When I began my journey into entrepreneurship in the late 1990s, I was attracted to major industries poised for disruption, thanks to the advent of technology such as the Internet. The people I admired and admired at the time – Jeff Bezos, Pierre Omidyar, and Elon Musk–brought a different perspective to the industry they changed. Inspired by these men, I spent several years launching and growing travel businesses before focusing my attention on furniture for homes around ten years in the past.
In my head, I’ve always been fascinated by the field of healthcare due to its enormous scale and impact on society. Healthcare is the only industry that impacts everyone as profoundly and as directly as healthcare. Healthcare makes up nearly 20% of U.S. GDP and more than 4 trillion dollars in annual worldwide spending.
The healthcare industry is also a highly personal field despite its size, with a lot of individual interactions between clinicians and patients and an endless array of administrators. Healthcare is a private affair that adds additional concerns like the privacy of data, rights for patients, and legal responsibility to an already complex field. If entrepreneurship is built upon learning about an industry to improve it, then the initial education curve for healthcare professionals is steep and intimidating.
Recently I met a gifted group of entrepreneurs who can take on some of the most challenging issues in healthcare. I’m becoming knowledgeable about the new business, just as I was at home and traveling. My course has made healthcare’s enormous magnitude and complexity evident, and it came to me that I might benefit from writing about my observations and lessons learned.
Let’s Start With Artificial Intelligence
There are many exciting aspects of healthcare that we can write about. However, I’d like to begin at the beginning with AI (AI), which is right at the crossroads of healthcare technology and technology. It offers a huge opportunity to increase the quality of healthcare for patients and reduce the cost of care. AI isn’t an unimportant buzzword but an entire range of computing technology designed to speed up and replicate human tasks. Machine Learning (ML) is a unique AI technology described as complicated statistical models to analyze vast quantities of unstructured and structured information to provide quick and insightful information that would require humans an extremely long time to process and present on their own.
Consumers have observed how AI and ML allow Big Tech to leverage data to predict what information or products, or services we could require. The most innovative businesses have been developed by AI and ML in various ways, including Google Ads to Amazon’s Alexa. AI needs data in the same way that humans require oxygen to grow and achieve their full potential. While the massive quantity of personal data stored by Big Tech may be scary but there’s no doubt that the results have become an essential element of our internet-based life.
Current Concerns And Uses Of AI And ML In Healthcare
AI, as well as ML applications for improving healthcare, are promising. However, results have been mixed since some of the algorithms used have been shown to produce inaccurate or flawed results. In some cases, the information presented as AI is, in reality, much more mundane, where humans are behind the scenes acting as the brains (often at offshore places).
However, advances in managing databases, digital imaging, and ML present convincing reasons to be optimistic. While we’re in the beginning stages, there has been a positive application of AI in fields like radiology. For this, researchers have created ML algorithms that use the powerful abilities of visual analysis of the latest computers to find potential instances of breast cancer that doctors may miss.
Today, increases in computing power enable companies like Google to diagnose patients using brutal methods for doctors on the human side to duplicate. For instance, Google now uses advanced machine learning to analyze vast amounts of patient data, including vital indicators like smoking history, age patterns, and even retina scans, to provide exact risks of heart disease that doctors cannot match.
An assessment using ML using clinical data could help doctors make better decisions in other scenarios. For instance, advanced ML is now able to handle a lot of the calculations used for cardiac MRI environments and other fields of expertise, meaning that radiologists do not have to compute the results manually, resulting in a faster diagnosis of patients with greater accuracy.
Now Come The Questions
These innovations, though fascinating, also raise questions within an already complicated business. Are there ways for AI and ML to improve the standard of medical treatment in areas that are not well-served and have the least number of doctors? How can AI and ML be monitored and controlled when humans aren’t able to recreate the strategies that are used? Who is responsible when an algorithm goes wrong and causes tragic consequences? In the long run, are AI and ML an addition to human doctors, or are they a substitute?
The questions are beginning to be addressed since the FDA recently approved specific algorithms for use in clinical trials without the supervision of a doctor. However, in these instances, the company that sponsors them is legally responsible for any mistakes. Imagine a possible courtroom battle where the defendant isn’t even a physician but an algorithm. It’s not exactly high-stakes, but who knows, maybe an imaginative screenwriter will develop an antihero of the future.
