White paper on ophthalmic imaging for choroidal nevus identification and transformation into Melanoma

Purpose: To discuss the evolution of noninvasive diagnostic methods in the identification of choroidal nevus and determination of risk factors for malignant transformation as well as introduce the novel role that artificial intelligence (AI) can play in the diagnostic process. Methods: White paper. Results: Longstanding diagnostic methods to stratify benign choroidal nevus from choroidal melanoma and to further determine the risk for nevus transformation into melanoma have been dependent on recognition of key clinical features by ophthalmic examination. These risk factors have been derived from multiple large cohort research studies over the past several decades and have garnered widespread use throughout the world. More recent publications have applied ocular diagnostic testing (fundus photog-raphy, ultrasound examination, autofluorescence, and optical coherence tomography) to identify risk factors for the malignant transformation of choroidal nevus based on multimodal imaging features. The widespread usage of ophthalmic imaging systems to identify and follow choroidal nevus, in conjunction with the characterization of malignant transformation risk factors via diagnostic imaging, presents a novel path to apply AI. Conclusions: AI applied to existing ophthalmic imaging systems could be used for both identification of choroidal nevus and as a tool to aid in earlier detection of transformation to malignant melanoma. Translational Relevance: Advances in AI models applied to ophthalmic imaging systems have the potential to improve patient care, because earlier detection and treatment of melanoma has been proven to improve long-term clinical outcomes

Study Design Considerations for Sleep Disordered Breathing Devices.

None: In recent years, sleep disordered breathing (SDB) has been recognized as a prevalent but under-diagnosed condition in adults and has prompted the need for new and better diagnostic and therapeutic options. To facilitate the development and availability of innovative, safe and effective SDB medical device technologies for patients in the United States, the Food and Drug Administration (FDA) collaborated with six SDB-related professional societies and a consumer advocacy organization to convene a public workshop focused on clinical investigations of SDB devices. Sleep medicine experts discussed appropriate definitions of terms used in the diagnosis and treatment of SDB, the use of home sleep testing versus polysomnography, clinical trial design issues in studying SDB devices, and current and future trends in digital health technologies for diagnosis and monitoring SDB. The panel\u27s breadth of clinical expertise and experience across medical specialties provided useful and important insights regarding clinical trial designs for SDB devices

Considerations for addressing bias in artificial intelligence for health equity

Abstract Health equity is a primary goal of healthcare stakeholders: patients and their advocacy groups, clinicians, other providers and their professional societies, bioethicists, payors and value based care organizations, regulatory agencies, legislators, and creators of artificial intelligence/machine learning (AI/ML)-enabled medical devices. Lack of equitable access to diagnosis and treatment may be improved through new digital health technologies, especially AI/ML, but these may also exacerbate disparities, depending on how bias is addressed. We propose an expanded Total Product Lifecycle (TPLC) framework for healthcare AI/ML, describing the sources and impacts of undesirable bias in AI/ML systems in each phase, how these can be analyzed using appropriate metrics, and how they can be potentially mitigated. The goal of these “Considerations” is to educate stakeholders on how potential AI/ML bias may impact healthcare outcomes and how to identify and mitigate inequities; to initiate a discussion between stakeholders on these issues, in order to ensure health equity along the expanded AI/ML TPLC framework, and ultimately, better health outcomes for all