RESUMO
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.
RESUMO
IMPORTANCE: The US Food and Drug Administration's medical device regulatory pathway was initially conceived with hardware devices in mind. The emerging market for ophthalmic digital devices necessitates an evolution of this paradigm. OBJECTIVES: To facilitate innovation in ophthalmic digital health with attention to safety and effectiveness. EVIDENCE REVIEW: This article presents a summary of the presentations, discussions, and literature review that occurred during a joint Ophthalmic Digital Health workshop of the American Academy of Ophthalmology, the American Academy of Pediatrics, the American Association for Pediatric Ophthalmology and Strabismus, the American Society of Cataract and Refractive Surgery, the American Society of Retina Specialists, the Byers Eye Institute at Stanford and the US Food and Drug Administration. FINDINGS: Criterion standards and expert graders are critically important in the evaluation of automated systems and telemedicine platforms. Training at all levels is important for the safe and effective operation of digital health devices. The risks associated with automation are substantially increased in rapidly progressive diseases. Cybersecurity and patient privacy warrant meticulous attention. CONCLUSIONS AND RELEVANCE: With appropriate attention to safety and effectiveness, digital health technology could improve screening and treatment of ophthalmic diseases and improve access to care.
RESUMO
The increased prevalence of myopia in the United States and other regions of the world, and the sight-threatening problems associated with higher levels of myopia have led to great interest in research designed to reduce these rates. As most of the progression of myopia occurs in childhood, these investigations have been directed toward slowing the progression of myopia in children. Treatments described to potentially slow the progression of myopia have included pharmacological interventions, multifocal spectacles, and multifocal correction created by contact lenses. Although some contact lens clinical trials have demonstrated promising results in slowing the progression of myopia, many of these studies have significant limitations, including only short follow-up times, limited randomization, and incomplete masking. Such limitations have underscored the need to develop a more robust clinical study design, so that future studies can demonstrate whether contact lenses, as well as other medical devices, can be used in a safe and effective manner to control myopia progression. We review previous key studies and discuss study design and regulatory issues relevant to future clinical trials.