3 hurdles healthcare AI should strive to clear this year
Want to see AI in healthcare? Look anywhere. Want to see AI underperforming in healthcare? The same directive applies.
The challenges facing the ubiquitous if still overhyped technology get a tidy untangling in Dataconomy by Ralph Tkatchuk, a freelance data security consultant. He points out three particular areas in which healthcare AI in its various iterations needs further maturing and more training:
1. Digitizing and consolidating data. Noting how hard it can be to marshal datasets stored in widely dispersed siloes that don’t speak to one another, Tkatchuk suggests stakeholders work harder at finding ways to “improve data consolidation and digitization so that medical data can be properly processed and analyzed by AI.”
2. Updating regulations. Privacy and confidentiality laws are good and necessary, but ensuring strict compliance can unfortunately thwart AI developers and implementers who might otherwise be able to drive great value into U.S. healthcare. “Regulatory bodies must implement rules that will help protect identities and allow healthcare providers to acquire high-quality data to allow their AI technologies to process data,” Tkatchuk writes. “Likewise, medical institutions must do their due diligence to comply with these regulations and be accountable in how they obtain patient data.”
3. Involving Humans. From patients wary about machines directing their care to providers worried about getting “replaced by robots,” concerns continue to swirl around the very concept of AI in healthcare. Overcoming such anxieties “is key to building an AI-driven healthcare system,” Tkatchuk asserts. “There must be a full understanding that AI only serves to augment the diagnostic capabilities of healthcare practitioners. This will encourage everyone to embrace AI-assisted medical practices.”
Read the whole thing at Dataconomy: