Methylphenidate for the Treatment of Post-COVID Cognitive Dysfunction (Brain Fog).

A substantial number of patients develop cognitive dysfunction after contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), significantly contributing to long-coronavirus disease (COVID) morbidity. Despite the urgent and overwhelming clinical need, there are currently no proven interventions to treat post-COVID cognitive dysfunction (PCCD). Psychostimulants like methylphenidate may enhance both noradrenergic and dopaminergic pathways in mesolimbic and pre-frontal areas, thus improving memory and cognition. We present a case series of six patients who were treated at the Johns Hopkins Post-Acute COVID-19 Team (PACT) clinic for PCCD with methylphenidate 5 - 20 mg in the context of routine clinical care and followed for 4 to 8 weeks. Baseline and post-treatment outcomes included subjective cognitive dysfunction and objective performance on a battery devised to measure cognitive dysfunction in long-COVID patients. Three out of the six patients reported subjective improvement with methylphenidate, one patient described it as "notable" and another as "marked" improvement in memory and concentration. We also found significant pre-treatment subjective complaints of cognitive dysfunction; however, formal cognitive assessment scores were not severely impaired. A statistically significant difference in pre and post scores, favoring intervention, was found for the following cognitive assessments: Hopkins verbal learning test (HVLT) immediate recall, HVLT delayed recall and category-cued verbal fluency. The current series demonstrates promising neurocognitive effects of methylphenidate for long-COVID cognitive impairment, particularly in recall and verbal fluency domains.

Marketing and US Food and Drug Administration Clearance of Artificial Intelligence and Machine Learning Enabled Software in and as Medical Devices: A Systematic Review.

Importance: The marketing of health care devices enabled for use with artificial intelligence (AI) or machine learning (ML) is regulated in the US by the US Food and Drug Administration (FDA), which is responsible for approving and regulating medical devices. Currently, there are no uniform guidelines set by the FDA to regulate AI- or ML-enabled medical devices, and discrepancies between FDA-approved indications for use and device marketing require articulation. Objective: To explore any discrepancy between marketing and 510(k) clearance of AI- or ML-enabled medical devices. Evidence Review: This systematic review was a manually conducted survey of 510(k) approval summaries and accompanying marketing materials of devices approved between November 2021 and March 2022, conducted between March and November 2022, following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Analysis focused on the prevalence of discrepancies between marketing and certification material for AI/ML enabled medical devices. Findings: A total of 119 FDA 510(k) clearance summaries were analyzed in tandem with their respective marketing materials. The devices were taxonomized into 3 individual categories of adherent, contentious, and discrepant devices. A total of 15 devices (12.61%) were considered discrepant, 8 devices (6.72%) were considered contentious, and 96 devices (84.03%) were consistent between marketing and FDA 510(k) clearance summaries. Most devices were from the radiological approval committees (75 devices [82.35%]), with 62 of these devices (82.67%) adherent, 3 (4.00%) contentious, and 10 (13.33%) discrepant; followed by the cardiovascular device approval committee (23 devices [19.33%]), with 19 of these devices (82.61%) considered adherent, 2 contentious (8.70%) and 2 discrepant (8.70%). The difference between these 3 categories in cardiovascular and radiological devices was statistically significant (P < .001). Conclusions and Relevance: In this systematic review, low adherence rates within committees were observed most often in committees with few AI- or ML-enabled devices. and discrepancies between clearance documentation and marketing material were present in one-fifth of devices surveyed.

Federated AI, Current State, and Future Potential.

Artificial intelligence and machine learning applications are becoming increasingly popular in health care and medical devices. The development of accurate machine learning algorithms requires large quantities of good and diverse data. This poses a challenge in health care because of the sensitive nature of sharing patient data. Decentralized algorithms through federated learning avoid data aggregation. In this paper we give an overview of federated learning, current examples in healthcare and ophthalmology, challenges, and next steps.