Should the Use of Adaptive Machine Learning Systems in Medicine be Classified as Research?

A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called "update problem," which concerns how regulators should approach systems whose performance and parameters continue to change even after they have received regulatory approval. In this paper, we draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong prima facie case that the use of continuous learning in medical ML systems should be categorized, and regulated, as research and that individuals whose treatment involves such systems should be treated as research subjects.

A spiking neural network with continuous local learning for robust online brain machine interface.

Objective.Spiking neural networks (SNNs) are powerful tools that are well suited for brain machine interfaces (BMI) due to their similarity to biological neural systems and computational efficiency. They have shown comparable accuracy to state-of-the-art methods, but current training methods require large amounts of memory, and they cannot be trained on a continuous input stream without pausing periodically to perform backpropagation. An ideal BMI should be capable training continuously without interruption to minimize disruption to the user and adapt to changing neural environments.Approach.We propose a continuous SNN weight update algorithm that can be trained to perform regression learning with no need for storing past spiking events in memory. As a result, the amount of memory needed for training is constant regardless of the input duration. We evaluate the accuracy of the network on recordings of neural data taken from the premotor cortex of a primate performing reaching tasks. Additionally, we evaluate the SNN in a simulated closed loop environment and observe its ability to adapt to sudden changes in the input neural structure.Main results.The continuous learning SNN achieves the same peak correlation (ρ=0.7) as existing SNN training methods when trained offline on real neural data while reducing the total memory usage by 92%. Additionally, it matches state-of-the-art accuracy in a closed loop environment, demonstrates adaptability when subjected to multiple types of neural input disruptions, and is capable of being trained online without any prior offline training.Significance.This work presents a neural decoding algorithm that can be trained rapidly in a closed loop setting. The algorithm increases the speed of acclimating a new user to the system and also can adapt to sudden changes in neural behavior with minimal disruption to the user.

Ethics in Clinical Orthopedic Surgery.

Introduction: Orthopedic surgeons, owing to their specialized role, have a set of medical and moral responsibilities that span beyond the confines of the operating room. The primary objective of this exploration is to emphasize the pivotal ethical and professional standards that these surgeons should uphold. Methodology: We derived key ethical and professional aspects by reviewing standard medical practices, professional guidelines, and through consultations with senior orthopedic professionals. These aspects covered both the conduct inside the surgery room and the interpersonal relations outside. Results: Several core areas of conduct were identified.Patient-Centered Care: Prioritizing the holistic well-being of the patient.Communication: Ensuring that all communications are both transparent and respectful.Informed Consent: Properly securing consent after ensuring the patient is adequately informed.Confidentiality: Taking measures to safeguard patient information.Professional Behavior: Upholding the highest standards of professional conduct.Continuous Learning: Remaining committed to updating skills and enhancing competence.Interpersonal Relations: Building healthy and constructive relationships with industry representatives, professional peers, and hospital staff.Personal Life Balance: Recognizing the importance of a balanced personal and professional life for holistic well-being. Conclusion: For Orthopedic surgeons, strict adherence to the outlined ethical and professional principles is essential. Such commitment not only ensures the trust and safety of patients but also serves to maintain and elevate the prestigious standing of the orthopedic community in the broader medical landscape.

Corrigendum: Studying Brains. What could neurometaphysics be to NeurotechEU?

[This corrects the article DOI: 10.3389/fnins.2023.1155547.].

The Unknown Abnormal Condition Monitoring Method for Pumped-Storage Hydroelectricity.

Pumped-storage hydroelectricity (PSH) is a facility that stores energy in the form of the gravitational potential energy of water by pumping water from a lower to a higher elevation reservoir in a hydroelectric power plant. The operation of PSH can be divided into two states: the turbine state, during which electric energy is generated, and the pump state, during which this generated electric energy is stored as potential energy. Additionally, the condition monitoring of PSH is generally challenging because the hydropower turbine, which is one of the primary components of PSH, is immersed in water and continuously rotates. This study presents a method that automatically detects new abnormal conditions in target structures without the intervention of experts. The proposed method automatically updates and optimizes existing abnormal condition classification models to accommodate new abnormal conditions. The performance of the proposed method was evaluated with sensor data obtained from on-site PSH. The test results show that the proposed method detects new abnormal PSH conditions with an 85.89% accuracy using fewer than three datapoints and classifies each condition with a 99.73% accuracy on average.

Evaluation of a workplace assessment method designed to improve self-assessment in operative dentistry: a quasi-experiment.

BACKGROUND: Dental education has placed continued emphasis on self-regulated learning (SRL) and its subprocess, self-assessment. This study set out to evaluate the effectiveness of a novel workplace assessment method in developing trainees' self-assessment of operative procedures. METHODS: A Direct Observation of Procedural Skills (DOPS) form was modified for the use and measurement of self-assessment. Participants were trained on how to conduct self-assessment using the designed assessment form and its grading rubric. Feedback and feedforward sessions were given to address self-assessment and performance issues. A P-value less than 0.10 was considered significant and the confidence level was set at 90%. RESULTS: Thirty-two Year 5 dental students with an age mean of 22.45 (SD = 0.8) completed five self DOPS encounters during the clinical operative dentistry module in 2022. The aggregated total deviation (absolute difference) between self-assessment and teacher assessment decreased consistently in the five assessment encounters with a significant mean difference and a medium effect size (P = 0.064, partial Eta squared = 0.069). Participants' self-assessment accuracy differed from one skill to another and their ability to identify areas of improvement as perceived by teachers improved significantly (P = 0.011, partial Eta squared = 0.099). Participants' attitudes towards the assessment method were positive. CONCLUSIONS: The findings suggest that the self DOPS method was effective in developing participants' ability to self-assess. Future research should explore the effectiveness of this assessment method in a wider range of clinical procedures.

Studying brains what could neurometaphysics be to NeurotechEU?

NeurotechEU has introduced a new conceptual hierarchy for neuroscientific research and its applications along 8 different core research areas, including the so-called 'neurometaphysics'. This paper explores this concept of neurometaphysics, its topics and its potential approach. It warns against an endemic Cartesianism in (neuro)science that somehow seems to survive explicit refutations by implicitly persisting in our conceptual scheme. Two consequences of this persisting Cartesian legacy are discussed; the isolated brain assumption and the idea that activity requires identifiable neural 'decisions'. Neuropragmatism is introduced as offering the promise of progress in neurometaphysics, by emphasizing that (1) studying brains interact organically with their environment and (2) studying brains requires an attitude of continuous learning.

#MedEd: Mapping the Current Landscape of Medical Education Discourse and Stakeholder Participation Across Social Media Platforms.

Background Medical education is a constantly evolving and multifaceted field that requires ongoing discussion and innovation. Social media platforms have emerged as a popular medium for disseminating information and engaging in professional discourse among medical educators. In particular, the hashtag #MedEd has gained widespread recognition amongst individuals and organizations within the medical education community. Our objective is to gain insights into the types of information and discussions surrounding medical education, as well as the individuals or organizations involved in these conversations. Methods Searches were conducted across major social media platforms, including Twitter, Instagram, and Facebook, using the hashtag #MedEd. The top 20 posts posted on these platforms were analyzed through a reflexive thematic analysis approach utilizing the Braun and Clarke method. Furthermore, an examination was conducted on the profiles of those responsible for posting the aforementioned top posts, to ascertain the degree of participation from individuals versus organizations within the broader discourse pertaining to the topic. Results Our analysis revealed three thematic categories associated with the usage of the #MedEd hashtag, including discussions on "continuous learning and medical case presentations," "medical specialties and topics," and "medical education pedagogy." The analysis revealed that social media can serve as a valuable platform for medical education by providing access to a diverse range of learning resources, fostering collaboration and professional networking, and providing innovative teaching methods. Furthermore, profile analysis showed that individuals were more actively involved in the discussion of medical education topics on social media compared to organizations across all three platforms. Conclusion Our study highlights the significant role that social media platforms play in facilitating the exchange of information and ideas within the medical education community. The hashtag #MedEd serves as a means of connecting individuals and organizations across the globe, enabling them to engage in professional discourse and stay informed on the latest developments in the field. Our findings suggest that a better understanding of the thematic categories and stakeholders involved in medical education discussions on social media can aid educators, learners, and organizations in enhancing their engagement with this dynamic field.

A novel discrete learning-based intelligent methodology for breast cancer classification purposes.

Classification is one of the most significant subfields of data mining that has been successfully applied to various applications. The literature has expended substantial effort to present more efficient and accurate classification models. Despite the diversity of the proposed models, they were all created using the same methodology, and their learning processes ignored a fundamental issue. In all existing classification model learning processes, a continuous distance-based cost function is optimized to estimate the unknown parameters. The classification problem's objective function is discrete. Consequently, applying a continuous cost function to a classification problem with a discrete objective function is illogical or inefficient. This paper proposes a novel classification methodology utilizing a discrete cost function in the learning process. To this end, one of the most popular intelligent classification models, the multilayer perceptron (MLP), is used to implement the proposed methodology. Theoretically, the classification performance of the proposed discrete learning-based MLP (DIMLP) model is not dissimilar to that of its continuous learning-based counterpart. Nevertheless, in this study, to demonstrate the efficacy of the DIMLP model, it was applied to several breast cancer classification datasets, and its classification rate was compared to that of the conventional continuous learning-based MLP model. The empirical results indicate that the proposed DIMLP model outperforms the MLP model across all datasets. The results demonstrate that the presented DIMLP classification model achieves an average classification rate of 94.70 %, a 6.95 % improvement over the classification rate of the traditional MLP model, which was 88.54 %. Therefore, the classification approach proposed in this study can be utilized as an alternative learning process in intelligent classification methods for medical decision-making and other classification applications, particularly when more accurate results are required.

Multi-Scopic Cognitive Memory System for Continuous Gesture Learning.

With the advancement of artificial intelligence technologies in recent years, research on intelligent robots has progressed. Robots are required to understand human intentions and communicate more smoothly with humans. Since gestures can have a variety of meanings, gesture recognition is one of the essential issues in communication between robots and humans. In addition, robots need to learn new gestures as humans grow. Moreover, individual gestures vary. Because catastrophic forgetting occurs in training new data in traditional gesture recognition approaches, it is necessary to preserve the prepared data and combine it with further data to train the model from scratch. We propose a Multi-scopic Cognitive Memory System (MCMS) that mimics the lifelong learning process of humans and can continuously learn new gestures without forgetting previously learned gestures. The proposed system comprises a two-layer structure consisting of an episode memory layer and a semantic memory layer, with a topological map as its backbone. The system is designed with reference to conventional continuous learning systems in three ways: (i) using a dynamic architecture without setting the network size, (ii) adding regularization terms to constrain learning, and (iii) generating data from the network itself and performing relearning. The episode memory layer clusters the data and learns their spatiotemporal representation. The semantic memory layer generates a topological map based on task-related inputs and stores them as longer-term episode representations in the robot's memory. In addition, to alleviate catastrophic forgetting, the memory replay function can reinforce memories autonomously. The proposed system could mitigate catastrophic forgetting and perform continuous learning by using both machine learning benchmark datasets and real-world data compared to conventional methods.

Influencing aspects of clinical nurses' interactive continuous learning behaviour based on in-service online network videos: A grounded theory approach.

OBJECTIVES: This study explores the influencing aspects of nurses' continuous interactive learning behaviour based on In-Service online network videos to facilitate their knowledge development and improve their engagement in online learning. Also, to provide a theoretical model for enhancing the quality of learning and teaching by promoting online nursing education. DESIGN: The qualitative research design of grounded theory methodology (Glaser and Strauss, 1967) was used to develop a theory of clinical nurses' interactive continuous learning behaviours. METHOD: A grounded theory approach and qualitative research method were adopted in this research. A three-level coding technique was used to extract data, which involved collecting 16,018 pieces of "Synchronous online feedback data" original data from interactive online in-service learning videos. The synchronous online feedback data were analysed using a constant comparative method and then utilised to construct the theoretical model of influencing aspects for nurses' interactive continuous learning. There were 11,083 Synchronous online feedback data of Clinical Nurses (N = 132 participants) based on 32 interactive learning video series from a single centre. The sampling method was based on initial purposive and then theoretical sampling techniques. The data were analysed and extracted using a three-level coding technique with a constant comparison analysis method. RESULTS: The study identified four major categories of influencing aspects for nurses' satisfaction in online learning. These categories include 1. individual aspects; 2. curriculum aspects; 3. teachers' aspects; and 4. nurses' interactive behaviour. These aspects were found to influence the learning satisfaction among nurses and nurses' interactive continuous learning behaviours. CONCLUSION: The findings provide a reference for maximising the value of In-Service online video learning resources and the sustainable development of educational informatisation. In addition, interactive continuous learning behaviour can promote adherence to a positive learning attitude and build an excellent online learning ecology.

A continuous learning approach to brain tumor segmentation: integrating multi-scale spatial distillation and pseudo-labeling strategies.

Introduction: This study presents a novel continuous learning framework tailored for brain tumour segmentation, addressing a critical step in both diagnosis and treatment planning. This framework addresses common challenges in brain tumour segmentation, such as computational complexity, limited generalisability, and the extensive need for manual annotation. Methods: Our approach uniquely combines multi-scale spatial distillation with pseudo-labelling strategies, exploiting the coordinated capabilities of the ResNet18 and DeepLabV3+ network architectures. This integration enhances feature extraction and efficiently manages model size, promoting accurate and fast segmentation. To mitigate the problem of catastrophic forgetting during model training, our methodology incorporates a multi-scale spatial distillation scheme. This scheme is essential for maintaining model diversity and preserving knowledge from previous training phases. In addition, a confidence-based pseudo-labelling technique is employed, allowing the model to self-improve based on its predictions and ensuring a balanced treatment of data categories. Results: The effectiveness of our framework has been evaluated on three publicly available datasets (BraTS2019, BraTS2020, BraTS2021) and one proprietary dataset (BraTS_FAHZU) using performance metrics such as Dice coefficient, sensitivity, specificity and Hausdorff95 distance. The results consistently show competitive performance against other state-of-the-art segmentation techniques, demonstrating improved accuracy and efficiency. Discussion: This advance has significant implications for the field of medical image segmentation. Our code is freely available at https://github.com/smallboy-code/A-brain-tumor-segmentation-frameworkusing-continual-learning.

Educational Needs in Oncology Nursing: A Scoping Review.

Care in oncology requires both technical and psychosocial skills by nursing staff, so continuous learning is necessary. Evidence suggests there are some educational gaps in oncology nursing staff, and continuing educational interventions have been effective in overcoming these deficiencies. Aim: to determine the basic educational lines that a continuous training program should have for oncology nurses. A bibliographic review study was carried out in two phases from October 2020 to January 2021. In a first phase, the main databases were analyzed: PubMed, Web of Science, Dialnet and Medline, following the PRISMA methodology; and subsequently, an analysis of the most important thematic nuclei that a training program in cancer nursing should contain. The DAFO matrix and the Hanlon prioritization method were used. Four competencies that every oncology nurse should have were described: communication, coping, self-direction of learning and technical health. The thematic contents that a training program should contain were then determined, and aspects such as stress prevention and burnout, adequate communication with patient and family, and continuous educational and technical skills were considered. The results found suggest that there are deficiencies in the education of nursing staff. Continuing education programs are effective in supplementing them. They should develop the four skills described in the results section.

Evaluating and Improving Neonatal Gentamicin Pharmacokinetic Models Using Aggregated Routine Clinical Care Data.

Model-informed precision dosing (MIPD) can aid dose decision-making for drugs such as gentamicin that have high inter-individual variability, a narrow therapeutic window, and a high risk of exposure-related adverse events. However, MIPD in neonates is challenging due to their dynamic development and maturation and by the need to minimize blood sampling due to low blood volume. Here, we investigate the ability of six published neonatal gentamicin population pharmacokinetic models to predict gentamicin concentrations in routine therapeutic drug monitoring from nine sites in the United State (n = 475 patients). We find that four out of six models predicted with acceptable levels of error and bias for clinical use. These models included known important covariates for gentamicin PK, showed little bias in prediction residuals over covariate ranges, and were developed on patient populations with similar covariate distributions as the one assessed here. These four models were refit using the published parameters as informative Bayesian priors or without priors in a continuous learning process. We find that refit models generally reduce error and bias on a held-out validation data set, but that informative prior use is not uniformly advantageous. Our work informs clinicians implementing MIPD of gentamicin in neonates, as well as pharmacometricians developing or improving PK models for use in MIPD.

Examining the Influence of Using First-Person View Drones as Auxiliary Devices in Matte Painting Courses on College Students' Continuous Learning Intention.

In terms of the teaching process of matte painting, it is essential for students to develop a sound understanding of the relationship between virtual and physical environments. In this study, first-person view (FPV) drones are applied to matte painting courses to evaluate the effectiveness of the teaching, and to propose more effective design suggestions for FPV drones that are more suitable for teaching. This provides students with a better learning environment using a digital education system. The results of the study indicate that the flow experience, learning interest, and continuous learning intention of students who use FPV drones in matte painting are significantly greater than those of students who only utilize traditional teaching methods. Furthermore, the technology incentive model (TIM) was developed in this study after being verified by the structural equation model. The results demonstrate that the second-order construct 'technology incentive' comprising perceived interactivity, perceived vividness, and novel experience positively influence students' learning interest and continuous learning intentions under the mediation of flow experience.

Impact of continuous learning on diagnostic breast MRI AI: evaluation on an independent clinical dataset.

Purpose: We demonstrate continuous learning and assess its impact on the performance of artificial intelligence of breast dynamic contrast-enhanced magnetic resonance imaging in the task of distinguishing malignant from benign lesions on an independent clinical test dataset. Approach: The study included 1979 patients with 1990 lesions who underwent breast MR imaging during 2015, 2016, and 2017, retrospectively collected under an IRB-approved protocol; there were 1494 malignant and 496 benign lesions based on histopathology. AI was conducted in the task of distinguishing malignant and benign lesions, and independent testing was performed to assess the effect of increasing the numbers of training cases. Five training sets mimicking clinical implementation of continuous AI learning included cases from (1) first quarter of 2015, (2) first half of 2015, (3) all 2015, (4) all 2015 and first half of 2016, and (5) all 2015 and 2016. All classifiers were evaluated on the 2017 independent test set. The area under the ROC curve (AUC) served as the performance metric and was calculated over all lesions in the test set, as well as only mass lesions and only non-mass enhancements. The Mann-Kendall test was used to determine if continuous learning resulted in a positive trend in classification performance. P < 0.05 was considered to be statistically significant. Results: Over the continuous training period, the selected feature subsets tended to become more similar and stable. Performance of the five training conditions on the independent test dataset yielded AUCs of 0.86 (95% CI: [0.83,0.90]), 0.87 (95% CI: [0.83,0.90]), 0.88 (95% CI: [0.84,0.91]), 0.89 (95% CI: [0.85,0.92]), and 0.89 (95% CI: [0.86,0.92]). The Mann-Kendall test indicated a statistically significant positive trend ( P = 0.0167 ) in classification performance with continuous learning. Conclusions: Improved diagnostic performance over time was observed when continuous learning of AI was implemented on an independent clinical test dataset.

A study on the effects of perceived playfulness and emotional support in interactive learning environments for German language acquisition-A role-playing library system case.

Introduction: This study examines the emotional support offered by the non-player characters (NPCs) in an interactive learning environment, as well as the effects of the perceived playfulness of the interactive system on German language learning. Method: We developed a role-playing library system to serve this purpose. 2,377 Chinese Internet users were surveyed using online questionnaire. Results: A theoretical model of emotion- driven learning (ELM) was proposed based on the analysis results of valid recovered data. Additionally, NPCs were found to be effective in improving learning outcomes through emotional support. Discussion: An interactive education system may be able to enhance the perceived playfulness of learning in order to enhance the learning experience.

A Continuous Learning Approach for Real-Time Network Intrusion Detection.

Network intrusion detection is becoming a challenging task with cyberattacks that are becoming more and more sophisticated. Failing the prevention or detection of such intrusions might have serious consequences. Machine learning approaches try to recognize network connection patterns to classify unseen and known intrusions but also require periodic re-training to keep the performances at a high level. In this paper, a novel continuous learning intrusion detection system, called Soft-Forgetting Self-Organizing Incremental Neural Network (SF-SOINN), is introduced. SF-SOINN, besides providing continuous learning capabilities, is able to perform fast classification, is robust to noise, and it obtains good performances with respect to the existing approaches. The main characteristic of SF-SOINN is the ability to remove nodes from the neural network based on their utility estimate. SF-SOINN has been validated on the well-known NSL-KDD and CIC-IDS-2017 intrusion detection datasets as well as on some artificial data to show the classification capability on more general tasks.

Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions.

A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from local Hebbian plasticity rules is not very well understood. The goal of this work is to discover interpretable local Hebbian learning rules that can provide autonomous global learning. To achieve this, we use a discrete representation to encode the learning rules in a finite search space. These rules are then used to perform synaptic changes, based on the local interactions of the neurons. We employ genetic algorithms to optimize these rules to allow learning on two separate tasks (a foraging and a prey-predator scenario) in online lifetime learning settings. The resulting evolved rules converged into a set of well-defined interpretable types, that are thoroughly discussed. Notably, the performance of these rules, while adapting the ANNs during the learning tasks, is comparable to that of offline learning methods such as hill climbing.

APDS Presidential Address.

My goal today is to make an argument that the APDS: is at a crossroads because of unprecedented changes; is in a great position to succeed; seeing the need for change we should nevertheless act consistent with our longstated principles/goals as we negotiate this new path we need to be resolute, bold and brave; the path I hope to convince you of, and which is the subject of this talk, is EQIP (Educational Quality Improvement Project). We humans are in a time of cataclysmic change that rivals fire, the wheel, and the printing press. This change, the digitization of data, allows the collection of vast troves of data that allows healthcare system consolidation, more and more physician oversight, and the subsequent demand for business-like efficiency in the healthcare space. Dramatic changes like this creates collateral damage and can be frustrating for those that live through it. If the APDS is doing its job, we should be able to help Program Directors with these frustrations. The purposes of the APDS since its inception in 1977 is to: provide a forum for the exchange of information related to postgraduate surgical education; maintain high standards of surgical residency training; provide advice, assistance, and support to program directors; encourage research into all aspects of the education and training of surgeons; and represent the interests of program to other organizations. As we look to the future, I think 2 of these purposes should be paramount-research and advocacy. Research and advocacy are the cornerstones of EQIP. Research and advocacy can be linked by one word-truth. We need to seek the truth. We seek truth by collecting and analyzing data. We cannot hide from it, no matter where it leads us. We cannot shy away from the complexity of the search for truth no matter how difficult the process may be. EQIP is a continuous learning quality improvement program run by program directors for program directors to allow for data-driven innovation in surgical education and to allow data-informed conversations about the future of general surgery. EQIP will have a data gathering interface, a data repository and will have data analytics capability and data reporting platform. We are about to begin a 2-year proof of concept journey. So, in this time of great change and momentous challenge why should the APDS answer the bell? We should answer the bell for 4 reasons. We should answer the bell because engagement is a tool of resiliency, engagement solves frustration, and engagement is an antidote to burn-out. We should answer the bell because it gives us a chance to dramatically improve surgical education. We should answer the bell because we are in a great position to do this heavy lift. We should answer the bell because engagement is fun.