Liability for Use of Artificial Intelligence in Medicine

While artificial intelligence has substantial potential to improve medical practice, errors will certainly occur, sometimes resulting in injury. Who will be liable? Questions of liability for AI-related injury raise not only immediate concerns for potentially liable parties, but also broader systemic questions about how AI will be developed and adopted. The landscape of liability is complex, involving health-care providers and institutions and the developers of AI systems. In this chapter, we consider these three principal loci of liability: individual health-care providers, focused on physicians; institutions, focused on hospitals; and developers

How Much Can Potential Jurors Tell Us About Liability for Medical Artificial Intelligence?

Artificial intelligence (AI) is rapidly entering medical practice, whether for risk prediction, diagnosis, or treatment recommendation. But a persistent question keeps arising: What happens when things go wrong? When patients are injured, and AI was involved, who will be liable and how? Liability is likely to influence the behavior of physicians who decide whether to follow AI advice, hospitals that implement AI tools for physician use, and developers who create those tools in the first place. If physicians are shielded from liability (typically medical malpractice liability) when they use AI tools, even if patient injury results, they are more likely to rely on these tools, even if the AI recommendations are counterintuitive. On the other hand, if physicians face liability from deviating from standard practice, whether an AI recommends something different or not, the adoption of AI is likely to be slower, and counterintuitive rejections— even correct ones—are likely to be rejected. In this issue of The Journal of Nuclear Medicine, Tobia et al. offer an important empiric look at this question, which has significant implications as to whether and when AI will come into clinical use

Potential Liability for Physicians Using Artificial Intelligence

Artificial intelligence (AI) is quickly making inroads into medical practice, especially in forms that rely on machine learning, with a mix of hope and hype. Multiple AI-based products have now been approved or cleared by the US Food and Drug Administration (FDA), and health systems and hospitals are increasingly deploying AI-based systems. For example, medical AI can support clinical decisions, such as recommending drugs or dosages or interpreting radiological images.2 One key difference from most traditional clinical decision support software is that some medical AI may communicate results or recommendations to the care team without being able to communicate the underlying reasons for those results. Medical AI may be trained in inappropriate environments, using imperfect techniques, or on incomplete data. Even when algorithms are trained as well as possible, they may, for example, miss a tumor in a radiological image or suggest the incorrect dose for a drug or an inappropriate drug. Sometimes, patients will be injured as a result. In this Viewpoint, we discuss when a physician could likely be held liable under current law when using medical AI

Facharztweiterbildung Psychiatrie und Psychotherapie: Problemorientiertes Lernen - Evaluation eines Modellprojekts

Zusammenfassung: Die Betonung individueller Lernbedürfnisse, der Fähigkeit zur Lösung komplexer klinischer Probleme sowie einer von interkollegialer Kommunikation geprägten professionellen Grundhaltung durch das problemorientierte Lernen (POL) spricht für dessen Eignung als didaktisches Format in der Facharztweiterbildung. Dennoch wurde es bisher selten hierfür eingesetzt. Im Rahmen dieses Modellprojektes wurde das POL in das Kurrikulum der strukturierten Facharztweiterbildung Psychiatrie und Psychotherapie aufgenommen und über einen Zeitraum von 12Monaten mittels strukturierter Fragebögen evaluiert. Es fanden im Evaluationszeitraum 41POL-Kurse statt, an denen insgesamt 447 Assistenzärzte teilnahmen. Die Teilnehmer und die Tutoren bewerteten 19 von 21 erfragten Aspekten der POL-Kurse als gut bis sehr gut (Mittelwert auf einer 5-stufigen Likert-Skala >4). Insgesamt wurde das POL als besonders geeignet für die Weiterbildung eingeschätzt (Teilnehmer 4,5±0,8; Tutoren 5,0±0,2). Die Ergebnisse dieses Modellprojekts sprechen für die Eignung des POL als Teil eines vielfältigen Weiterbildungsangebots, um den Praxisbezug und die Anwendbarkeit des Wissens im klinischen Alltag zu stärke

Towards automatic pulmonary nodule management in lung cancer screening with deep learning

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.Comment: Published on Scientific Report

A Search for Binary Active Galactic Nuclei: Double-Peaked [OIII] AGN in the Sloan Digital Sky Survey

We present AGN from the Sloan Digital Sky Survey (SDSS) having double-peaked profiles of [OIII] 5007,4959 and other narrow emission-lines, motivated by the prospect of finding candidate binary AGN. These objects were identified by means of a visual examination of 21,592 quasars at z < 0.7 in SDSS Data Release 7 (DR7). Of the spectra with adequate signal-to-noise, 148 spectra exhibit a double-peaked [OIII] profile. Of these, 86 are Type 1 AGN and 62 are Type 2 AGN. Only two give the appearance of possibly being optically resolved double AGN in the SDSS images, but many show close companions or signs of recent interaction. Radio-detected quasars are three times more likely to exhibit a double-peaked [OIII] profile than quasars with no detected radio flux, suggesting a role for jet interactions in producing the double-peaked profiles. Of the 66 broad line (Type 1) AGN that are undetected in the FIRST survey, 0.9% show double peaked [OIII] profiles. We discuss statistical tests of the nature of the double-peaked objects. Further study is needed to determine which of them are binary AGN rather than disturbed narrow line regions, and how many additional binaries may remain undetected because of insufficient line-of-sight velocity splitting. Previous studies indicate that 0.1% of SDSS quasars are spatially resolved binaries, with typical spacings of ~10 to 100 kpc. If a substantial fraction of the double-peaked objects are indeed binaries, then our results imply that binaries occur more frequently at smaller separations (< 10 kpc). This suggests that simultaneous fueling of both black holes is more common as the binary orbit decays through these spacings.Comment: 33 pages, 5 figures, LaTeX. Major revisions. Accepted for publication in ApJ

Colloid transport through soil and other porous media under transient flow conditions—A review

Understanding colloid transport in porous media under transient-flow conditions is crucial in understanding contaminant transport in soil or the vadose zone where flow conditions vary constantly. In this article, we provide a review of experimental studies, numerical approaches, and new technologies available to determine the transport of colloids in transient flow. Experiments indicate that soil structure and preferential flow are primary factors. In undisturbed soils with preferential flow pathways, macropores serve as main conduits for colloid transport. In homogeneously packed soil, the soil matrix often serves as filter. At the macroscale, transient flow facilitates colloid transport by frequently disturbing the force balance that retains colloids in the soil as indicated by the offset between colloid breakthrough peaks and discharge peaks. At the pore-scale and under saturated condition, straining, and attachment at solid–water interfaces are the main mechanisms for colloid retention. Variably saturated conditions add more complexity, such as immobile water zones, film straining, attachment to air–water interfaces, and air–water–solid contact lines. Filter ripening, size exclusion, ionic strength, and hydrophobicity are identified as the most influential factors. Our review indicates that microscale and continuum-scale models for colloid transport under transient-flow conditions are rare, compared to the numerous steady-state models. The few transient flow models that do exist are highly parameterized and suffer from a lack of a priori information of required pore-scale parameters. However, new techniques are becoming available to measure colloid transport in real-time and in a nondestructive way that might help to better understand transient flow colloid transport. This article is categorized under: Science of Water &gt; Hydrological Processes Science of Water &gt; Water Quality