In a world where hackers relentlessly prey on healthcare providers, and cryptocurrency becomes synonymous with ransom, AI rises to the challenge to help ransomware victims fight back. In part 1 of the AI fights back series: Halt, Quo Vadis? (Who Goes There?) we examined vulnerabilities that allow nefarious hackers to penetrate healthcare institutions and the treasure troves of sensitive and broad clinical and financial patient data that they store. In part 2, we will explore battleground protocols and a new weapon (AI) to be used in the fight against hackers.
In a world where hackers relentlessly prey on healthcare providers, and cryptocurrency becomes synonymous with ransom, AI rises to the challenge to help ransomware victims fight back.
Ceci n’est pas une pipe (This is not a pipe): OCR (Optical Character Recognition) progress and pitfalls for healthcare
Recognizing objects and patterns in medical images or documents can be fraught with misinterpretation and errors. Distinguishing the meaning of words that are represented by pixels can be even more challenging because words must be first identified, then processed, then understood with the right context and knowledge of that word. While OCR (Optical Character Recognition) has come a long way since Ray Kurzweil’s OCR computer program in the 1970s, it will take continuous improvement in Artificial intelligence (AI) and Machine learning (ML) to advance this technology further.
In the 1984 Joe Dante film Gremlins, a cute and gentle creature can turn into a nightmarish monster if you don’t precisely follow the care instructions. Training a healthcare AI algorithm, although not quite as dramatic, can give its creators cold sweats nonetheless.
While Speech Recognition was a definite and necessary building block that made Natural Language Processing (NLP) ultimately possible, equating it with NLP is like comparing a 1921 Model T Ford with a 2021 model, self-driving Tesla.