The Covid-19 Pandemic has turned our world upside down and has taken a major toll on almost every aspect of our lives. While it severely impacted things negatively, it also made some positive changes in our society. For instance, it accelerated the digital transformation like never before. As a result, every industry today is more technology-centered. And now, there’s no stopping to this!
For obvious reasons, the healthcare industry is at the center of all industries. Not too long ago, as the contagious coronavirus was killing people around the globe, the healthcare sector was tirelessly working to minimize the damage. During all this hustle and bustle, telehealth became a necessity, and this led to the increased use of artificial intelligence
in the medical field.
"The promise of artificial intelligence in medicine is to provide composite, panoramic views of individuals’ medical data; to improve decision making; to avoid errors such as misdiagnosis and unnecessary procedures; to help in the ordering and interpretation of appropriate tests; and to recommend treatment."
Eric Topol, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
In recent months, the role of artificial intelligence in the medical field has been one of the hottest talking points, and the adoption of this tech is not showing any signs of slowing down. As AI can make medical care more human with improved accuracy and efficiency, the healthcare industry
is counting on AI healthcare applications for making better decisions, managing patient data, developing customized medicine plans, discovering new drugs, and much more.
Here are some common use cases of artificial intelligence in the medical field.
Current Examples of Artificial Intelligence in the Medical Field
Most of the current AI healthcare applications revolve around NLP (natural language processing), RPA
(robotic process automation), and pattern recognition. The following are well-known examples of how artificial intelligence is used in healthcare.
AI is a transformational force when it comes to primary care. With millions of annual patient visits in the US alone, primary care has the biggest need for AI implementation to ease the burden. The following are the major primary care areas where AI is already generating amazing advantages:
Medical advice and triage
Coaching for digital health
Managing medical practice
Resourcing and paneling
"We should think about AI the way we think about patient care — as a continuum, spanning care areas and disease states.”
Dr. Mark Michalski, Executive Director of MGH and BWH Center for Clinical Data Science
According to the data from Health Leaders Media, the total expenditure on potentially preventable medical conditions is $1 out of every $10. With accurate risk management, hospitals can save billions of dollars.
A real-time example of this is the AI-powered BaseHealth technology implemented by Banner Health in Phoenix, Az. It carries out primary care interventions and helps the hospital management predict around 42 medical conditions. This reduces emergency room utilization to a great extent.
Drug Development & Discovery
Discovering new drugs and taking them to market is a lengthy and troubling process. In a study conducted by BIO (Biotechnology Innovation Organization), it was found that 9 out of 10 clinical drugs don’t make it to the trials and even fewer reach the FDA approval stage. This drives the cost of drug discovery and development to a very high level and unfortunately, these costs are often passed down to ordinary people.
Thankfully, AI can be used to slice down the expenditure required in the R&D of drugs with effective drug discovery, development and even marketing
. The biggest example of this is the collaboration of Toronto-based biotech firm Cyclica and Bayer AI to arm pharma operations with AI and cloud platforms to improve the overall efficiency of drug R&D.
CAD (computed aided detection) was the first AI-powered tool used in radiology. Today, AI has come a long way in the radiology field and offers several benefits to radiologists. The following are the AI healthcare applications for radiologists:
Hidden fracture detection
Neurological abnormalities detection
Breast cancer detection
Brain tumor classification
Furthermore, AI algorithms can help radiologists offer a second opinion. New York-based Mount Sinai Health System utilized AI medical diagnosis alongside a human expert as another option for a second opinion for novel coronavirus detection.
Future of AI in Healthcare
The amount of research on artificial intelligence in the medical field is increasing rapidly. The following are the (at least) predictions as to which trends will be the hottest trends when it comes to future AI in the healthcare space.
Medical Diagnosis of AI
Diagnosis prediction is one of the key areas that will experience AI’s forceful impacts. Machine learning models in the future will enable AI medical predictions to predict medical conditions at their onsets or much before they actually exhibit themselves.
Thanks to machine learning-based models, in the future, people will be able to receive hospital-like treatments in the comfort of their homes. There will be advanced versions of AI chatbots that will be able to provide medical guidance to patients and also receive information from them about their symptoms.
Electronic Health Records (EHR)
Even though electronic health records
are not completely related to artificial intelligence, they have a critical share in AI’s role in healthcare in the future. EHRs are nothing but the digital version of doctors’ notes about the patient. It has data related to patients' 10-year medical history, health journey, and diagnoses. In a way, they are huge databases that can be used with AI and Machine Learning techniques to extract and gather critical patient data.
In a nutshell, healthcare is one of the most important sectors that artificial intelligence will undoubtedly revolutionize. The industry is already set to invest trillions of dollars in collaboration with AI, which clearly indicates the level of impact AI will have on the healthcare universe.
Frequently Asked Questions
What are the most common challenges to applying artificial intelligence in the medical field?
Appropriate strategies are necessary to overcome the following common challenges you may face while applying AI in the medical field:
Identifying potential use cases
Training staff and patients
Getting rid of the black box
What are the key benefits of AI in healthcare?
Doctors and patients agree that artificial intelligence is beneficial. The following are the key benefits of AI in healthcare:
Enhancing healthcare accessibility
Accurate clinical decision-making
Which ethical dilemmas do you encounter while applying AI in healthcare?
The following are the ethical and legal dilemmas you need to think about when applying AI to healthcare:
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