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Startup adjusts medical voice assistant for a Zoom world - MedCity News


As more physicians are taking their practices online, software companies have also had to adjust their services. One example: Saykara, a startup developing an AI voice assistant to automatically fill health records, had to shift its platform to Zoom. In early March, Saykara celebrated a milestone when its AI voice assistant was able to operate autonomously, meaning for some specialties, it could automatically update patient records and notes without any clicks or voice commands. But a few weeks later, the Seattle-based startup had to quickly adjust to a new world where most appointments are being conducted online. "Things were growing every day until we had the hiccup of Covid thrown in there," said Dr. Graham Hughes, president and COO of Saykara.

Cleveland Clinic Targets Telemedicine, Big Data and AI to Improve the Future of Care


The Cleveland Clinic has a history of being on the bleeding edge of health IT and its new CEO Tom Mihaljevic has made it clear that the Ohio-based health system will keep pushing ahead as a medical technology pioneer. "Most of our plans for the future will depend on digital platforms: telemedicine, data analytics, artificial intelligence," Mihaljevic said during the State of the Clinic address in late February. "Digital technology will allow us to deliver smarter, more affordable and more accessible [care]. The Cleveland Clinic has always been an early adopter, beginning with our electronic medical records. But now, we have to take technology even more seriously.

Xconomy: Healthcare's Future is Telemedicine & AI, But Will Everyone Benefit?


John Halamka thinks the digital health industry is still "emerging." But it has come a long way and is starting to deliver after years of hype. Halamka, a Boston-based physician and healthcare technology expert, says that's thanks to several coalescing factors: improved technology, more favorable financial incentives for using digital products in healthcare, and growing demand from patients accustomed to tech-enabled convenience in other areas of their lives. "In 2019, the tech is better, but also the alignment of incentives is better," says Halamka, who leads innovation at Beth Israel Lahey Health system and is a Harvard Medical School professor. Over his 30-year career in medicine, Halamka has had a front row seat to advances in healthcare technology, and at times has helped drive them.

Artificial Intelligence for Social Good: A Survey Artificial Intelligence

Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform [1]; email writing becomes much faster with machine learning (ML) based auto-completion [2]; many businesses have adopted natural language processing based chatbots as part of their customer services [3]. AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports [4] to games such as poker [5] and Go [6]. All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" [7]. Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.

Multimodal Machine Learning for Automated ICD Coding Machine Learning

This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD-10 codes. Key evidence was also extracted to make our prediction more convincing and explainable. We used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.