![]() He was also Vice President of Big Data Services at Oracle from 2014 to 2016. Suki AI’s CTO used to work at Salesforce, which is certainly doing AI with its Einstein product, but he does not seem to have worked on Einstein. Suki AI does employ a Senior Machine Learning Engineer who seems to have business experience in machine learning, but we were unable to find employees with similar credentials on the company’s LinkedIn page. Startups can’t often focus on more than one niche when it comes to building machine learning models, and business leaders should be skeptical of AI vendors claiming to offer robust machine learning software for more than three verticals. In fact, the talent at the company, $2 billion in revenue, and 8,000 employees likely mean that they have the resources to train and update their voice recognition systems. This isn’t to say that Nuance’s voice recognition system is worse than other systems from companies that focus on the healthcare domain. What this means is that it is both resource and time-intensive to train voice recognition systems, making it difficult for companies to offer software for it to a variety of industries. ![]() Only in doing this can the machine learning model behind the voice recognition system “learn” to transcribe jargon medical jargon. In addition, these subject-matter experts would be required to correct the software as it transcribes the jargon incorrectly or fails to transcribe it altogether and feed those corrections back into the natural language processing algorithm. Machine learning engineers looking to build a voice recognition system for use in hospital and clinical settings will likely need to recruit subject-matter experts to provide audio data for the algorithm that involves jargon and commonly used phrases in those settings. Similarly, natural language processing models need to be trained on specific word and phrases, technical terms and argon. We explain this in further detail in our report on Crowdsourced Natural Language or Speech Training – Use Cases and Explanation. If a machine learning model built for a voice recognition system is fed only audio data from people with Boston accents, for example, the voice recognition system might have trouble picking up commands when they’re said by someone with a different accent. Natural language processing algorithms often require specificity in the way they’re trained. Ideally, a company sells into one or two niches, tailoring their software to specific use cases. This makes them stand out from the other companies listed in this report. That said, Nuance Communications offers natural language processing software to a variety of industries, not just healthcare companies. This is generally what we look for when it comes to vetting a company on their claims to leveraging artificial intelligence and cutting through the marketing hype we so often see on AI vendor websites. Nuance employs many data scientists with PhDs and Master’s degrees in computer science and hard sciences like physics. It isn’t clear how exactly their solutions could work without natural language processing, a kind of artificial intelligence. We can’t seem to find evidence that the prominent companies offering speech recognition software for medical transcription have what we would expect in terms of talent at their company, except for Nuance Communications. Machine Learning for Medical Transcription – Insights Up Front We found that these solutions are intended to help hospitals and healthcare companies with medical transcription in different forms, transcribing speech into text in order to fill out and update patient medical records in electronic health record (EHR) and electronic medical record (EMR) databases. There are several companies claiming to offer AI-based medical transcription software, specifically speech recognition software, to hospitals and healthcare companies.
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