ABSTRACT
BACKGROUND: Ensuring safe adoption of AI tools in healthcare hinges on access to sufficient data for training, testing and validation. Synthetic data has been suggested in response to privacy concerns and regulatory requirements and can be created by training a generator on real data to produce a dataset with similar statistical properties. Competing metrics with differing taxonomies for quality evaluation have been proposed, resulting in a complex landscape. Optimising quality entails balancing considerations that make the data fit for use, yet relevant dimensions are left out of existing frameworks. METHOD: We performed a comprehensive literature review on the use of quality evaluation metrics on synthetic data within the scope of synthetic tabular healthcare data using deep generative methods. Based on this and the collective team experiences, we developed a conceptual framework for quality assurance. The applicability was benchmarked against a practical case from the Dutch National Cancer Registry. CONCLUSION: We present a conceptual framework for quality assuranceof synthetic data for AI applications in healthcare that aligns diverging taxonomies, expands on common quality dimensions to include the dimensions of Fairness and Carbon footprint, and proposes stages necessary to support real-life applications. Building trust in synthetic data by increasing transparency and reducing the safety risk will accelerate the development and uptake of trustworthy AI tools for the benefit of patients. DISCUSSION: Despite the growing emphasis on algorithmic fairness and carbon footprint, these metrics were scarce in the literature review. The overwhelming focus was on statistical similarity using distance metrics while sequential logic detection was scarce. A consensus-backed framework that includes all relevant quality dimensions can provide assurance for safe and responsible real-life applications of synthetic data. As the choice of appropriate metrics are highly context dependent, further research is needed on validation studies to guide metric choices and support the development of technical standards.
Subject(s)
Delivery of Health Care , Trust , Humans , Health FacilitiesABSTRACT
Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.
ABSTRACT
Phospholipid vesicles have been the focus of attention as potential vehicles for drug delivery, as they are biomimetic, easy to produce, and contain an aqueous compartment which can be used to carry hydrophilic material, such as drugs or dyes. Lipid vesicles used for this purpose present a particular challenge, as they are not especially stable and can rapidly break down and release their contents away from the target area, especially at physiological temperatures/environments. This study aims to investigate optimum methods for vesicle stabilization where the vesicles are employed as part of a system or technology that signals the presence of pathogenic bacteria via the effect of secreted cytolytic virulence factors on a sensor interface. A number of approaches have been investigated and are presented here as a systematic study of the long-term (14 day) stability at 37 °C, and at various pHs. The response of vesicles, both in suspension and within hydrogels, to Staphylococcus aureus (RN 4282) and Pseudomonas aeruginosa (PAO1) whole bacteria, and supernatants from overnight cultures of both (containing secreted proteins but free of cells), was measured via a sensitive encapsulated carboxyfluorescein release assay. The results showed that lipid chain length, cholesterol concentration, and stabilization via photopolymer stable components were critical in achieving stability. Finally, dispersion of the optimum vesicle formulation in hydrogel matrixes was investigated, culminating in the in vivo demonstration of a simple prototype wound dressing.
