Tag: Artificial Intelligence

Summary of article from Sage Journals, by Jean-Christophe Bélisle-Pipon, Maria Powell, Renee English, Marie-Françoise Malo, Vardit Ravitsky, Bridge2AI–Voice Consortium, Yael Bensoussan:

Voice as a health biomarker using artificial intelligence (AI) is gaining momentum in research. The noninvasiveness of voice data collection through accessible technology (such as smartphones, telehealth, and ambient recordings) or within clinical contexts means voice AI may help address health disparities and promote the inclusion of marginalized communities. However, the development of AI-ready voice datasets free from bias and discrimination is a complex task. The objective of this study is to better understand the perspectives of engaged and interested stakeholders regarding ethical and trustworthy voice AI, to inform both further ethical inquiry and technology innovation.

Summary of article from GovCIO, by Silvia Oakland:

The Department of Health and Human Services (HHS) has restructured its technology and data strategy responsibilities, consolidating them under its policy office. This reorganization primarily affects the Office of National Coordinator for Health IT (ONC), now renamed the Assistant Secretary for Technology Policy and ONC (ASTP/ONC). A new Office of the Chief Technology Officer will be established, encompassing the Office of the Chief AI Officer, Office of the Chief Data Officer, and a new Office of Digital Services. This digital services team will oversee HHS-wide digital strategy and ethics in technology initiatives. The 405(d) cybersecurity program will transition to the Administration for Strategic Preparedness and Response (ASPR) to enhance healthcare cybersecurity. HHS Secretary Xavier Becerra emphasized the growing importance of cybersecurity, data, and AI in healthcare. Additionally, ONC has updated the Trusted Exchange Framework and Common Agreement (TEFCA) to improve the nationwide exchange of electronic health information.

Summary of blog post from FDA, by Troy Tazbaz:

AI’s continuous learning and adaptability pose risks, such as exacerbating biases, which can harm patients and underrepresented populations. Lifecycle Management (LCM), integral to reliable software since the 1960s, can address these challenges through structured frameworks. The AI Lifecycle (AILC) concept maps traditional Software Development Lifecycles to AI-specific phases, emphasizing systematic methods for data and model evaluation. This AILC model serves as a guide for assessing standards, tools, metrics, and best practices, promoting quality, interoperability, and ethical practices. The health care community is encouraged to engage with and refine these concepts to ensure AI’s safe and effective integration into health care. Feedback and involvement are welcomed to support the development of high-quality AI models.

Summary of article from Fierce Healthcare, by Heather Landi:

Microsoft is collaborating with Mass General Brigham and the University of Wisconsin-Madison to enhance AI in medical imaging. The partnership aims to develop, test, and validate AI algorithms to improve the accuracy and consistency of medical image analysis. These AI models will be integrated into clinical workflows via Microsoft’s Azure AI platform and Nuance’s PowerScribe radiology reporting platform. The collaboration seeks to assist radiologists and clinicians in interpreting medical images, generating reports, classifying diseases, and analyzing structured data. This initiative addresses the healthcare industry’s challenges of physician burnout and staffing shortages by leveraging generative AI to enhance workflow efficiencies. Key leaders from the partner organizations emphasize the potential of generative AI to transform clinical care and improve patient outcomes. Additionally, Microsoft is working with Nvidia to advance generative AI and cloud computing in healthcare and life sciences.

Summary of article from DataGuidance, by Michael Borrelli:

The EU AI Act aims to regulate AI systems within the EU, categorizing them by risk levels and imposing stringent requirements on high-risk systems, particularly in healthcare. This legislation emphasizes transparency, accountability, and ethical considerations to ensure AI technologies are safe and trustworthy. High-risk AI systems in healthcare must meet rigorous standards for risk management, data quality, transparency, human oversight, and post-market monitoring. While compliance presents challenges, the Act fosters innovation and aims to improve healthcare outcomes and patient safety. Overall, the EU AI Act is pivotal in shaping the ethical deployment of AI in healthcare.

Summary of article from California Health Report, by Jennifer McLelland:

The author tested three major AI chatbots—Google Gemini, Meta Llama 3, and ChatGPT—on medical questions to evaluate their accuracy, finding that their responses were often incorrect or misleading. This raises concerns about AI’s potential to spread harmful misinformation, especially for families seeking information on rare medical conditions. The author argues that while AI promises simple solutions, the complex needs of children with special health care requirements necessitate increased funding for human providers who can offer personalized, accurate guidance. Furthermore, the use of AI in health insurance decisions could perpetuate existing disparities and biases in the healthcare system. The author advocates for legislative oversight and more substantial investment in human resources to ensure equitable and reliable healthcare.