The Healthcare industry is gradually succumbing to its ailments. The American healthcare industry was worth $24.7 billion in 1960. It is now valued at $3.5 trillion. Receiving care in a hospital for a heart attack will cost you an average of $20,246 in the US, and the expense might actually cause you another.
In 2014, the lowest-income countries spent an average of $120 per person annually; in 2040, it goes up only to $195. Countries like Somalia spend as little as $33 per person. Being an expensive item in national budgets, getting the best value for money or getting the best outcome for the citizenry is arduous.
According to WHO statistics, worldwide life expectancy in low-income countries was 18.1 years lower than in high-income countries in 2016. The healthcare system is falling prey to its own ambush like inflation rates, dropping life expectancy and byzantine procedures in acquiring care.
The Revolution of AI in Healthcare:
Artificial Intelligence has revolutionary benefits like real-time assistance, personalization, and automation, which can bring transparency in the medical workflow.
In 2017, artificial intelligence in medicine was valued at $719 million, globally. Now, it is estimated to reach $18.1 billion at a CAGR of 49.6% by 2025. Once a fable in science fiction is now a reality in modern-day healthcare.
AI can analyze complex medical data and improve the efficiency of drug discovery. It can also help to manage clinical trials approximating human cognition. Inflation, scarcity of skilled workers, and time-consuming redundant tasks are major growth factors for AI in healthcare. Powered by increasing availability of healthcare data, artificial intelligence is bringing a paradigm shift in the healthcare industry.
How is Healthcare Harnessing AI Capabilities?
Automation of redundant tasks:
An ample amount of time and medical staff is wasted in filling up and maintaining patient documents.
“End-user organizations adopt RPA technology as a quick and easy fix to automate manual tasks,” said Cathy Tornbohm, vice president at Gartner. Automating such admin tasks would not only mean significant time saving but also provide transparency in the workflow.
AI in Radiology:
In radiology, qualified physicians visually examined medical images to detect, characterize, and diagnose diseases. With recent advances in AI, non-deterministic, deep learning algorithms have demonstrated remarkable improvement in perception and interpretation of complex data and image recognition tasks.
Such features are designed to quantify specific radiographic characteristics, such as the 3D shape of a tumour or it’s intratumoral texture and distribution of pixel intensities.
AI in Cancer diagnosis:
Skin cancer, the most common human malignancy is primarily diagnosed visually, beginning with an initial clinical screening followed by dermoscopic analysis, a biopsy and histopathological examination. Owing to the fine-grained variability in the appearance of skin lesions it is quite difficult to classify. However, deep convolutional neural network (CNN) when trained end-to-end using a vast dataset of images, using only pixels and disease labels as inputs can achieve performance on par with all tested experts.
IBM Watson-Oncology and Microsoft’s Hanover Project are a real-life implication of AI in cancer detection.IBM Watson-Oncology has picked up drugs for the treatment of cancer patients with equal or better efficiency than human experts. Microsoft’s Hanover Project at Oregon has analyzed medical research to tailor personalized cancer treatment option.
AI in Drug Selection:
Creation of a new drug can take several years and billions of dollars. AI systems can help improve the efficiency of drug discovery and easily manage clinical trials. Training a neural network with the results of past attempts can rule out the need to test every combination. It can guide new treatment discovery processes and speed up the drug selection process.
Real-Life use of this was a program trying to find new therapies for Ebola through drug repurposing conducted by University of Toronto startup Atomwise.
Remote Patient Monitoring:
AI replacing radiologists and pathologists may be an exaggeration. However, one can expect AI as part and parcel of healthcare.
AI in health care is bringing about a paradigm shift. https://t.co/5YvC3r0vZC
— Mantra Labs (@Mantra_Labs) August 13, 2019
Robot doctors are saving lives in Canada. In the provinces of Saskatchewan, where people live hundreds of kilometres from medical care, robots equipped with a stethoscope, electrocardiogram, and powerful cameras perform the initial screening. These robots can also perform ultrasound tests in real-time; thus helping doctors to examine patients remotely.
Modus V is a robot which assists doctors to detect sensitive areas like nerves and blood vessels during surgery. Heartlander, the already 12-year-old, inch-long robot, offers stabilization, sensing, and enhanced access with minimal discomfort during surgery.
We help startups and enterprises, build & scale AI-driven products and solutions.
Reach out to us on firstname.lastname@example.org to learn more.