Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management.

PURPOSE OF REVIEW: Machine learning (ML) is increasingly being studied for the screening, diagnosis, and management of diabetes and its complications. Although various models of ML have been developed, most have not led to practical solutions for real-world problems. There has been a disconnect between ML developers, regulatory bodies, health services researchers, clinicians, and patients in their efforts. Our aim is to review the current status of ML in various aspects of diabetes care and identify key challenges that must be overcome to leverage ML to its full potential. RECENT FINDINGS: ML has led to impressive progress in development of automated insulin delivery systems and diabetic retinopathy screening tools. Compared with these, use of ML in other aspects of diabetes is still at an early stage. The Food & Drug Administration (FDA) is adopting some innovative models to help bring technologies to the market in an expeditious and safe manner. ML has great potential in managing diabetes and the future is in furthering the partnership of regulatory bodies with health service researchers, clinicians, developers, and patients to improve the outcomes of populations and individual patients with diabetes.

Machine Learning Systems Applied to Health Data and System.

The use of machine learning (ML) in medicine is becoming increasingly fundamental to analyse complex problems by discovering associations among different types of information and to generate knowledge for medical decision support. Many regulatory and ethical issues should be considered. Some relevant EU provisions, such as the General Data Protection Regulation, are applicable. However, the regulatory framework for developing and marketing a new health technology implementing ML may be quite complex. Other issues include the legal liability and the attribution of negligence in case of errors. Some of the above-mentioned concerns could be, at least partially, resolved in case the ML software is classified as a 'medical device', a category covered by EU/national provisions. Concluding, the challenge is to understand how sustainable is the regulatory system in relation to the ML innovation and how legal procedures should be revised in order to adapt them to the current regulatory framework.

Machine learning in suicide science: Applications and ethics.

For decades, our ability to predict suicide has remained at near-chance levels. Machine learning has recently emerged as a promising tool for advancing suicide science, particularly in the domain of suicide prediction. The present review provides an introduction to machine learning and its potential application to open questions in suicide research. Although only a few studies have implemented machine learning for suicide prediction, results to date indicate considerable improvement in accuracy and positive predictive value. Potential barriers to algorithm integration into clinical practice are discussed, as well as attendant ethical issues. Overall, machine learning approaches hold promise for accurate, scalable, and effective suicide risk detection; however, many critical questions and issues remain unexplored.

Algorithmic accountability.

There is enormous opportunity for positive social impact from the rise of algorithms and machine learning. But this requires a licence to operate from the public, based on trustworthiness. There are a range of concerns relating to how algorithms might be held to account in areas affecting the public sphere. This paper outlines a number of approaches including greater transparency, monitoring of outcomes and improved governance. It makes a case that public sector bodies that hold datasets should be more confident in negotiating terms with the private sector. It also argues that all regulators (not just data regulators) need to wake up to the challenges posed by changing technology. Other improvements include diversity of the workforce, ethics training, codes of conduct for data scientists, and new deliberative bodies. Even if these narrower issues are solved, the paper poses some wider concerns including data monopolies, the challenge to democracy, public participation and maintaining the public interest.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.

[Required Framework for the Collection of Real-life Data: An Example from University Eye Hospital Munich]. / Rahmenbedingungen zur Sammlung von "Real-Life"-Daten am Beispiel der Augenklinik der Universität München.

Background The importance of evaluating real-life data is constantly increasing. Currently available computer systems better allow for analyses of data, as more and more data is available in a digital form. Before a project for real-life data analyses is started, technical considerations and staff, legal, and data protection procedures need to be addressed. In this manuscript, experiences made at the University Eye Hospital in Munich will be shared. Materials and Methods Legal requirements, as found in laws and guidelines governing documentation and data privacy, are highlighted. Technical requirements for information technology infrastructure and software are defined. A survey conducted by the German Ophthalmological Society, among German eye hospitals investigating the current state of digitalization, was conducted. Also, staff requirements are outlined. Results A database comprising results of 330,801 patients was set up. It includes all diagnoses, procedures, clinical findings and results from diagnostic devices. This database was approved by the local data protection officer. In less than half of German eye hospitals (n = 21) that participated in the survey (n = 54), a complete electronic documentation is done. Fourteen institutions are completely paper-based, and the remainder of the hospitals used a mixed system. Conclusion In this work, we examined the framework that is required to develop a comprehensive database containing real-life data from clinics. In future, these databases will become increasingly important as more and more innovation are made in decision support systems. The base for this is comprehensive and well-curated databases.

Addressing health disparities in the Food and Drug Administration's artificial intelligence and machine learning regulatory framework.

The exponential growth of health data from devices, health applications, and electronic health records coupled with the development of data analysis tools such as machine learning offer opportunities to leverage these data to mitigate health disparities. However, these tools have also been shown to exacerbate inequities faced by marginalized groups. Focusing on health disparities should be part of good machine learning practice and regulatory oversight of software as medical devices. Using the Food and Drug Administration (FDA)'s proposed framework for regulating machine learning tools in medicine, I show that addressing health disparities during the premarket and postmarket stages of review can help anticipate and mitigate group harms.