In the past few decades, Artificial Intelligence (AI) has been playing a significant role in the world. Most people do not even realize how artificial intelligence in healthcare is impacting our lives in different forms.
Online shopping, Logging into social media accounts and your email, to car ride services, everything includes AI algorithms to improve user experience.
One major field in which this technology is growing rapidly is healthcare, particularly in the treatment and diagnostic management.
There is fear that this technology will surpass human abilities and tasks. At the same time, AI in healthcare can assist in clinical decisions, enhance treatment efficiency, and support the human judgment.
Artificial Intelligence in Healthcare
This technology is helping in different ways in the medical field. It uses a web database enabling doctors and practitioners to access millions of diagnostic resources.
As the medical professionals have deep knowledge in the field and are aware of the current research, artificial intelligence in healthcare significantly enhances a faster outcome that exactly matches with their clinical education.
In the clinical setting, the technology fears eventually reducing or replacing the need for human physicians. However, it only enhances and benefits decision-making and diagnostics, and nothing can replace the need for an actual doctor.
There are times when the patient shows different symptoms and can correlate with multiple conditions by physical and genetic characteristics.
These situations can delay a diagnosis. In this scenario, artificial intelligence in healthcare can help doctors in terms of providing qualitative and quantitative data according to input feedback.
It also assists in improving accuracy in early detection, diagnosis, efficiency, outcome prediction, and treatment plan.
Artificial Intelligence holds the capability to learn from the data, which offers the opportunity for improved accuracy according to feedback responses. There will be input from doctors, research institutions, practitioners, and many back-end database sources in the feedback.
The system in which artificial intelligence in healthcare industry always operate in real-time, which means all the medical data is always up to date, enhancing relevance and accuracy.
The data includes electronic recordings from different medical devices, physical examinations, laboratory images, and different demographics. It also contains different medical notes.
With the information updating in real-time, medical professionals have unlimited resources to increase their treatment capabilities.
AI Machine Learning Offers More Targeted Diagnosis
There is a large amount of medical data in the field, so artificial intelligence in healthcare needs to sort out through the presented data to learn and build a network more effectively.
There are two types of data you’ll see in the field; structured and unstructured.
There are three different types of techniques in structured data, including Modern Deep Learning, Machine Learning Techniques (ML), and a Neural Network System. Contrary to that, all unstructured data uses Natural Language Processing (NLP).
Modern Deep Learning:
Modern Deep Learning goes beyond the surface of machine learning. It takes the same inputs as ML but feeds them into a computerized neural network.
The neural network is a hidden layer that further files the data to a more simplified output. It aids healthcare professionals who might have multiple possible diagnoses narrow down to one or two outcomes, allowing them to make a more tactile and absolute conclusion.
Machine Learning Techniques (ML):
Machine Learning techniques pull out particular patient traits using analytical algorithms. These traits include all the data that is collected during a patient visit with the doctor.
The traits such as medications, basic metrics, diagnostic imaging, different laboratory testing, physical exam results, symptoms, disease-specific data, and gene expressions all comprise the collected structured data.
Using machine learning, you can determine patient outcomes.
Supervised learning is another common type of Machine Learning used in clinical settings. It uses the physical traits of a patient backed with a database of information to provide a more targeted outcome.
Neural Network System
Neural Networking technology was used in a breast cancer diagnostic process. In this process 6,567 genes were sorted and paired with textured information that was incorporated from the subjects’ mammograms.
The combination of physical characteristics and logged genetic enabled a more specific tumor indicator outcome.
Natural Language Processing (NLP)
Natural Language Processing (NLP) is similar to structured data processes but focuses on all the unstructured data in the clinical setting.
The unstructured data is from documented speech and clinical notes to text processing when a healthcare professional sees a patient. It includes narratives from exam summaries, physical examination, and laboratory reports.
Natural Language Processing utilizes historical databases having disease-related keywords assisting in the decision-making process for a diagnosis.
These processes offer a more effective and precise diagnosis for a patient, which speeds up the treatment process and saves time for the practitioner. The specific, faster, and more targeted the diagnosis, the sooner a patient is on the road to recovery.
Integration of Artificial Intelligence in Major Disease Areas
As neurological disorders, cancer, and cardiovascular diseases are among the top causes of death these days, it has become crucial to utilize as many resources as possible to aid in early diagnosis, detection, and treatment.
Implementing artificial intelligence in healthcare pinpoints any risk alerts a person may have and benefits in early detection.
In one research, AI algorithms were used in patients with risk for stroke on their genetic history and presented symptoms to place them in an early detection stage.
The detection stage collected data based on movements. Whenever there is any abnormal physical movement in the patient, it would trigger an alert. The triggered alert allows doctors to get patients to a CT scan or MRI sooner for disease evaluation.
The early detection alert in the research provided 87.6% accuracy in a prognosis evaluation and diagnosis. It means that the doctors are able to carry out treatment earlier and predict whether the patient had a greater possibility of stroke in the future.
Similarly, ML was used 48 hours in post-stroke patients to predict whether the patient could have another stroke or not, offering a prediction accuracy of 70%.
Telehealth – AI On a Smaller Scale
Medical professionals use Artificial Intelligence in healthcare on a larger scale for high-risk diseases, but telehealth tools are being used in the homes of patients to prevent these high-risk situations and treatment. It reduces hospital readmissions.
Telehealth tools help in taking, processing, and documenting different metrics. They notify the doctor immediately when there’s a high-risk variable. Offering faster diagnosis, updated treatment plans, and early detection, these tools save money and time both for the hospital and patient.
While offering more immediate care for patients.
Conclusion
The future of Artificial Intelligence in healthcare industry are interconnected. Artificial Intelligence in healthcare has a far-reaching power in everything from drug discovery to mobile training solutions.
It empowers doctors to make more logical and efficient decisions, bettering patient care as a whole.
To implement the artificial intelligence in healthcare organization, you need to consult a reputable vendor such as Xavor Corporation.
Xavor offers cutting-edge tech solutions intersecting technology and healthcare, delivering quality solutions, products, and innovations to patients and enterprises across the globe.
