Frauds in scientific research and how to possibly overcome them.

Frauds and misconduct have been common in the history of science. Recent events connected to the COVID-19 pandemic have highlighted how the risks and consequences of this are no longer acceptable. Two papers, addressing the treatment of COVID-19, have been published in two of the most prestigious medical journals; the authors declared to have analysed electronic health records from a private corporation, which apparently collected data of tens of thousands of patients, coming from hundreds of hospitals. Both papers have been retracted a few weeks later. When such events happen, the confidence of the population in scientific research is likely to be weakened. This paper highlights how the current system endangers the reliability of scientific research, and the very foundations of the trust system on which modern healthcare is based. Having shed light on the dangers of a system without appropriate monitoring, the proposed analysis suggests to strengthen the existing journal policies and improve the research process using new technologies supporting control activities by public authorities. Among these solutions, we mention the promising aspects of the blockchain technology which seems a promising solution to avoid the repetition of the mistakes linked to the recent and past history of research.

Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature.

BACKGROUND: The COVID-19 pandemic is favoring digital transitions in many industries and in society as a whole. Health care organizations have responded to the first phase of the pandemic by rapidly adopting digital solutions and advanced technology tools. OBJECTIVE: The aim of this review is to describe the digital solutions that have been reported in the early scientific literature to mitigate the impact of COVID-19 on individuals and health systems. METHODS: We conducted a systematic review of early COVID-19-related literature (from January 1 to April 30, 2020) by searching MEDLINE and medRxiv with appropriate terms to find relevant literature on the use of digital technologies in response to the pandemic. We extracted study characteristics such as the paper title, journal, and publication date, and we categorized the retrieved papers by the type of technology and patient needs addressed. We built a scoring rubric by cross-classifying the patient needs with the type of technology. We also extracted information and classified each technology reported by the selected articles according to health care system target, grade of innovation, and scalability to other geographical areas. RESULTS: The search identified 269 articles, of which 124 full-text articles were assessed and included in the review after screening. Most of the selected articles addressed the use of digital technologies for diagnosis, surveillance, and prevention. We report that most of these digital solutions and innovative technologies have been proposed for the diagnosis of COVID-19. In particular, within the reviewed articles, we identified numerous suggestions on the use of artificial intelligence (AI)-powered tools for the diagnosis and screening of COVID-19. Digital technologies are also useful for prevention and surveillance measures, such as contact-tracing apps and monitoring of internet searches and social media usage. Fewer scientific contributions address the use of digital technologies for lifestyle empowerment or patient engagement. CONCLUSIONS: In the field of diagnosis, digital solutions that integrate with traditional methods, such as AI-based diagnostic algorithms based both on imaging and clinical data, appear to be promising. For surveillance, digital apps have already proven their effectiveness; however, problems related to privacy and usability remain. For other patient needs, several solutions have been proposed, such as telemedicine or telehealth tools. These tools have long been available, but this historical moment may actually be favoring their definitive large-scale adoption. It is worth taking advantage of the impetus provided by the crisis; it is also important to keep track of the digital solutions currently being proposed to implement best practices and models of care in future and to adopt at least some of the solutions proposed in the scientific literature, especially in national health systems, which have proved to be particularly resistant to the digital transition in recent years.

Transferring Health Big Data within the European Legal Framework: What Role for National Healthcare Services?

The main objective of this article is to describe the legal principles governing the selection by European public authorities, such as National Health Services (NHS) of third parties, when entering into agreements for the transfer of health data. According to Directive 2003/98/EC, and in light of the provisions of the Treaties of the European Union, the choice as to how a public authority makes its data available to third parties needs to be transparent, non-discriminatory and may not in any case benefit a specific company at the expense of others. For this reason, we maintain that a hypothetical agreement by which a public authority grants exclusive access to a large amount of health data to a private company selected with non-transparent criteria appears highly questionable. We advocate that the NHS should adopt more appropriate data policies aimed at promoting the sustainability of the NHS, following the legal framework analysed in this article.

A step-by-step researcher's guide to the use of an AI-based transformer in epidemiology: an exploratory analysis of ChatGPT using the STROBE checklist for observational studies.

Objective: This study aims at investigating how AI-based transformers can support researchers in designing and conducting an epidemiological study. To accomplish this, we used ChatGPT to reformulate the STROBE recommendations into a list of questions to be answered by the transformer itself. We then qualitatively evaluated the coherence and relevance of the transformer's outputs. Study design: Descriptive study. Methods: We first chose a study to be used as a basis for the simulation. We then used ChatGPT to transform each STROBE checklist's item into specific prompts. Each answer to the respective prompt was evaluated by independent researchers in terms of coherence and relevance. Results: The mean scores assigned to each prompt were heterogeneous. On average, for the coherence domain, the overall mean score was 3.6 out of 5.0, and for relevance it was 3.3 out of 5.0. The lowest scores were assigned to items belonging to the Methods section of the checklist. Conclusions: ChatGPT can be considered as a valuable support for researchers in conducting an epidemiological study, following internationally recognized guidelines and standards. It is crucial for the users to have knowledge on the subject and a critical mindset when evaluating the outputs. The potential benefits of AI in scientific research and publishing are undeniable, but it is crucial to address the risks, and the ethical and legal consequences associated with its use.