npj Collections | 可持续发展目标 SDG9论文推荐

Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder

摘要：Autism spectrum disorder (ASD) can be reliably diagnosed at 18 months, yet significant diagnostic delays persist in the United States. This article showed a double-blinded, multi-site, prospective, active comparator cohort study tested the accuracy of an artificial intelligence-based Software as a Medical Device designed to aid primary care healthcare providers (HCPs) in diagnosing ASD.  Among participants for whom the Device abstained from providing a result, specialists identified that 91% had one or more complex neurodevelopmental disorders. No significant differences in Device performance were found across participants’ sex, race/ethnicity, income, or education level. For nearly a third of this primary care sample, the Device enabled timely diagnostic evaluation with a high degree of accuracy. The Device shows promise to significantly increase the number of children able to be diagnosed with ASD in a primary care setting, potentially facilitating earlier intervention and more efficient use of specialist resources.

原文：Megerian, J.T., Dey, S., Melmed, R.D. et al. Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder. npj Digit. Med. 5, 57 (2022). https://doi.org/10.1038/s41746-022-00598-6

The imperative for regulatory oversight of large language models (or generative AI) in healthcare

摘要：The rapid advancements in artificial intelligence (AI) have led to the development of sophisticated large language models (LLMs) such as GPT-4 and Bard. The potential implementation of LLMs in healthcare settings has already garnered considerable attention because of their diverse applications that include facilitating clinical documentation, obtaining insurance pre-authorization, summarizing research papers, or working as a chatbot to answer questions for patients about their specific data and concerns. We argue that regulatory oversight should assure medical professionals and patients can use LLMs without causing harm or compromising their data or privacy. This paper summarizes our practical recommendations for what we can expect from regulators to bring this vision to reality.

原文：Meskó, B., Topol, E.J. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. npj Digit. Med. 6, 120 (2023). https://doi.org/10.1038/s41746-023-00873-0

Distribution shift detection for the postmarket surveillance of medical AI algorithms: a retrospective simulation study

摘要：Distribution shifts remain a problem for the safe application of regulated medical AI systems, and may impact their real-world performance if undetected. We implemented and evaluated three deep-learning based shift detection techniques (classifier-based, deep kernel, and multiple univariate kolmogorov-smirnov tests) on simulated shifts in a dataset of 130’486 retinal images. We trained a deep learning classifier for diabetic retinopathy grading. We conclude that effective tools exist for detecting clinically relevant distribution shifts. In particular classifier-based tests can be easily implemented components in the post-market surveillance strategy of medical device manufacturers.

原文：Koch, L.M., Baumgartner, C.F. & Berens, P. Distribution shift detection for the postmarket surveillance of medical AI algorithms: a retrospective simulation study. npj Digit. Med. 7, 120 (2024). https://doi.org/10.1038/s41746-024-01085-w

The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretation

摘要：As applications of AI in medicine continue to expand, there is an increasing focus on integration into clinical practice. An underappreciated aspect of this clinical translation is where the AI fits into the clinical workflow, and in turn, the outputs generated by the AI to facilitate clinician interaction in this workflow. Here, we evaluate the current state of FDA-cleared AI devices for medical image interpretation assistance in terms of intended clinical use, outputs generated, and types of explainability offered. We create a curated database focused on these aspects of the clinician-AI interface, where we find a high frequency of “triage” devices, notable variability in output characteristics across products, and often limited explainability of AI predictions. Altogether, we aim to increase transparency of the current landscape of the clinician-AI interface and highlight the need to rigorously assess which strategies ultimately lead to the best clinical outcomes.

原文：McNamara, S.L., Yi, P.H. & Lotter, W. The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretation. npj Digit. Med. 7, 80 (2024). https://doi.org/10.1038/s41746-024-01080-1

Germany’s digital health reforms in the COVID-19 era: lessons and opportunities for other countries

摘要：Reimbursement is a key challenge for many new digital health solutions, whose importance and value have been highlighted and expanded by the current COVID-19 pandemic. Germany’s new Digital Healthcare Act (Digitale–Versorgung–Gesetz or DVG) entitles all individuals covered by statutory health insurance to reimbursement for certain digital health applications (i.e., insurers will pay for their use). Since Germany, like the United States (US), is a multi-payer health care system, the new Act provides a particularly interesting case study for US policymakers. We first provide an overview of the new German DVG and outline the landscape for reimbursement of digital health solutions in the US, including recent changes to policies governing telehealth during the COVID-19 pandemic. We then discuss challenges and unanswered questions raised by the DVG, ranging from the limited scope of the Act to privacy issues. Lastly, we highlight early lessons and opportunities for other countries.

原文：Gerke, S., Stern, A.D. & Minssen, T. Germany’s digital health reforms in the COVID-19 era: lessons and opportunities for other countries. npj Digit. Med. 3, 94 (2020). https://doi.org/10.1038/s41746-020-0306-7

Broadening the reach of the FDA Sentinel system: A roadmap for integrating electronic health record data in a causal analysis framework

摘要：The Sentinel System is a major component of the United States Food and Drug Administration’s (FDA) approach to active medical product safety surveillance. While Sentinel has historically relied on large quantities of health insurance claims data, leveraging longitudinal electronic health records (EHRs) that contain more detailed clinical information, as structured and unstructured features, may address some of the current gaps in capabilities. We identify key challenges when using EHR data to investigate medical product safety in a scalable and accelerated way, outline potential solutions, and describe the Sentinel Innovation Center’s initiatives to put solutions into practice by expanding and strengthening the existing system with a query-ready, large-scale data infrastructure of linked EHR and claims data. We describe our initiatives in four strategic priority areas: (1) data infrastructure, (2) feature engineering, (3) causal inference, and (4) detection analytics, with the goal of incorporating emerging data science innovations to maximize the utility of EHR data for medical product safety surveillance.

原文：Desai, R.J., Matheny, M.E., Johnson, K. et al. Broadening the reach of the FDA Sentinel system: A roadmap for integrating electronic health record data in a causal analysis framework. npj Digit. Med. 4, 170 (2021). https://doi.org/10.1038/s41746-021-00542-0

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