The need for a system view to regulate artificial intelligence/machine learning-based software as medical device

Artificial intelligence (AI) and Machine learning (ML) systems in medicine are poised to significantly improve health care, for example, by offering earlier diagnoses of diseases or recommending optimally individualized treatment plans. However, the emergence of AI/ML in medicine also creates challenges, which regulators must pay attention to. Which medical AI/ML-based products should be reviewed by regulators? What evidence should be required to permit marketing for AI/ML-based software as a medical device (SaMD)? How can we ensure the safety and effectiveness of AI/ML-based SaMD that may change over time as they are applied to new data? The U.S. Food and Drug Administration (FDA), for example, has recently proposed a discussion paper to address some of these issues. But it misses an important point: we argue that regulators like the FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition

Comparison of two soil tillage treatments for winter barley-soybean growing based only on residual nitrogen after soybean

The winter barley crop growing has not been adequately researched regarding soil tillage systems, especially in crop rotation with the soybean, both crops gaining importance as food or fodder. Also, productivity of such crop rotation in low nitrogen environment is especially interesting for organic crop growing, where mineral nitrogen fertilization is not allowed. The research on two soil tillage systems, the conventional one, based on mouldboard ploughing (PLOW) and reduced soil tillage, based on discharrowing (DISC), with no other nitrogen source except symbiotic soybean bacterial fixation, was conducted at the experimental site Bokšić (Croatia), during the seasons 2004/05 and 2005/06. Results showed low but stable yields of winter barley, between 2.1 and 2.6 t ha-1, where PLOW treatment recorded lower yield than DISC in 2005, and usual soybean yields (between 2.8 and 3.4 t ha-1), with higher soybean grain yields for PLOW only in 2006. The absolute mass and hectolitre mass did not show any statistical differences among treatments either

The Need for a System View to Regulate Artificial Intelligence/Machine Learning-Based Software as Medical Devices

Artificial intelligence (AI) and Machine learning (ML) systems in medicine are poised to significantly improve health care, for example, by offering earlier diagnoses of diseases or recommending optimally individualized treatment plans. However, the emergence of AI/ML in medicine also creates challenges, which regulators must pay attention to. Which medical AI/ML-based products should be reviewed by regulators? What evidence should be required to permit marketing for AI/ML-based software as a medical device (SaMD)? How can we ensure the safety and effectiveness of AI/ML-based SaMD that may change over time as they are applied to new data? The U.S. Food and Drug Administration (FDA), for example, has recently proposed a discussion paper to address some of these issues. But it misses an important point: we argue that regulators like the FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition

Corrosion Behavior of the As-cast and Heat-treated ZA27 Alloy

Corrosion behaviour of the as-cast and heat-treated ZA27 alloy was examined. The alloy was prepared by conventional melting and casting route and then thermally processed by applying T4 heat treatment regime (solutionizing at 370 °C for 3 hours followed by water quenching and natural aging). Corrosion rate of the as-cast and heat-treated ZA27 alloy was determined in 3.5 wt. % NaCl solution through immersion test using both weight loss method and polarization resistance measurements. It was shown that applied thermal treatment resulted in increased ductility of the heat-treated alloy and had a small beneficial effect on the corrosion resistance of ZA27 alloy

How AI can learn from the law: putting humans in the loop only on appeal

While the literature on putting a “human in the loop” in artificial intelligence (AI) and machine learning (ML) has grown significantly, limited attention has been paid to how human expertise ought to be combined with AI/ML judgments. This design question arises because of the ubiquity and quantity of algorithmic decisions being made today in the face of widespread public reluctance to forgo human expert judgment. To resolve this conflict, we propose that human expert judges be included via appeals processes for review of algorithmic decisions. Thus, the human intervenes only in a limited number of cases and only after an initial AI/ML judgment has been made. Based on an analogy with appellate processes in judiciary decision-making, we argue that this is, in many respects, a more efficient way to divide the labor between a human and a machine. Human reviewers can add more nuanced clinical, moral, or legal reasoning, and they can consider case-specific information that is not easily quantified and, as such, not available to the AI/ML at an initial stage. In doing so, the human can serve as a crucial error correction check on the AI/ML, while retaining much of the efficiency of AI/ML’s use in the decision-making process. In this paper, we develop these widely applicable arguments while focusing primarily on examples from the use of AI/ML in medicine, including organ allocation, fertility care, and hospital readmission

Lift-off dynamics in a simple jumping robot

We study vertical jumping in a simple robot comprising an actuated mass-spring arrangement. The actuator frequency and phase are systematically varied to find optimal performance. Optimal jumps occur above and below (but not at) the robot's resonant frequency $f_0$. Two distinct jumping modes emerge: a simple jump which is optimal above $f_0$ is achievable with a squat maneuver, and a peculiar stutter jump which is optimal below $f_0$ is generated with a counter-movement. A simple dynamical model reveals how optimal lift-off results from non-resonant transient dynamics.Comment: 4 pages, 4 figures, Physical Review Letters, in press (2012

Matrix-assisted laser desorption/ionization time of flight mass spectrometry identification of Vibrio (Listonella) anguillarum isolated from sea bass and sea bream

Vibrio (Listonella) anguillarum is a pathogenic bacterium causing septicaemia in a wide range of marine organisms and inducing severe mortalities, thus it is crucial to conduct its accurate and rapid identification. The aim of this study was to assess MALDI-TOF MS as a method of choice for identification of clinical V. anguillarum isolates from affected marine fish. Since the method accuracy might be influenced by the type of the medium used, as well as by the incubation conditions, we tested V. anguillarum isolates grown on standard media with and without the addition of NaCl, cultured at three incubation temperatures, and at three incubation periods. The best scores were retrieved for V. anguillarum strains grown on NaCl-supplemented tryptone soy agar (TSA) at 22°C and incubated for 48h (100% identification to species level; overall score 2.232), followed by incubation at 37°C and 48h (100% to species level; score 2.192). The strains grown on non-supplemented TSA gave the best readings when incubated at 22°C for 72h (100% identification to species level; overall score 2.182), followed by incubation at 15°C for 72h (100% to species level; score 2.160). Unreliable identifications and no-identifications were growing with the incubation duration at 37°C, on both media, amounting to 88.89% for 7d incubation on supplemented TSA, and 92.60% for 7d incubation on non-supplemented TSA. The age of the cultured strains and use of media significantly impacted the mass spectra, demonstrating that for reliable identification, MALDI-TOF MS protein fingerprinting with the on-target extraction should be performed on strains grown on a NaCl-supplemented medium at temperatures between 15 and 22°C, incubated for 48-72 hours

Capacitive Spring Softening in Single-Walled Carbon Nanotube Nanoelectromechanical Resonators

We report the capacitive spring softening effect observed in single-walled carbon nanotube (SWNT) nanoelectromechanical (NEM) resonators. The nanotube resonators adopt dual-gate configuration with both bottom-gate and side-gate capable of tuning the resonance frequency through capacitive coupling. Interestingly, downward resonance frequency shifting is observed with increasing side-gate voltage, which can be attributed to the capacitive softening of spring constant. Furthermore, in-plane vibrational modes exhibit much stronger spring softening effect than out-of-plan modes. Our dual-gate design should enable the differentiation between these two types of vibrational modes, and open up new possibility for nonlinear operation of nanotube resonators.Comment: 12 pages/ 3 figure