing patterns of health complications far more accurately than the human brain.

In an industry where the time taken and decisions made can have life-changing consequences for patients, the time saved and conditions diagnosed are critical.

Information management: AI in healthcare is a great addition to physician and patient information management. Patients can save time and money by getting to doctors faster, or not at all, thanks to telemedicine. This will help relieve pressure on healthcare professionals and increase patient comfort.


Applications of AI in healthcare

AI in future can completely change the way a patient is monitored, and treated. It will also help in post care of the patients. But for now, here are some applications of AI in healthcare

Managing medical records: Data management is the most widely used application of artificial intelligence and digital automation in health care because compiling and analysing information (such as medical records and past history) is the first step. To provide faster, more consistent access, robots collect, store, re-format, and trace data.

Analyzing reports: Robots can do tests like analyzing X-rays, CT scans, data entry, and other routine tasks faster and more accurate. Cardiology and radiology are two disciplines where analysing large amounts of data can be difficult and time-consuming. In the future, cardiologists and radiologists should only look at the most complicated cases that require human supervision.

Custom treatment plan: Artificial intelligence systems have been developed to analyse data – such as notes and reports from a patient's file, external research, and clinical expertise – in order to assist in the selection of the most appropriate, individually tailored treatment path.

Virtual nurses: Between doctor visits, virtual nurses can assist patients in monitoring their condition and following up on treatments. Programs like these use machine learning to assist patients.

Drug creation: Clinical trials can take more than a decade and cost billions of dollars to develop medicines. Making this process faster and less expensive has the potential to change the world. During the Ebola virus, an AI-powered programmed was used to scan existing medicines for new ways to combat the disease.

The program discovered two medications that may reduce Ebola infectivity in just one day, compared to months or years for similar research – a difference that could save thousands of lives.

How can GTS help you?

When it comes to developing applications and algorithms that can detect, and localize problems like cancer, we at Global Technology Solutions understand that you need quality datasets to train, test and validate the model. 

That’s why we provide high quality, and accurate video, text, image and voice datasets. Our data is custom made for each solution and each dataset is vigorously checked by our Quality assurance team. For your needs related to data collection and annotation of different data, you can contact us. 

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