Do you care about artificial Intelligence? What about machines learning and cognitively “thinking” for themselves; perhaps replacing humans in certain parts of the healthcare sector? If you don’t care now, you might care soon. As there is clear evidence that artificial intelligence is a surging market. In fact, developments in AI technology are opening new possibilities in nearly every sector in nearly every industry.
Simply put, AI is becoming a big deal.
According to Mercom Capital Group, artificial intelligence and data analytics companies raised $1.1 billion in 2017 leading funding in all other digital health categories. As much as $419 million went into the development of AI. Overall, funding for digital health totaled nearly $7.2 billion on 778 deals, up 42 percent from $5.1 billion and 622 deals in 2016. Worldwide corporate funding for health IT firms was $8.2 billion in 2017 versus $5.6 billion the prior year.
Data analytics companies also led in mergers and acquisitions, with 21 deals in 2017, according to the report. Practice management solutions companies had the next largest number of transactions with 19, followed by mHealth apps with 17, and telemedicine with 16.
In healthcare, there are still questions about AI that need answers, per Venture Beat:
- Will AI-focused approaches actually drive more accurate diagnoses and improve healthcare?
- How quickly will regulatory bodies and payers approve and adopt these approaches?
As machine learning and AI are rapidly commercialized for healthcare, we’re going to see more investment, and more practical use in the space. According to the Mercom report, artificial intelligence in healthcare is a major area of investment.
Even the federal government realizes the benefits of AI technology in healthcare, stating in a recent publication that “AI is beginning to play a growing role in transformative changes now underway in both health and healthcare." However, the publication also lists significant challenges in the field, which include:
"...the acceptance of AI applications in clinical practice, initially to support diagnostics; the ability to leverage the confluence of personal networked devices and AI tools; the availability of quality training data form which to build and maintain AI applications in health; executing large-scale data collection to include missing data streams; in building on the success in other domains, creating relevant AI competition; and understanding the limitations of AI methods in health and healthcare applications."
There is potential for a proliferation of misinformation that could cause harm or impede the adoption of AI applications for health. “Websites, apps and companies have already emerged that appear questionable based on information available. Methods to insure transparency in disclosure of large scale computational models and methods in the context of scholarly reproducibility are just beginning to be developed in the scientific community,” the report says.