It’s no secret that artificial intelligence has experienced a boom over the past five years, as companies have openly developed numerous algorithms tailor-made for applications in various industries. One such industry is healthcare, where immense data capture and governance occurs every moment of every day globally. This reality has forged strong bonds between the healthcare world and AI developers. What does the healthcare AI landscape look like, and why does this revolution matter?
Assessing the State of the Healthcare AI Market
The total market for healthcare AI reflects just how enthusiastically companies are developing related solutions. Experts predict that global healthcare AI revenue will eclipse $34 billion, while the use of 22 different tools will generate $8.6 billion annually by 2025. New tools have spawned over time while existing tools have grown more robust. This improved functionality goes a long way towards winning professionals over, ultimately boosting adoption.
The emergence of healthcare AI has advanced the medical industry in these noteworthy areas:
Diagnostics and Privacy
Intel Labs has been a major developer of healthcare AI for some time, having partnered with Penn Medicine earlier this year to advance a joint Brain Tumor AI project. This technology uses databases of patient information to provide diagnoses with 99% accuracy. That level of success is staggering, yet it doesn’t happen overnight. Diagnostic AI can’t move immediately from development to implementation, considering its use case is so sensitive. A diagnosis of a major illness carries a lot of weight for patients, and thus accuracy is of the utmost importance.
Engineers prepare AI algorithms for prime time by exposing them to training sets — large samplings of data used to pinpoint algorithmic strengths and weaknesses. The AI framework is improved until it delivers exceptional results that match expectations. Researchers used the International Brain Tumor Segmentation database in this instance, though we can expect similar procedures for other medical conditions.
The Brain Tumor AI Project also illustrates how intelligently-applied AI can safeguard patient privacy. Data mobility involved in artificial intelligence can pose security risks unless it’s done in a federated fashion. AI models of old required institutions to transport data offsite to remote locations—often a centralized database. Transit is vulnerable, but placing one’s eggs in one database basket carries its own concerns. What if this central hub of information suffers from a breach? HIPAA and other regulations in the healthcare field levy stiff penalties for privacy failures.
The federated approach to AI in healthcare lets the models travel (as opposed to the data), keeping medical institutions in control of information without exposing personal patient information. This methodology will gain plenty of steam in the coming years.
From time to time, and especially during a pandemic, people can’t always seek care effectively. Also, consider that seniors and dependent individuals cannot easily find transportation to doctors’ offices, clinics, or hospitals. AI allows healthcare providers to bring care to households. Patients can useContinue reading