Artificial Intelligence is steadily making itself useful in healthcare. Yet, the potential is far from its peak. AI in healthcare still needs to go through hoops— especially when data privacy, security, and confidentiality are concerned. And despite the instances of era-defining success, these roadblocks have been hindering holistic adoption.
In this discussion, we cover those points while focusing on the argument favoring healthcare-specific AI implementation. Once there, we will also talk about how AI can benefit the otherwise established healthcare industry by primarily focusing on the compliance part of things:
Here are the three key takeaways:
- The success of AI in healthcare is determined by the availability of accurate and voluminous training data. Once datasets are abundant, the algorithms and subsequent models come out better.
- AI models, even in healthcare, need to be trained to eliminate the prevalent bias. The idea would be to access diverse data sets, adding to the sample size. Additionally, data diversity also takes care of the localized licensing bottlenecks.
- Companies planning healthcare AI models should consider data de-identification to eliminate the PHI (Personal Health Information) and PII (Personal Identifiable Information) guardrails.
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