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The use of digital health products has gained considerable interest as a new way to improve therapeutic research and development. Although these products are being adopted by various industries and stakeholders, their incorporation in clinical trials has been slow due to a disconnect between the promises of digital products and potential risks in using these new technologies in the absence of regulatory support. The Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium hosted a public workshop to address challenges and opportunities in this field. Important characteristics of tool development were addressed in a series of presentations, case studies, and open panel sessions. The workshop participants endorsed the usefulness of an evidentiary criteria framework, highlighted the importance of early patient engagement, and emphasized the potential impact of digital monitoring tools and precompetitive collaborations. Concerns were expressed about the lack of real‐life validation examples and the limitations of legacy standards used as a benchmark for novel tool development and validation. Participants recognized the need for novel analytical and statistical approaches to accommodate analyses of these novel data types. Future directions are to harmonize definitions to build common methodologies and foster multidisciplinary collaborations; to develop approaches toward integrating digital monitoring data with the totality of the data in clinical trials, and to continue an open dialog in the community. There was a consensus that all these efforts combined may create a paradigm shift of how clinical trials are planned, conducted, and results brought to regulatory reviews.

The First International Conference in Systems and Network Medicine gathered together 200 global thought leaders, scientists, clinicians, academicians, industry and government experts, medical and graduate students, postdoctoral scholars and policymakers. Held at Georgetown University Conference Center in Washington D.C. on September 11-13, 2019, the event featured a day of pre-conference lectures and hands-on bioinformatic computational workshops followed by two days of deep and diverse scientific talks, panel discussions with eminent thought leaders, and scientific poster presentations. Topics ranged from: Systems and Network Medicine in Clinical Practice; the role of -omics technologies in Health Care; the role of Education and Ethics in Clinical Practice, Systems Thinking, and Rare Diseases; and the role of Artificial Intelligence in Medicine. The conference served as a unique nexus for interdisciplinary discovery and dialogue and fostered formation of new insights and possibilities for health care systems advances.

Importance The development of artificial intelligence (AI) and other machine diagnostic systems, also known as software as a medical device, and its recent introduction into clinical practice requires a deeply rooted foundation in bioethics for consideration by regulatory agencies and other stakeholders around the globe. Objectives To initiate a dialogue on the issues to consider when developing a bioethically sound foundation for AI in medicine, based on images of eye structures, for discussion with all stakeholders. Evidence Review The scope of the issues and summaries of the discussions under consideration by the Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group, as first presented during the Collaborative Community on Ophthalmic Imaging inaugural meeting on September 7, 2020, and afterward in the working group. Findings Artificial intelligence has the potential to improve health care access and patient outcome fundamentally while decreasing disparities, lowering cost, and enhancing the care team. Nevertheless, substantial concerns exist. Bioethicists, AI algorithm experts, as well as the Food and Drug Administration and other regulatory agencies, industry, patient advocacy groups, clinicians and their professional societies, other provider groups, and payors (i.e., stakeholders) working together in collaborative communities to resolve the fundamental ethical issues of nonmaleficence, autonomy, and equity are essential to attain this potential. Resolution impacts all levels of the design, validation, and implementation of AI in medicine. Design, validation, and implementation of AI warrant meticulous attention. Conclusions and Relevance The development of a bioethically sound foundation may be possible if it is based in the fundamental ethical principles of nonmaleficence, autonomy, and equity for considerations for the design, validation, and implementation for AI systems. Achieving such a foundation will be helpful for continuing successful introduction into medicine before consideration by regulatory agencies. Important improvements in accessibility and quality of health care, decrease in health disparities, and lower cost thereby can be achieved. These considerations should be discussed with all stakeholders and expanded on as a useful initiation of this dialogue.

Importance There is an enormous and growing amount of data available from individual cancer cases, which makes the work of clinical oncologists more demanding. This data challenge has attracted engineers to create software that aims to improve cancer diagnosis or treatment. However, the move to use computers in the oncology clinic for diagnosis or treatment has led to instances of premature or inappropriate use of computational predictive systems. Objective To evaluate best practices for developing and assessing the clinical utility of predictive computational methods in oncology. Evidence Review The National Cancer Policy Forum and the Board on Mathematical Sciences and Analytics at the National Academies of Sciences, Engineering, and Medicine hosted a workshop to examine the use of multidimensional data derived from patients with cancer and the computational methods used to analyze these data. The workshop convened diverse stakeholders and experts, including computer scientists, oncology clinicians, statisticians, patient advocates, industry leaders, ethicists, leaders of health systems (academic and community based), private and public health insurance carriers, federal agencies, and regulatory authorities. Key characteristics for successful computational oncology were considered in 3 thematic areas: (1) data quality, completeness, sharing, and privacy; (2) computational methods for analysis, interpretation, and use of oncology data; and (3) clinical infrastructure and expertise for best use of computational precision oncology. Findings Quality control was found to be essential across all stages, from data collection to data processing, management, and use. Collecting a standardized parsimonious data set at every cancer diagnosis and restaging could enhance reliability and completeness of clinical data for precision oncology. Data completeness refers to key data elements such as information about cancer diagnosis, treatment, and outcomes, while data quality depends on whether appropriate variables have been measured in valid and reliable ways. Collecting data from diverse populations can reduce the risk of creating invalid and biased algorithms. Computational systems that aid clinicians should be classified as software as a medical device and thus regulated according to the potential risk posed. To facilitate appropriate use of computational methods that interpret high-dimensional data in oncology, treating physicians need access to multidisciplinary teams with broad expertise and deep training among a subset of clinical oncology fellows in clinical informatics. Conclusions and Relevance Workshop discussions suggested best practices in demonstrating the clinical utility of predictive computational methods for diagnosing or treating cancer.

As we enter the information age of health care, digital health technologies offer significant opportunities to optimize both clinical care delivery and clinical research. Despite their potential, the use of such information technologies in clinical care and research faces major data quality, privacy, and regulatory concerns. In hopes of addressing both the promise and challenges facing digital health technologies in the transformation of health care, we convened a think tank meeting with academic, industry, and regulatory representatives in December 2016 in Washington, DC. In this paper, we summarize the proceedings of the think tank meeting and aim to delineate a framework for appropriately using digital health technologies in healthcare delivery and research.

Biomass pellets are a source of renewable energy, although, the air pollution and exposure risks posed by the emissions from burning pellets in biomass boilers (BBs) are uncertain. The present study examines the organic species in fine particle matter (PM) emissions from an BB firing switchgrass (SwG) and hardwood (HW) biomass pellets using different test cycles. The organic and elemental carbon (OC and EC) content and select semivolatile organic compounds (SVOCs) in filter-collected PM were identified and quantified using thermal-optical analysis and gas chromatography&ndash, mass spectrometry (GC&ndash, MS), respectively. Fine PM emissions from the BB ranged from 0.4 g/kg to 2.91 g/kg of pellets burned of which 40% &plusmn, 17% w/w was carbon. The sum of GC&ndash, MS quantified SVOCs in the PM emissions varied from 0.13 to 0.41 g/g OC. Relatively high levels of oxygenated compounds were observed in the PM emissions, and the most predominant individual SVOC constituent was levoglucosan (12.5&ndash, 320 mg/g OC). The effect of boiler test cycle on emissions was generally greater than the effect due to pellet fuel type. Organic matter emissions increased at lower loads, owing to less than optimal combustion performance. Compared with other types of residential wood combustion studies, pellet burning in the current BB lowered PM emissions by nearly an order of magnitude. PM emitted from burning pellets in boilers tested across multiple studies also contains comparatively less carbon, however, the toxic polycyclic aromatic hydrocarbons (PAH) in the PM tested across these pellet-burning studies varied substantially, and produced 2&ndash, 10 times more benzo[k]fluoranthene, dibenz[a,h]anthracene and indeno[1,2,3-c,d]pyrene on average. These results suggest that further toxicological evaluation of biomass pellet burning emissions is required to properly understand the risks posed.