The Chirp Issue #10

The key to trustworthy AI results in health tech is quality data used in machine learning. Good data is crucial because the accuracy and effectiveness of AI solutions depend on the information they are trained on. In healthcare, where precision and reliability are paramount, utilizing high-quality, diverse, and representative datasets ensures that AI models can generalize to real-world scenarios. Good data not only enhances the predictive capabilities of machine learning algorithms, but also helps mitigate the risk of biases and inaccuracies that may arise from inadequate or skewed datasets. Health tech companies must prioritize the curation of robust datasets to build AI applications that can truly contribute to improved patient outcomes, clinical decision-making, and overall healthcare delivery.

Today’s topic: Getting a healthy dose of reliable results.

From the experts

World Economic Forum Talks AI

The 54th World Economic Forum Annual Meeting delved into discussions on AI, emphasizing the opportunities and challenges it presents. The sessions featured call for a global approach to regulations and the involvement of all stakeholders in AI development. The discussions recognized AI’s potential to accelerate scientific discovery, but also highlighted ethical considerations. Overall, the conversations at Davos underscored the importance of addressing AI’s impact on jobs, fairness, privacy, and democracy while emphasizing the enduring relevance of human qualities and connections in the evolving technological landscape.

How AI Is Improving Diagnostics, Decision-Making and Care

A recent survey highlights healthcare consumers’ interest in the potential benefits of generative AI, tempered by concerns about data transparency. Nearly half of Americans (49%) fear the possibility of generative AI producing false information affecting their care, and a similar percentage would distrust results from providers using such technology. The health tech challenge lies in addressing consumer concerns while leveraging the positive impacts of AI. The need for transparency, developer expertise, clinician involvement, and adherence to standards is emphasized to build trust and ensure responsible implementation.

Guidelines for AI-Based Prediction Models in Healthcare

This review looks into the use of AI-based prediction models in healthcare, focusing on how they are developed, evaluated, and put into practice. The review is organized around six stages of creating these models: getting the data ready, building the model, checking if it works, creating the software, assessing its impact, and finally, using it in everyday healthcare. The study points out that while there is a lot of advice for the early stages, the later ones get less attention in scientific literature. It emphasizes the need for clear guidelines to make sure these models are used responsibly in healthcare and highlights areas where more research and real-world experience are needed.

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CPT Codes

Current Procedural Terminology (CPT) codes are now available for clinicians to seamlessly incorporate Canary Speech assessments into their routine medical practices! By seamlessly fitting into existing medical workflows, our technology adds value to routine assessments and empowers clinicians to make more informed decisions about patient care.

See you at HIMSS

The HIMSS Global Health Conference will be in Orlando March 11-15, 2024. Want to connect? Email henry@canaryspeech.com or respond to this email to meet up!