Artificial Intelligence And Machine Learning Research Articles

Algorithms in the Margins: Organized Community Resistance to Port Automation in the Los Angeles Harbor Area

Public deliberations on artificial intelligence and machine learning (AI/ML) provoke strong interest in automation, or the perceived displacement of human labor due to general technological advances. Social science scholarship on well-compensated technology workers and a growing global underclass suggests automation remains largely a myth, with emerging technologies generating new forms of computational and emotional work. But little research has examined automation from the vantage point of community members at perceived greatest risk of displacement, such as those sustained by generations of logistics labor and physically located in racialized, underserved areas. In the United States and around the globe, logistics work is directly targeted by “automation engineers” to reduce labor costs on behalf of industry interests, posing a severe threat to port workers and their respective communities. This article examines organized community resistance to automation within the Los Angeles Harbor Area, drawing on four public hearings following the Port of LA’s planned integration of autonomous vehicles for the movement of cargo (i.e., automated straddle carriers). We find community mobilizations widely sought to enlarge public consideration of AI/ML to encompass matters of societal well-being and the collective good. These include shifting from the legality of AI integration to underscore public morality in light of anticipated harm, in addition to emphasizing the invisible social arrangements surrounding the Port and nearby community life. Our research reveals how lay people may reassert their own importance when confronted by technoscientific injustice, such as by reframing the future of work toward an insistence on “the future of our communities.”

Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery.

The discovery of new crystalline inorganic compounds-novel compositions of matter within known structure types, or even compounds with completely new crystal structures-constitutes an important goal of solid-state and materials chemistry. Some fractions of new compounds can eventually lead to new structural and functional materials that enhance the efficiency of existing technologies or even enable completely new technologies. Materials researchers eagerly welcome new approaches to the discovery of new compounds, especially those that offer the promise of accelerated success. The recent report from a group of scientists at Google who employ a combination of existing data sets, high-throughput density functional theory calculations of structural stability, and the tools of artificial intelligence and machine learning (AI/ML) to propose new compounds is an exciting advance. We examine the claims of this work here, unfortunately finding scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility. While the methods adopted in this work appear to hold promise, there is clearly a great need to incorporate domain expertise in materials synthesis and crystallography.

ARTISANS—Artificial Intelligence for Simulation of Advanced Nuclear Systems for Nuclear Fission Technology

The objective of this Technical Opinion Paper (TOP) is to provide an overview of the research topics in the ARTISANS (Artificial Intelligence for Simulation of Advanced Nuclear Systems) research group at the North Carolina State University. We will discuss the connections between our research with the key items outlined in the Virtual Special Issues (VSI) Nuclear Fission Technology (NFT) series. NFT is crucial for the global goal of decarbonization, but as outlined by the VSI framework, significant challenges remain that inhibit NFT profitability. Our research addresses some of these challenges, centered around improving the simulation of complex nuclear systems with Artificial Intelligence and Machine Learning (AI/ML). The ARTISANS group specializes in many aspects of applications of AI/ML to nuclear engineering, whose potential impact on NFT continues to grow as AI/ML technologies improve.

Updates to the Alliance of Genome Resources central infrastructure.

The Alliance of Genome Resources (Alliance) is an extensible coalition of knowledgebases focused on the genetics and genomics of intensively studied model organisms. The Alliance is organized as individual knowledge centers with strong connections to their research communities and a centralized software infrastructure, discussed here. Model organisms currently represented in the Alliance are budding yeast, Caenorhabditis elegans, Drosophila, zebrafish, frog, laboratory mouse, laboratory rat, and the Gene Ontology Consortium. The project is in a rapid development phase to harmonize knowledge, store it, analyze it, and present it to the community through a web portal, direct downloads, and application programming interfaces (APIs). Here, we focus on developments over the last 2 years. Specifically, we added and enhanced tools for browsing the genome (JBrowse), downloading sequences, mining complex data (AllianceMine), visualizing pathways, full-text searching of the literature (Textpresso), and sequence similarity searching (SequenceServer). We enhanced existing interactive data tables and added an interactive table of paralogs to complement our representation of orthology. To support individual model organism communities, we implemented species-specific "landing pages" and will add disease-specific portals soon; in addition, we support a common community forum implemented in Discourse software. We describe our progress toward a central persistent database to support curation, the data modeling that underpins harmonization, and progress toward a state-of-the-art literature curation system with integrated artificial intelligence and machine learning (AI/ML).

BioVDB: biological vector database for high-throughput gene expression meta-analysis.

High-throughput sequencing has created an exponential increase in the amount of gene expression data, much of which is freely, publicly available in repositories such as NCBI's Gene Expression Omnibus (GEO). Querying this data for patterns such as similarity and distance, however, becomes increasingly challenging as the total amount of data increases. Furthermore, vectorization of the data is commonly required in Artificial Intelligence and Machine Learning (AI/ML) approaches. We present BioVDB, a vector database for storage and analysis of gene expression data, which enhances the potential for integrating biological studies with AI/ML tools. We used a previously developed approach called Automatic Label Extraction (ALE) to extract sample labels from metadata, including age, sex, and tissue/cell-line. BioVDB stores 438,562 samples from eight microarray GEO platforms. We show that it allows for efficient querying of data using similarity search, which can also be useful for identifying and inferring missing labels of samples, and for rapid similarity analysis.

Abstract A031: Prospective and clinical prediction in a retrospective trial that experimentally validated an AI/ML-derived whole-genome predictor as the most accurate and precise predictor of survival and response to treatment in glioblastoma

Abstract Cancer is complex, with contributing factors distributed across the entire genome affecting every aspect of the disease. But typical artificial intelligence and machine learning (AI/ML) would require 3B-patient training sets to generate predictive models from the whole 3B-nucleotide genome. As a result, tests remain limited to one to a few hundred genes. Prediction continues to rely mostly on such factors as a tumor’s grade and the patient’s age. And the understanding and management of cancer continue to involve guesswork. A genome-wide pattern in tumors from glioblastoma (GBM) patients was recently experimentally validated in a retrospective clinical trial as the most accurate and precise predictor of life expectancy and response to standard of care [doi: 10.1063/1.5142559]. Applicable to the U.S. population at large, this predictor, the first to encompass the whole genome, was mathematically (re)discovered and computationally (re)validated in open-source datasets from as few as 50–100 patients by using our data-agnostic physics-inspired AI/ML [doi: 10.1063/1.5099268, 10.1073/pnas.0530258100]. All other attempts to connect a GBM patient’s outcome with the tumor’s DNA copy numbers failed. For 70 years, the best indicator has been age. At 75–95% accuracy, our predictor is more accurate than and independent of age and all other indicators, including the one-gene tests for MGMT, IDH1, and TERT. Platform- and reference genome-agnostic, the predictor’s >99% precision is greater than the community consensus of <70% reproducibility based upon one to a few hundred genes. It describes mechanisms of transformation and identifies drug targets and combinations of targets to sensitize tumors to treatment. Now, in follow-up results from the trial we, first, show correct prospective prediction of the outcome of the five of the 79 patients who were alive four years earlier, at the time of first results. Two patients, who were predicted to have shorter survival, lived less than five years from diagnosis, whereas of the three patients predicted to have longer survival, one lived more than five, and the remaining two are alive >11.5, years from diagnosis. Second, we demonstrate 100%-precise clinical prediction for the 59 of the 79 patients with remaining tumor DNA, by using whole-genome sequencing in a Clinical Laboratory Improvement Amendments (CLIA) and College of American Pathologists (CAP) -regulated laboratory. Third, we establish that the risk that a tumor’s whole genome confers upon outcome, as is reflected by the predictor, among the 79 and, separately, 59 patients, is greater than that conferred by the patient’s Karnofsky performance score and access to chemotherapy and is surpassed only by the patient’s access to radiotherapy. This is a proof of principle that our AI/ML is uniquely suited for personalized medicine, that a patient’s survival and response to treatment are the outcome of their tumor’s whole genome, and that our AI/ML-derived whole-genome predictors can take the guesswork out of standard of care, clinical trials, and drug development. Citation Format: Sri Priya Ponnapalli, Penelope Miron, Kristy L. S. Miskimen, Kristin A. Waite, Nadiya Sosonkina, Sara E. Coppens, Anthony C. Bryan, Estevan P. Kiernan, Huanming Yang, Jay Bowen, Ghunwa A. Nakouzi, Jill S. Barnholtz-Sloan, Andrew E. Sloan, Tiffany R. Hodges, Orly Alter. Prospective and clinical prediction in a retrospective trial that experimentally validated an AI/ML-derived whole-genome predictor as the most accurate and precise predictor of survival and response to treatment in glioblastoma [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr A031.

Transforming Data into Compliance: Harnessing AI/ML to Enhance Regulatory Reporting Processes

This paper delves into the incorporation of artificial intelligence and machine learning (AI/ML) technologies to optimize regulatory reporting processes. It explores how AI/ML algorithms streamline data analysis, interpretation, and compliance within regulatory frameworks. Through the utilization of advanced algorithms, organizations can bolster the efficiency and accuracy of regulatory reporting, resulting in enhanced compliance outcomes. The paper outlines key applications of AI/ML in regulatory reporting and addresses challenges and considerations linked to their implementation. Additionally, it underscores the potential benefits of adopting AI/ML-driven approaches for regulatory reporting processes across diverse industries.

FDA-Approved Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices: An Updated Landscape

As artificial intelligence (AI) has been highly advancing in the last decade, machine learning (ML)-enabled medical devices are increasingly used in healthcare. In this study, we collected publicly available information on AI/ML-enabled medical devices approved by the FDA in the United States, as of the latest update on 19 October 2023. We performed comprehensive analysis of a total of 691 FDA-approved artificial intelligence and machine learning (AI/ML)-enabled medical devices and offer an in-depth analysis of clearance pathways, approval timeline, regulation type, medical specialty, decision type, recall history, etc. We found a significant surge in approvals since 2018, with clear dominance of the radiology specialty in the application of machine learning tools, attributed to the abundant data from routine clinical data. The study also reveals a reliance on the 510(k)-clearance pathway, emphasizing its basis on substantial equivalence and often bypassing the need for new clinical trials. Also, it notes an underrepresentation of pediatric-focused devices and trials, suggesting an opportunity for expansion in this demographic. Moreover, the geographical limitation of clinical trials, primarily within the United States, points to a need for more globally inclusive trials to encompass diverse patient demographics. This analysis not only maps the current landscape of AI/ML-enabled medical devices but also pinpoints trends, potential gaps, and areas for future exploration, clinical trial practices, and regulatory approaches. In conclusion, our analysis sheds light on the current state of FDA-approved AI/ML-enabled medical devices and prevailing trends, contributing to a wider comprehension.

Persons or data points? Ethics, artificial intelligence, and the participatory turn in mental health research.

This article identifies and examines a tension in mental health researchers' growing enthusiasm for the use of computational tools powered by advances in artificial intelligence and machine learning (AI/ML). Although there is increasing recognition of the value of participatory methods in science generally and in mental health research specifically, many AI/ML approaches, fueled by an ever-growing number of sensors collecting multimodal data, risk further distancing participants from research processes and rendering them as mere vectors or collections of data points. The imperatives of the "participatory turn" in mental health research may be at odds with the (often unquestioned) assumptions and data collection methods of AI/ML approaches. This article aims to show why this is a problem and how it might be addressed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

HARMONIZATION OF CHRONIC CONDITIONS WAREHOUSE DATA FOR OUTCOMES RESEARCH ON GERIATRIC SYNDROMES

Abstract Geriatric syndromes such as cognitive impairment, falls, depression, urinary incontinence, pressure ulcers, and polypharmacy are common conditions and syndromes that affect older adults. Studying geriatric syndromes in Medicare beneficiaries may provide important insights for improving health outcomes and quality of life. As a result of the Improving Medicare Post-Acute Care Transformation Act of 2014, it is now easier to compare standardized geriatric assessment data (including information about geriatric syndromes) collected in post-acute and long-term care settings with corresponding data from claims, encounter, Part D, and durable medical equipment files. However, to our knowledge, methods to link and harmonize post-acute and long-term care assessment data (available for 75% of decedents) are yet to be utilized in the health services research community. The goal of this project is to develop harmonized datasets that can be used to identify geriatric syndromes and study their prevalence, cost, health outcomes, and service delivery factors over time. This work will allow our team and other researchers to fully leverage these data for use in predictive algorithms and artificial intelligence and machine learning (AI/ML) research. The project uses the five most recent years of data available from the Centers for Medicare and Medicaid Services (CMS) Chronic Conditions Data Warehouse. Data sources include Medicare and Medicaid claims, encounter, MedPAR, Hospice claims, Minimum Data Set (MDS), MDS-Swing Bed (SB), Outcome and Assessment Information Set (OASIS), Inpatient Rehabilitation Facility Patient Assessment Instrument (IRF-PAI), Pharmacy (Part D), and Durable Medical Equipment (DME).

A machine learning approach toward automating spatial identification of LAG3+/CD3+ cells in ulcerative colitis

Over the past decade, automation of digital image analysis has become commonplace in both research and clinical settings. Spurred by recent advances in artificial intelligence and machine learning (AI/ML), tissue sub-compartments and cellular phenotypes within those compartments can be identified with higher throughput and accuracy than ever before. Recently, immune checkpoints have emerged as potential targets for auto-immune diseases. As such, spatial identification of these proteins along with immune cell markers (e.g., CD3+/LAG3+ T-cells) is a crucial step in understanding the potential and/or efficacy of such treatments. Here, we describe a semi-automated imaging and analysis pipeline that identifies CD3+/LAG3+ cells in colorectal tissue sub-compartments. While chromogenic staining has been a clinical mainstay and the resulting brightfield images have been utilized in AI/ML approaches in the past, there are associated drawbacks in phenotyping algorithms that can be overcome by fluorescence imaging. To address these tradeoffs, we developed an analysis pipeline combining the strengths of brightfield and fluorescence images. In this assay, immunofluorescence imaging was conducted to identify phenotypes followed by coverslip removal and hematoxylin and eosin staining of the same section to inform an AI/ML tissue segmentation algorithm. This assay proved to be robust in both tissue segmentation and phenotyping, was compatible with automated workflows, and revealed presence of LAG3+ T-cells in ulcerative colitis biopsies with spatial context preserved.

OP115 Scoping Meta-Review On Methods Used To Assess Artificial Intelligence-Based Medical Devices For Heart Failure

IntroductionThe International Federation of Medical and Biological Engineering created a multidisciplinary working group to discuss assessments of artificial intelligence and machine learning (AI/ML) applications in health care. Engineers, clinicians, and economists identified evidence generation as a critical topic. Heart failure (HF) was selected to investigate the available evidence on the clinical effectiveness and safety of AI/ML applications. Attention was paid to transparency of AI/ML methods and their data sources.MethodsA scoping review was conducted on AI/ML algorithms developed for the management of HF. A search for systematic reviews, scoping reviews, and meta-analyses published from 1976 to October 2022 was conducted in Embase, MEDLINE, and Scopus.ResultsOf 456 relevant publications, 21 papers were included in the final analysis. Most papers (10 systematic reviews, five meta-analyses, and six non-systematic or scoping reviews) included studies conducted in North America. No study was conducted in Africa. The healthcare setting was not clearly stated in approximately half of the studies. A lack of agreement was noticed regarding the quality assessment tools used among the reviews. The most common data source for AI/ML algorithms was electronic health records, but in some cases data sources were not reported. While deep learning emerged as the most common adopted methodology, covariates were not always included in the algorithm development. The review demonstrated that comparative assessment of algorithms requires further investigation, given the high variability in the comparator used (e.g., clinical gold standard, other AI/ML algorithms, or other statistical methods). The main investigated endpoints were the incidence of HF and the number of hospital admissions.ConclusionsWhen assessing innovative health technologies such as AI/ML applications in health care, evidence is among the main challenges. Our scoping review, focusing on algorithms developed to manage HF, showed that the biggest challenges relate to the quality of the studies, the adoption of a comparative approach, and transparency of methods.

Artificial Intelligence and Machine Learning (AIML) approaches to empower genomics, drug, and biomarker discovery in ADRD

AbstractBackgroundIn 2021, the NIA launched the Artificial Intelligence and Machine Learning (AIML) Consortium, to develop novel AI and machine learning techniques to tackle pressing questions in Alzheimer’s disease research.MethodDrawing on vast repositories of data from the Alzheimer’s Disease Sequencing Project (ADSP) and other large public biobanks, the 8 awardees funded under this program meet monthly to present their innovations in data analytics in 4 main areas: (1) genome‐guided drug discovery and drug repositioning for AD; (2) discovery of novel genetic and molecular targets for AD therapeutics using whole genome and omics data; and (3) understanding the regulatory code of the genome using deep learning and bioinformatics, and (4) use of electronic health records (EHR) to study treatment outcomes in ADRD. A second branch of AIML activity draws on repositories of neuroimaging data (MRI/FLAIR, DTI, and amyloid‐ and tau‐sensitive PET) to (1) identify ADRD subtypes based on neuroimaging, clinical and neuropsychological data, that differ in terms of their genomic drivers and expected treatment response (for precision medicine and drug trial stratification), (2) perform genetic analysis of endophenotypes derived from brain images (such as longitudinal hippocampal volume changes, atrophy subtype scores, and PET or WMH signatures) for biomarker‐guided gene discovery, and (3) predict neuropathology (TDP‐43, CAA, LBD) from multimodal imaging using AI. The AIML initiative benefits from and helps the ADSP’s Phenotype Harmonization Consortium, as AI methods can also be used to adjust neuroimaging data for site and protocol effects (harmonization).ResultIn this overview of the AIML Consortium, we highlight some recent successes using (1) convolutional neural networks (CNNs), GANs and deep learning for ADRD subtyping and staging; (2) discovery of AD‐relevant motifs and networks in whole genome sequences and functional genomics data; (3) prognostic models of clinical outcomes based on WGS and biomarkers; and (4) novel methods from statistical physics, based on evolutionary action, which model the fitness effect of coding mutations analytically.ConclusionAIML investigators are hosting regular workshops at AAIC and upcoming genomics and clinical neurology conferences to disseminate and teach these approaches to the AD and broader scientific community.

Interview

Nao Tokui is an artist, researcher, and DJ who has been finding interesting new ways of integrating artificial intelligence and machine learning (AI/ML) techniques into his eclectic body of work since 2015. Stephen Roddy talked with Nao about his approach to AI in his own work and where he sees the relationship between AI and musical creativity going in the future.

HR Practices Through the Lens of Technology and Digital Transformation

Abstract The company’s digital transformation requires the optimization and automation of processes through Industry 4.0 technology, leading to a new business model. Along with technology, effective human resource management (HRM) is crucial for this model. HRM and Industry 4.0 technology are interdependent. Technologies require new HR operating models, and HR, through organizational management strategies such as change management, agile organizations, and talent management, influences the provision of a workforce capable of implementing these disruptive technologies. This paper focuses on HR's role in digital transformation and the resulting changes in HRM and HR practices. It presents use cases of Industry 4.0 technology and their benefits. The study presented in this paper examines the impact of HR practices on operational, financial, and employee performance in relation to the use of disruptive technologies. A survey questionnaire was used to examine this impact on 11 large and medium-sized companies in Serbia, using statistical methods of descriptive statistics and correlation analysis through SPSS 23 software to analyze the results. The research showed that the use of artificial intelligence and machine learning (AI/ML) in HR processes has a positive effect on employee performance, as well as operational and financial performance. Other hypotheses were only partially proven. The research also revealed the lack of training for HR employees regarding the use of digital technologies in HR processes and digital skills compared to other sectors in the company. The recommendation is that HR managers begin training on the digitization of HRM as soon as possible.

Trust criteria for artificial intelligence in health: normative and epistemic considerations

Rapid advancements in artificial intelligence and machine learning (AI/ML) in healthcare raise pressing questions about how much users should trust AI/ML systems, particularly for high stakes clinical decision-making. Ensuring that...

Trademarks in the Age of Automated Commerce: Consumer Choice and Autonomy

In the age of automated commerce, powered by artificial intelligence and machine learning (AI/ML) tools, the role of trademarks in shopping may be diminishing. For instance, the Alexa voice assistant will announce only a couple of purchase options under specific trademarks, concealing the plenitude of other products on its interface. Another example is that Amazon will pursue the shipping-then-shopping scenario, with trademarks being perceived by consumers only upon delivery. Moreover, it has been predicted that, in some cases, the new AI/ML tools will search for products irrespective of trademarks and will do so faster than any human being. Under those circumstances, consumer search costs will be lower, not because of trademarks but because of the new shopping architecture based on those tools. While lowering consumer search costs has traditionally been the role of trademarks, the availability of other tools for the same purpose may be a positive development. However, another trend is that the new AI/ML tools are constantly taking part in consumer decision-making, possibly reducing consumers’ freedom of choice and personal autonomy. In attempting to tackle this issue, it is worth approaching trademarks from the perspective of choice and autonomy. Seen from this angle, it may be possible to learn specific lessons from European Union (EU) trademark law that will assist in reshaping the automated – and autonomous – shopping architecture for the benefit of consumer well-being. In this exercise, close attention will be paid to the case-law of the Court of Justice of the European Union, with its discourse on product alternatives, while due regard will be shown to the new legislation on the platform economy in the EU. In the end, this paper will demonstrate that trademarks are valuable not only because they help reduce search costs but also because they promote alternatives and, thus, improve consumers’ general autonomy.

From Data to Compliance: The Role of AI/ML in Optimizing Regulatory Reporting Processes

This paper explores the integration of artificial intelligence and machine learning (AI/ML) technologies in optimizing regulatory reporting processes. It examines how AI/ML algorithms can streamline data analysis, interpretation, and compliance within regulatory frameworks. By leveraging advanced algorithms, organizations can enhance the efficiency and accuracy of regulatory reporting, leading to improved compliance outcomes. The paper highlights key applications of AI/ML in regulatory reporting and discusses challenges and considerations associated with their implementation. Furthermore, it emphasizes the potential benefits of adopting AI/ML-driven approaches for regulatory reporting processes across various industries.

Scoping Meta-Review of Methods Used to Assess Artificial Intelligence-Based Medical Devices for Heart Failure.

Artificial intelligence and machine learning (AI/ML) are playing increasingly important roles, permeating the field of medical devices (MDs). This rapid progress has not yet been matched by the Health Technology Assessment (HTA) process, which still needs to define a common methodology for assessing AI/ML-based MDs. To collect existing evidence from the literature about the methods used to assess AI-based MDs, with a specific focus on those used for the management of heart failure (HF), the International Federation of Medical and Biological Engineering (IFMBE) conducted a scoping meta-review. This manuscript presents the results of this search, which covered the period from January 1974 to October 2022. After careful independent screening, 21 reviews, mainly conducted in North America and Europe, were retained and included. Among the findings were that deep learning is the most commonly utilised method and that electronic health records and registries are among the most prevalent sources of data for AI/ML algorithms. Out of the 21 included reviews, 19 focused on risk prediction and/or the early diagnosis of HF. Furthermore, 10 reviews provided evidence of the impact on the incidence/progression of HF, and 13 on the length of stay. From an HTA perspective, the main areas requiring improvement are the quality assessment of studies on AI/ML (included in 11 out of 21 reviews) and their data sources, as well as the definition of the criteria used to assess the selection of the most appropriate AI/ML algorithm.

Digital twin-enabled domain adaptation for zero-touch UAV networks: Survey and challenges

In existing wireless networks, the control programs have been designed manually and for certain predefined scenarios. This process is complicated and error-prone, and the resulting control programs are not resilient to disruptive changes. Data-driven control based on Artificial Intelligence and Machine Learning (AI/ML) has been envisioned as a key technique to automate the modeling, optimization and control of complex wireless systems. However, existing AI/ML techniques rely on sufficient well-labeled data and may suffer from slow convergence and poor generalizability. In this article, focusing on digital twin-assisted wireless unmanned aerial vehicle (UAV) systems, we provide a survey of emerging techniques that can enable fast-converging data-driven control of wireless systems with enhanced generalization capability to new environments. These include simultaneous localization and sensing (SLAM)-based sensing and network softwarization for digital twin construction, robust reinforcement learning and system identification for domain adaptation, and testing facility sharing and federation. The corresponding research opportunities are also discussed.