Regulatory considerations for medical imaging AI/ML devices in the United States: concepts and challenges.

Purpose: To introduce developers to medical device regulatory processes and data considerations in artificial intelligence and machine learning (AI/ML) device submissions and to discuss ongoing AI/ML-related regulatory challenges and activities. Approach: AI/ML technologies are being used in an increasing number of medical imaging devices, and the fast evolution of these technologies presents novel regulatory challenges. We provide AI/ML developers with an introduction to U.S. Food and Drug Administration (FDA) regulatory concepts, processes, and fundamental assessments for a wide range of medical imaging AI/ML device types. Results: The device type for an AI/ML device and appropriate premarket regulatory pathway is based on the level of risk associated with the device and informed by both its technological characteristics and intended use. AI/ML device submissions contain a wide array of information and testing to facilitate the review process with the model description, data, nonclinical testing, and multi-reader multi-case testing being critical aspects of the AI/ML device review process for many AI/ML device submissions. The agency is also involved in AI/ML-related activities that support guidance document development, good machine learning practice development, AI/ML transparency, AI/ML regulatory research, and real-world performance assessment. Conclusion: FDA's AI/ML regulatory and scientific efforts support the joint goals of ensuring patients have access to safe and effective AI/ML devices over the entire device lifecycle and stimulating medical AI/ML innovation.

A randomized, controlled trial comparing topical steroid application to wet versus dry skin in children with atopic dermatitis (AD).

BACKGROUND: Soak and smear (SS), a technique whereby a bath is followed by topical corticosteroid (TCS) application to wet skin, is reported to be a beneficial adjunctive therapy for patients with recalcitrant atopic dermatitis (AD). OBJECTIVE: We evaluated whether SS is of greater benefit than application of TCS to dry skin for the treatment of childhood AD. METHODS: A randomized, investigator-blinded, controlled study was performed in children with AD. Patients were randomized to apply TCS either via SS (n = 22) or to dry skin (n = 23) for 14 days. The primary outcome was an improvement in the Eczema Area and Severity Index score. Secondary outcomes included assessments of disease burden, pruritus, and sleep; morning cortisol levels; and adverse effects. RESULTS: Patients with AD severity who applied TCS via SS or to dry skin improved 84.8% (95% confidence interval 77.5-92.1) and 81.4% (95% confidence interval 70.3-92.4) by Eczema Area and Severity Index score, respectively. There was no statistical difference between the 2 groups (P value = .85). LIMITATIONS: Small sample size limited the power of our study. CONCLUSIONS: We did not find that application of TCS to presoaked skin works better than application to dry skin for the treatment of AD in children.

Extracting laboratory test information from biomedical text.

BACKGROUND: No previous study reported the efficacy of current natural language processing (NLP) methods for extracting laboratory test information from narrative documents. This study investigates the pathology informatics question of how accurately such information can be extracted from text with the current tools and techniques, especially machine learning and symbolic NLP methods. The study data came from a text corpus maintained by the U.S. Food and Drug Administration, containing a rich set of information on laboratory tests and test devices. METHODS: THE AUTHORS DEVELOPED A SYMBOLIC INFORMATION EXTRACTION (SIE) SYSTEM TO EXTRACT DEVICE AND TEST SPECIFIC INFORMATION ABOUT FOUR TYPES OF LABORATORY TEST ENTITIES: Specimens, analytes, units of measures and detection limits. They compared the performance of SIE and three prominent machine learning based NLP systems, LingPipe, GATE and BANNER, each implementing a distinct supervised machine learning method, hidden Markov models, support vector machines and conditional random fields, respectively. RESULTS: Machine learning systems recognized laboratory test entities with moderately high recall, but low precision rates. Their recall rates were relatively higher when the number of distinct entity values (e.g., the spectrum of specimens) was very limited or when lexical morphology of the entity was distinctive (as in units of measures), yet SIE outperformed them with statistically significant margins on extracting specimen, analyte and detection limit information in both precision and F-measure. Its high recall performance was statistically significant on analyte information extraction. CONCLUSIONS: Despite its shortcomings against machine learning methods, a well-tailored symbolic system may better discern relevancy among a pile of information of the same type and may outperform a machine learning system by tapping into lexically non-local contextual information such as the document structure.