Element RegNav’s Post

Do you know that by 2030, AI in the healthcare industry is forecasted to reach $188 billion? The future of the healthcare industry is booming with AI-powered tools, but training them demands high-quality data annotation. This process comes with its own set of challenges. Let’s explore the biggest hurdles in the healthcare annotation industry. #cappersoft #dataannotation #datalabeling #textclassification #imagelabeling #videoannotation #textannotation #audioannotation #documentprocessing

Stop leaving money on the table! 💰 Maximize your rev cycle and boost E/M coding accuracy with Calm Waters AI. Experience healthcare innovation at its finest. Sign up for a risk-free trial now and unlock your practice's full potential: https://bit.ly/3wiMa58 #CalmWatersAI #HealthcareInnovation #healthcareAI #revenuegrowth #calmwatersai #EfficientEMCoding #AdvancedHealthcareAI

I just wrote an article addressing the challenges of medical data annotation for training AI models in Healthcare. Check it out to learn more about this critical aspect of healthcare technology. #HealthcareAI #MedicalDataAnnotation #AIinHealthcare #ArtificialIntelligence #data

To develop AI in healthcare, it is very crucial to consider patient' s data confidentiality. One way of to keep the data P&C, the developer create a process where the data/image is tagged/labelled/coded at the institution where the data is originated. My question is how you ensure the data that was labelled is not conflicted with other institution? One institution may labelled/coded the data started from point A but another institution may put the same data started from point B, and it may have different code that will produce different outcome. We have to know that the data is present as 3D images.

I've been reflecting on the transformative power of machine learning (ML) in medicine and wanted to share some insights. Unlike traditional hand-coded rules, ML brings adaptability, efficiency, and scalability to medical data analysis. But, as with any powerful tool, it's not without challenges. The biggest? Data bias. How we train our models can unintentionally embed existing biases, affecting outcomes. It's a reminder that while ML can revolutionize healthcare, it's essential to approach it with caution and awareness. Let's embrace the tech but stay vigilant about its pitfalls. #MachineLearning #HealthcareInnovation #DataScience

Yesterday, our VP of Medical Software, Jung en (Andrew) Wu, had the honor of participating in a webinar titled, "Mastering the Regulatory Landscape for AI/ML Enabled SaMD." The webinar was hosted by the Asia Pacific Medical Technology Association (APACMed) in partnership with Rook Quality Systems. Andrew had the privilege of speaking on a panel alongside industry regulatory experts. During the webinar he covered topics such as key pipelines of data, machine learning and software code, and provided an overview of the current regulatory landscape globally. Review the below webinar recording to learn how you can gain a competitive edge in your AI-driven medical technology. #MedicalTechnology #SaMD #AI #MachineLearning #APAC #Regulatory

Calm Waters AI—the autonomous E/M coding solution that achieves 97% accuracy—does even more than save physicians time and reduce stress over compliance. It guides you toward better documentation, which enables you to get paid for the services you provide. Ready to learn more? Click the link: https://bit.ly/3TKJzID #CalmWatersAI #CodingEfficiency #AuditResilience #HealthcareSolutions #CostSavings #RevolutionizeCoding #EMCodingExcellence

Integrating artificial intelligence into healthcare systems: more than just algorithm AI can only be successful in healthcare sys­tems if their predictions are available at the right time and place. Algorithms, while cri­tical, cannot function in isolation – they must be paired with dedicated infrastructure, resources, and personnel trained to act on their predictions.