Why healthcare facilities are better in United States. Because of shortest wait times, better access to preventive care and care for serious diseases. All the money put in the research and technology, US has the world’s best cancer survival rates, best longevity after age 80 and better heart attack survival rates than average comparable countries.
Today in this article i will try to add details about how AI and ML playing big role in healthcare industry.
AI and ML bringing evolution in healthcare industry in many places like medical/medicine industry, clinical field and specialities. Using Artificial Intelligence, Machine Learning, Natural Language Processing and deep learning enables medical professionals and stake holders to identify healthcare needs and fine faster solutions with more accuracy. ML and Data patterns helps them to make informed medical and business decisions.
Many people use this umbrella term “AI in Healthcare” but what it is really? AI is when machine which mimic human cognition, and capable of learning, thinking, making decision and take appropriate actions. AI in healthcare is, use computer to analyze medical data, act according to machine learning model which helps to predict a particular outcome. It is basically describes applications of machine learning algorithms and other cognitive technologies in the healthcare or medical environment.
Healthcare is one of the most important industry, it is also in the broader landscape of big data because of its fundamental role in a productive, thriving society. Use of AI in healthcare data is really matter of life and death, because many life’s depend on accurate results. AI in healthcare can enhance preventive care and quality of life, produce more accurate diagnoses and treatment plans, and lead to better patient flow. AI can also predict and track the spread of infectious diseases by analyzing data from a government, private, healthcare, and other sources. As a result, AI can play a crucial role in global public health as a tool for combatting epidemics and pandemics.
AI helps to gather, analyze large (big data) amounts of data stored by healthcare organization in the form of images, research trials, medical claims, samples. It helps to identify patterns and insights often undetectable by manual human skills. AI algorithms are “taught” to identify and label data pattens, while natural processing allows these algorithms to isolate appropriate information. Using deep learning this data gets analyzed and interpreted with the help of ML model.
Machine learning models has been used to analyze the large sets of data from databases. High processing CPUs or supercomputers have been used to predict molecular structure which potential medicines would and would not be effective for various diseases. Using convolutional neural networks technology was able to predict the binding of small molecules to proteins by analyzing hints from millions of experimental measurements and thousands of protein structures.
This process helps convolutional neural networks to identify a safe and effective drug candidate from the database searched, reducing the cost of developing medicine.
In today’s cloud technology world, smart devices are taking over the consumer environment, offering everything from real-time video from the inside of a refrigerator to self driving cars.
In the medical environment, smart devices are critical for monitoring patients in the ICU. Using AI to enhance the ability to identify deterioration, suggest the sepsis is taking hold, or sense the development of complications can significantly improve outcomes and also helps to reduce long term cost.
“When we’re talking about integrating disparate data from across the healthcare system, integrating it, and generating an alert that would alert an ICU doctor to intervene early on – the aggregation of that data is not something that a human can do very well,” said Mark Michalski, MD, Executive Director of the MGH & BWH Center for Clinical Data Science.
Using intelligence algorithms into the medical devices can reduce cognitive burdens form doctors and physicians while ensuring the patients receive quality care in as timely a manner as possible.
Unstructured data in the healthcare industry comes from different sources like patient records, images, lab reports, scanned copies of reports. Health data and medical records of patients are stored as complicated unstructured data, which makes it difficult to interpret and access. Doctors, Physician often face difficulty to stay updated with the latest medical advances, while providing quality patient-centered care due to huge amounts of health data and medical records. Electronic health records/data and biomedical information curated by medical units, medical professionals can be quickly scanned by machine learning models to provide prompt, reliable answers to clinicians.
AI can seek, collect, store and standardize medical data regardless of the format, assisting repetitive tasks and supporting clinicians with fast, accurate, processed step-by-step treatment plans and medicine for their patients instead of being loaded under the weight of searching, identifying, collecting and transcribing the solutions they need from piles of paper formatted electronic health records..
During a sudden heart attack, the time between the 911 call to the ambulance arrival is crucial for recovery. For an increased chance of survival, emergency dispatchers must be able to recognize the symptoms of a cardiac arrest in order to take appropriate measures. AI can analyze both verbal and nonverbal clues in order to establish a diagnostic from a distance. By analyzing the voice of the caller, background noise and relevant data from medical history of the patients alert emergency staff if it detects a symptoms of heart attack.
Emergency dispatchers are able to identify a cardiac arrest based on the description provided by the caller around 70% to 75% of the time. But AI can do better. A small-scale study conducted in 2019 revealed that ML models were able to recognized cardiac arrest calls better than human dispatchers by using speech recognition software, ML and other background clues.
ML can play an essential role in supporting emergency medical staff. In the future medical units could use the technology to respond to emergency calls with automatic defibrillators equipped drones or with CPR-trained volunteers, which would increase the chances for survival in cases of cardiac arrest that take place in the community.
Turning electronic health record into an AI-driven predictive tool allows physician to be more effective with their workflows, medical decisions and treatment plan. Machine learning and Natural language processing can read the entire medical history of a patient in real time, connect it with symptoms, chronic affections or an illness that affects other members of the family. They can turn the result into a predictive analytics tool that can catch and treat a disease before it becomes life-threatening.
These details are used alongside EHRs as a source to generate clinical insights for medical professionals, allowing for data-driven decisions to improve patient outcomes. This solutions have already been applied in several high-risk diseases such as renal failure, pneumonia, congestive heart failure, hypertension, liver cancer, diabetes, orthopedic surgery and stroke, with the stated objective to lower costs for patients and clinicians by assisting in early and accurate diagnoses of patients.
AI adoption in healthcare have its own challenges, such as lack of trust in the results delivered by an ML and the need to meet specific requirements. However, the use of AI in health has already brought multiple advantages to healthcare stakeholders. By improving workflows and operations, assisting medical and non-medical staff with repetitive tasks, helping users in finding faster answers to inquiries, and developing innovative treatments and therapies, patients, payers, researchers and clinicians can all benefit from the use of AI in healthcare.