Artificial intelligence (AI) has become the latest technology to upend the status quo. However, unlike other industries, healthcare’s adoption and implementation of AI is still in its infancy, in part, due to many providers still updating their systems and processes for the new technology. Nonetheless, the momentum is building and AI and the Internet of Medical Things (IoMT) is poised to revolutionize the healthcare industry.
A recent analysis from Accenture shows growth in the AI health market is expected to reach $6.6 billion by 2021 and key clinical health AI applications can potentially create $150 billion in annual savings for the US healthcare economy by 2026. Additionally, Harvard Business Review found the application of AI to administrative processes could add a potential annual value of $18 billion by 2026.
Based on what we know so far, it’s clear AI can provide the healthcare industry with a unique opportunity to not only offer tools and insights that can vastly improve patient care, but that also improve their bottom line. AI has the power to see patterns in research studies, detect ailments faster and provide more in-depth education.
However, despite all the benefits and advantages of AI, some providers remain skeptical and hesitant to implement solutions, and are concerned about the challenges of AI in the healthcare industry.
First rule of AI in medicine: Do no harm Since AI relies mainly on data collection, if the data isn’t accurate, the AI solution is blamed. In healthcare, AI solutions that rely on deep learning capabilities can lead to incorrect patterns being identified and thus incorrect diagnoses – such as false positive results. Despite these trepidations, providers who are pro-AI argue that this technology is actually much faster and more accurate than humans and only provides us with even more opportunities to succeed and streamline tasks.
Other AI fears relate to job loss – much like the argument made across all industries against AI. Automation of processes will certainly make some roles obsolete, but for many positions within healthcare and caregiving, machines and computers will be responsible for one role, not the many hats worn by healthcare providers. Take radiologists for example. Deep learning solutions can help them identify areas of interest within a scan, but that’s not all radiologists do. AI solutions are simply a supplement to their duties and can allow them to spend more time focused on patients and providing value-based care.