Mohammad Hosseini

Authorship attributions and the associated ethical challenges have been discussed at great length in the scholarly literature (Hosseini and Gordijn 2020; Marušić, Bošnjak, and Jerončić 2011). Widel...

BACKGROUND AI-enabled medical devices are increasing rapidly, but whether FDA review capacity, regulatory resources, and postmarket surveillance indicators have scaled in parallel is unclear. OBJECTIVE To analyze temporal trends in time to decision for AI-enabled versus non-AI devices, (Center for Devices and Radiological Health) CDRH resources and workload, review timeliness, and medical device reports. METHODS Cross-sectional analysis of publicly available FDA data from 2015 to 2025. Traditional 510(k) clearances, CDRH program activity metrics, MDUFA performance measures, statutory response metrics, and MAUDE reports information was extracted from FDA publically available databases and FDA Reports to Congress. Main outcomes included review time trends; funding composition, workload, full-time equivalents, and workload per full-time equivalent; MDUFA goal performance; statutory on-time response rates; and MAUDE report counts and rates. RESULTS From 2017 to 2025, AI-enabled Traditional 510(k) clearances increased from 20 of 2614 (0.77%) to 288 of 2655 total (10.85%; annual growth rate, 39.30%). Annual median review time increased by 3.94 days/year for AI-enabled devices, and 1.93 days/year for non-AI devices. AI-enabled median review time was higher in 12 of 15 shared clinical specialties. CDRH user-fee share increased from 26.42% to 46.90%, and pre-submissions increased from 2154 to 3910. MDUFA 510(k) monthly performance shortfalls were greatest in 2021 and 2022 (11.7% and 9.0%). Median statutory on-time response rates were 30% for 513(f)(2), 29% for 513(g), and 77% for postmarket surveillance plans. MAUDE included 17,707,433 all-device reports; AI-enabled devices accounted for 2097 reports, including 1847 malfunctions, 240 injuries, and 10 deaths. CONCLUSIONS Rising AI-enabled device volume, steeper annual review-time increases, increasing user-fee dependence, selected MDUFA shortfalls, low information-request response performance, and expanding postmarket reporting signal volume suggest growing operational pressure on FDA device review and surveillance functions.

Formal recognition of research contributions is critical for career advancement and the allocation of research funding. However, some contributions are mentioned only in the acknowledgments section, which are not indexed by scholarly databases, resulting in little recognition for those involved. We contextualize this shortfall in terms of contributorship, the movement to recognize specific research contributions rather than rely solely on authorship. Broadening the range of recognized individuals is currently advanced largely through reducing authorship restrictions and unbundling manuscripts into smaller elements, such as datasets and protocols, that receive their own attributions. Here we focus on a complementary path, enhancing the contents and metadata of acknowledgments sections. Capitalizing on existing infrastructure and standards, we propose: 1) when acknowledging individuals, authors include ORCIDs (subject to the acknowledgees' approval) and provide CRediT information where applicable; 2) publishers solicit identities of acknowledgees in a similar way to how they do so for authors in their submission portals; and 3) publishers include metadata of acknowledgees in JATS-XML files. Implementing these steps should encourage scholarly databases to index non-author contributors. The ensuing increase in visibility for research contributors, such as technicians and library professionals, should result in greater recognition of non-author roles.

State-supported research funding agencies are critical to the scientific enterprise. However, it remains unclear how funding agencies cooperate with academic communities to realize common scientific goals. Here, we present a fully digital archive assembled by the National Human Genome Research Institute (NHGRI), focusing on the nascent stages of “genomics” as a scientific field and the everyday workings of the Human Genome Project and subsequent major genomics projects. We identify early events behind the conception of genome-wide association studies, clarify hitherto obscured factors around funding decisions, and how NHGRI and academics outside NHGRI ensured continuity in technical expertise across projects. The computational models we developed correctly recapitulate how academic experts and NHGRI increased adoption of genomics by jointly deciding which organisms’ genomes to sequence. Taken together, these findings reveal how a funding agency contributed to scientific innovation in a nascent field of science by repeatedly cooperating with the broader scientific community.

Key Points: The CRediT Standing Committee has developed examples of research tasks that can be attributed to each CRediT role. Further integration of CRediT within scholarly communication will require ongoing attention to technical, social, and financial challenges. Crossref's adoption of CRediT will centralise and disseminate contributor metadata, improving visibility, reuse, and attribution.

In this article, we discuss the growing problem of hallucinated citations produced by Generative Artificial Intelligence (GenAI) in scholarly research and writing. We argue that GenAI hallucinated citations might qualify as a provable instance of research misconduct under the U.S. federal regulations when a) the researcher uses a GenAI tool to produce hallucinated (i.e., nonexistent) citations for a research document; b) the citations function as data because they directly support research findings, as in, for example, review articles or bibliometric studies; and c) the researcher demonstrates indifference to the risk of fabrication of the data (i.e. citations) because they did not check the GenAI's output for veracity and accuracy. Other types of problematic citations such as bibliometrically incorrect citations, or contextually inaccurate citations, are indicative of poor scholarship and irresponsible behavior, but do not qualify as research misconduct. Recognizing that GenAI hallucinated citations could be regarded as research misconduct in certain cases will hopefully encourage researchers to take this problem more seriously than they do now. In partnership with scientific institutions, funders and professional societies, the scholarly community should work on establishing, promoting, and enforcing standards for responsible use of AI in research, including standards pertaining to citation practices.

Authorship remains the primary currency of academic credit and a cornerstone of research integrity, yet current practices often fail to reflect the collaborative and interdisciplinary nature of modern science and questionable authorship practices persist. We argue that addressing these shortcomings is a collective responsibility shared by researchers, journals, research funders, scholarly societies, and research institutions. We examined authorship guidelines issued by journals and research institutions and found that their recommendations to researchers are highly variable. We propose that fostering a responsible authorship culture requires a shared, principle-based framework grounded in transparency, credit, and accountability. These three interconnected principles highlight when authorship practices are questionable and offer a framework for constructive reflection on the meaning of authorship. We outline practical ways research leaders can embed these principles into everyday practice by initiating early, inclusive, and fair authorship discussions and ensuring transparent description of contributions. Research institutions have a unique opportunity to inculcate good practices and lead this culture change with harmonized guidance, education, fair conflict resolution, and reform of researcher assessment. Anchoring authorship in transparency, credit, and accountability will strengthen the credibility of individual research, the fairness of recognition systems, and, ultimately, the trustworthiness of science itself.