The healthcare industry today faces various issues regarding cost, revenue, and quality. These issues are on a rise and have pushed the industry to try out new technologies to overcome these issues. Many healthcare institutes are turning to artificial intelligence (AI) to aid them in better decision making. It is believed that AI will help them deliver a faster turnaround of insights with little scope of errors.
The use of artificial intelligence will ease the lives of patients, doctors, and hospital staff by performing meticulous and mundane tasks that are usually performed by us humans, in a fraction of the time and the cost.
It was forecasted that the AI market in the healthcare industry is bound to grow from $4.9 billion in 2020 to $45.2 billion by 2026. It is also projected to grow at a CAGR of 44.9% during this period. The major factor that has propelled AI to grow in the healthcare sector is the ever so increasing volume of healthcare data and rising complexities of datasets. Also, the inevitable need to reduce the healthcare cost, enhancing computing power when there is a sinking hardware cost, an increasing number of cross-industry collaboration and partnership, and the rising disproportion between healthcare staff and patients, have driven the need for technology intervention to improve healthcare services.
AI has multiple purposes and can be used to find new links between genetic codes or to operate surgery-assisting robots. AI in a way is reinvigorating and reinventing healthcare through machines that can learn, comprehend, predict and act.
Let us now dig deeper into how does artificial intelligence helps in health care?
Efficiently Diagnoses And Decrease Errors
One of the most promising areas where AI can help healthcare is in the process of diagnostics. In the year 2015 medical errors and misdiagnosing illness were estimated for 10% of all the deaths in the United States.
When dealing with large case data and incomplete medical histories can lead to fatal human errors. AI is immune to all these factors and can easily predict and diagnose diseases faster than any medical staff. A study revealed that the use of an AI model utilizing algorithms and deep learning diagnosed breast cancer faster than the combined work of 11 pathologists.
Accurate Cancer Diagnosis
PathAI a Cambridge, Massachusetts based company is in the process of developing ML (Machine Learning) technology to aid pathologists in making accurate diagnoses. Presently the company is pursuing the goal to decrease error in cancer diagnoses and developing ways for individualized medical treatment.
PathAI has collaborated with drug manufacturers like Bristol-Myers Squibb and institutions like the Bill & Melinda Gates Foundation to extend its AI technology in various healthcare industries.
Efficient In Checking Symptoms
Buoy Health came up with an AI-powered symptom and cure checker. The company used algorithms to diagnose and treat illness. Buoy Health uses chatbots that listen to a patient’s health concerns and symptoms, then step-by-step guides the patient to the correct care based diagnosis.
Harvard Medical School to name a few, who have started using Buoy’s AI to identify, diagnose and treat patients quickly.
Deep Learning For Actionable Insights
Enlitic, a San Francisco, California based company developed a deep learning medical tool that can streamline radiology diagnoses. Enlitic’s deep learning solution can thoroughly analyze unstructured medical data like blood tests, radiology images, EKGs, patient’s medical history, genomics, etc. to give the doctor more reliable insights into a patient’s health.
Also, Enlitic was named the 5th smartest AI Company in the world by MIT, ranking above Microsoft and Facebook.
Early Cancer Detection
Freenome, a San Francisco, California based company is using AI in the screening process, blood work, and diagnostic cancer tests. It can do this by using AI at general screenings. Freenome’s goal is to detect cancer in the primitive stages and consequently develop new treatments.
Diagnosing Deadly Blood Diseases Faster
Beth Isreal Deaconess Medical Center a Harvard University‘s teaching hospital is using AI to diagnose likely fatal blood disease at a primitive stage.
Doctors are using AI-powered microscopes to scan and detect harmful bacterias like staphylococcus and E. coli in the blood samples. The process is eventually faster than manual scanning. Around 25,000 images were taken to teach the machine how to search for bacteria. Ultimately the machine learned how to identify and predict dangerous bacteria in blood with a staggering 95% accuracy.
Ai-powered Radiology Assistant
Zebra Medical Vision, a Shefayim, Israel based company came up with AI-powered assistance for radiologists. This assistance receives imaging scans and automatically analyse them for multiple clinical findings it has studied. Then the findings are passed onto radiologists, who consider the reports while making a diagnosis.
Biopharmaceutical Development
BioXcel Therapeutics, based out of New Haven, Connecticut, used AI to recognise and develop new medicines in the field of neurosciences and immuno-oncology. The company also is into a drug re-innovation program that employs AI to identify new patients or find a new application for existing drugs.
BioXcel Therapeutics was named the Most Innovative Healthcare AI Developments of 2019″ for their work in AI-based drug development.
Treatment of Rare Disease
BERG, based out of Framingham, Massachusetts is a clinical stage. Its AI-powered biotech platform maps diseases to hasten the identification and development of breakthrough medicines. It combines its “Interrogative Biology” method with traditional R&D, BERG recently exhibited its findings on Parkinson’s Disease treatment at Neuroscience 2018 conference. BERG used AI to discover connections between chemicals in the human body that were previously unknown.
AI and Cloud-based Digital Drug Discovery
XtalPi’s, based out of Cambridge, Massachusetts, combined AI, the cloud, and quantum physics to create XtalPi’s ID4 platform. It can predict the chemical and pharmaceutical properties of small-molecule candidates of drug design and development. XtalPi also claims that their crystal structure prediction technology can predict complex molecular systems in a matter of days, not a week or months.
Companies like Google, Sequoia Capital, and Tencent have invested in XtalPi. If you are looking for a custom healthcare software development company to develop a solution for your organization, do visit us
Conclusion
People say that AI would replace humans. But it is so increasingly clear that AI systems will not replace humans on a large scale in the healthcare sector. However, AI is said to augment our efforts to care for patients. With time maybe the healthcare staff may move towards the duties and jobs designed to draw on uniquely human skills like persuasion, empathy, and big-picture integration.
