At the beginning of the artificial intelligence (AI)/machine learning (ML) era, the expectations are high, and experts foresee that AI/ML shows potential for diagnosing, managing and treating a wide variety of medical conditions. However, the obstacles for implementation of AI/ML in daily clinical practice are numerous, especially regarding the regulation of these technologies. Therefore, we provide an insight into the currently available AI/ML-based medical devices and algorithms that have been approved by the US Food & Drugs Administration (FDA). We aimed to raise awareness of the importance of regulatory bodies, clearly stating whether a medical device is AI/ML based or not. Cross-checking and validating all approvals, we identified 64 AI/ML based, FDA approved medical devices and algorithms. Out of those, only 29 (45%) mentioned any AI/ML-related expressions in the official FDA announcement. The majority (85.9%) was approved by the FDA with a 510(k) clearance, while 8 (12.5%) received de novo pathway clearance and one (1.6%) premarket approval (PMA) clearance. Most of these technologies, notably 30 (46.9%), 16 (25.0%), and 10 (15.6%) were developed for the fields of Radiology, Cardiology and Internal Medicine/General Practice respectively. We have launched the first comprehensive and open access database of strictly AI/ML-based medical technologies that have been approved by the FDA. The database will be constantly updated.

Objective: Identify key demographic factors and modes of follow-up in surgical survey response. Summary Background Data: Surveys are widely used in surgery to assess patient and procedural outcomes, but response rates vary widely which compromises study quality. Currently there is no consensus as to what the average response rate is and which factors are associated with higher response rates. Methods: The National Library of Medicine (MEDLINE/PubMed) was systematically searched from Januray 1, 2007 until February 1, 2020 using the following strategy: ((( questionnaire) OR survey) AND “response rate”) AND ( surgery OR surgical ). Original survey studies from surgical(-related) fields reporting on response rate were included. Through one-way analysis of variance we present mean response rate per survey mode over time, number of additional contacts, country of origin, and type of interviewee. Results: The average response is 70% over 811 studies in patients and 53% over 1746 doctor surveys. In-person surveys yield an average 76% response rate, followed by postal (65%) and online (46% web-based vs 51% email) surveys. Patients respond significantly more often than doctors to surveys by mail ( P < 0.001), email ( P = 0.003), web-based surveys ( P < 0.001) and mixed mode surveys ( P = 0.006). Additional contacts significantly improve response rate in email ( P = 0.26) and web-based ( P = 0.041) surveys in doctors. A wide variation in response rates was identified between countries. Conclusions: Every survey is unique, but the main commonality between studies is response rate. Response rates appear to be highly dependent on type of survey, follow-up, geography, and interviewee type.

Background. Frailty is a multidimensional condition and is the result of the body’s age-associated decline in physical, cognitive, physiological, and immune reserves. The aim of this systematic review is to assess the quality of evidence of the included studies, determine the prevalence of frailty among kidney transplant candidates, and evaluate the relationship between frailty and associated patient characteristics and outcomes after kidney transplantation. Methods. A systematic search was performed for relevant literature on frailty and kidney transplantation. This was followed by a meta-analysis for patient characteristics and outcomes reported by a minimum of 2 studies including mean age, gender, mean body mass index, type of kidney transplantation, dialysis, previous kidney transplantation, comorbidities, hypertension, race, preemptive kidney transplantation, delayed graft function, and length of stay. Results. A total of 18 studies were included in the systematic review and 14 of those studies were suitable for meta-analysis. The overall pooled prevalence of frailty before transplantation was estimated at 17.1% (95% confidence interval [CI], 15.4-18.7). Frailty was significantly associated with higher age (mean difference, 3.6; 95% CI, 1.4-5.9), lower rate of preemptive transplantation (relative risk, 0.60; 95% CI, 0.4-0.9), longer duration of delayed graft function (relative risk, 1.80; 95% CI, 1.1-3.0), and length of stay longer than 2 wk (odds ratio, 1.64; 95% CI, 1.2-2.3). Conclusions. One in 6 kidney transplant recipients is frail before transplantation. The presence of frailty is associated with lower rates of preemptive transplantation, older recipient age, higher rates of delayed graft function, and longer length of stay. Future research is required to explore the association of frailty with other adverse outcomes after kidney transplantation and the effects of intervention programs to improve the different frailty domains.