Summary of Keynote Speeches from the 2024 Voice AI Symposium, presented by the Bridge2AI-Voice Consortium.

Introduction: The 2024 Voice AI Symposium, hosted by the Bridge2AI-Voice Consortium in Tampa, FL, featured two keynote speeches that addressed the intersection of voice AI, healthcare, ethics, and law. Dr. Rupal Patel and Dr.

Synthetic Health Data: Real Ethical Promise and Peril.

Researchers and practitioners are increasingly using machine-generated synthetic data as a tool for advancing health science and practice, by expanding access to health data while-potentially-mitigating privacy and related ethical concerns around data sharing. While using synthetic data in this way holds promise, we argue that it also raises significant ethical, legal, and policy concerns, including persistent privacy and security problems, accuracy and reliability issues, worries about fairness and bias, and new regulatory challenges. The virtue of synthetic data is often understood to be its detachment from the data subjects whose measurement data is used to generate it.

Systematic enhancement of protein crystallization efficiency by bulk lysine-to-arginine (KR) substitution.

Structural genomics consortia established that protein crystallization is the primary obstacle to structure determination using x-ray crystallography. We previously demonstrated that crystallization propensity is systematically related to primary sequence, and we subsequently performed computational analyses showing that arginine is the most overrepresented amino acid in crystal-packing interfaces in the Protein Data Bank. Given the similar physicochemical characteristics of arginine and lysine, we hypothesized that multiple lysine-to-arginine (KR) substitutions should improve crystallization.

Principles for Health Information Collection, Sharing, and Use: A Policy Statement From the American Heart Association.

The evolution of the electronic health record, combined with advances in data curation and analytic technologies, increasingly enables data sharing and harmonization. Advances in the analysis of health-related and health-proxy information have already accelerated research discoveries and improved patient care. This American Heart Association policy statement discusses how broad data sharing can be an enabling driver of progress by providing data to develop, test, and benchmark innovative methods, scalable insights, and potential new paradigms for data storage and workflow.

Exclusion cycles: Reinforcing disparities in medicine.

Clinical practice, data collection, and medical AI constitute self-reinforcing and interacting cycles of exclusion.

Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges.

Machine learning applications promise to augment clinical capabilities and at least 64 models have already been approved by the US Food and Drug Administration. These tools are developed, shared, and used in an environment in which regulations and market forces remain immature. An important consideration when evaluating this environment is the introduction of open-source solutions in which innovations are freely shared; such solutions have long been a facet of digital culture.

Respecting Autonomy And Enabling Diversity: The Effect Of Eligibility And Enrollment On Research Data Demographics.

Many promising advances in precision health and other Big Data research rely on large data sets to analyze correlations among genetic variants, behavior, environment, and outcomes to improve population health. But these data sets are generally populated with demographically homogeneous cohorts. We conducted a retrospective cohort study of patients at a major academic medical center during 2012-19 to explore how recruitment and enrollment approaches affected the demographic diversity of participants in its research biospecimen and data bank.

Collaboration in times of crisis: A study on COVID-19 vaccine R&D partnerships.

Collaboration is central for initiatives and efforts in the race to fight COVID-19, with particular focus on fostering rapid development of safe and effective COVID-19 vaccines. We investigated the types of partnerships that have emerged during the pandemic to develop these products. Using the World Health Organization's list of COVID-19 vaccine developments, we found nearly one third of all vaccine candidates were developed by partnerships, which tended to use next-gen vaccine platforms more than solo efforts.

Regulatory responses to medical machine learning.

Companies and healthcare providers are developing and implementing new applications of medical artificial intelligence, including the artificial intelligence sub-type of medical machine learning (MML). MML is based on the application of machine learning (ML) algorithms to automatically identify patterns and act on medical data to guide clinical decisions. MML poses challenges and raises important questions, including (1) How will regulators evaluate MML-based medical devices to ensure their safety and effectiveness? and (2) What additional MML considerations should be taken into account in the international context? To address these questions, we analyze the current regulatory approaches to MML in the USA and Europe.

Privacy Gaps for Digital Cardiology Data: Big Problems With Big Data.

Mr. M is a 55 year-old man who suffers an acute myocardial infarction (MI) and undergoes coronary stenting. Following hospitalization, he completes cardiac rehabilitation.

Regulatory oversight, causal inference, and safe and effective health care machine learning.

In recent years, the applications of Machine Learning (ML) in the health care delivery setting have grown to become both abundant and compelling. Regulators have taken notice of these developments and the U.S.

Shadow health records meet new data privacy laws.

Large sets of health data can enable innovation and quality measurement but can also create technical challenges and privacy risks. When entities such as health plans and health care providers handle personal health information, they are often subject to data privacy regulation. But amid a flood of new forms of health data, some third parties have figured out ways to avoid some data privacy laws, developing what we call “shadow health records”—collections of health data outside the health system that provide detailed pictures of individual health—that allow both innovative research and commercial targeting despite data privacy rules.

Privacy in the age of medical big data.

Big data has become the ubiquitous watch word of medical innovation. The rapid development of machine-learning techniques and artificial intelligence in particular has promised to revolutionize medical practice from the allocation of resources to the diagnosis of complex diseases. But with big data comes big risks and challenges, among them significant questions about patient privacy.