Commercial Artificial Intelligence Research Articles

Proof of Concept for a Deep Learning Algorithm for Identification and Quantification of Key Microscopic Features in the Murine Model of DSS-Induced Colitis.

Inflammatory bowel disease (IBD) is a complex disease which leads to life-threatening complications and decreased quality of life. The dextran sulfate sodium (DSS) colitis model in mice is known for rapid screening of candidate compounds. Efficacy assessment in this model relies partly on microscopic semiquantitative scoring, which is time-consuming and subjective. We hypothesized that deep learning artificial intelligence (AI) could be used to identify acute inflammation in H&E-stained sections in a consistent and quantitative manner. Training sets were established using ×20 whole slide images of the entire colon. Supervised training of a Convolutional Neural Network (CNN) was performed using a commercial AI platform to detect the entire colon tissue, the muscle and mucosa layers, and 2 categories within the mucosa (normal and acute inflammation E1). The training sets included slides of naive, vehicle-DSS and cyclosporine A-DSS mice. The trained CNN was able to segment, with a high level of concordance, the different tissue compartments in the 3 groups of mice. The segmented areas were used to determine the ratio of E1-affected mucosa to total mucosa. This proof-of-concept work shows promise to increase efficiency and decrease variability of microscopic scoring of DSS colitis when screening candidate compounds for IBD.

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study

To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXRs) for predicting patient outcomes and the need for mechanical ventilation in COVID-19 pneumonia. Our IRB-approved study included 1367 serial CXRs from 405 adult patients (mean age 65 ± 16 years) from two sites in the US (Site A) and South Korea (Site B). We recorded information pertaining to patient demographics (age, gender), smoking history, comorbid conditions (such as cancer, cardiovascular and other diseases), vital signs (temperature, oxygen saturation), and available laboratory data (such as WBC count and CRP). Two thoracic radiologists performed the qualitative assessment of all CXRs based on the RALE score for assessing the severity of lung involvement. All CXRs were processed with a commercial AI algorithm to obtain the percentage of the lung affected with findings related to COVID-19 (AI score). Independent t- and chi-square tests were used in addition to multiple logistic regression with Area Under the Curve (AUC) as output for predicting disease outcome and the need for mechanical ventilation. The RALE and AI scores had a strong positive correlation in CXRs from each site (r2 = 0.79–0.86; p < 0.0001). Patients who died or received mechanical ventilation had significantly higher RALE and AI scores than those with recovery or without the need for mechanical ventilation (p < 0.001). Patients with a more substantial difference in baseline and maximum RALE scores and AI scores had a higher prevalence of death and mechanical ventilation (p < 0.001). The addition of patients’ age, gender, WBC count, and peripheral oxygen saturation increased the outcome prediction from 0.87 to 0.94 (95% CI 0.90–0.97) for RALE scores and from 0.82 to 0.91 (95% CI 0.87–0.95) for the AI scores. AI algorithm is as robust a predictor of adverse patient outcome (death or need for mechanical ventilation) as subjective RALE scores in patients with COVID-19 pneumonia.

Beyond the AJR: "External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms".

Beyond the AJR: “External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms”Aimilia Gastounioti, PhD1 and Emily F. Conant, MD1Audio Available | Share

Comparative Performance of Artificial Intelligence Algorithms for Screening Mammography.

HomeRadiology: Imaging CancerVol. 2, No. 6 PreviousNext Research HighlightsFree AccessComparative Performance of Artificial Intelligence Algorithms for Screening MammographyMichio TayaMichio TayaMichio TayaPublished Online:Nov 27 2020https://doi.org/10.1148/rycan.2020209034MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In Take-Away Points■ Major Focus: To evaluate the performance of three commercially available artificial intelligence algorithms in screening mammography assessment in an external population both independently and in combination with radiologists.■ Key Result: One of the three artificial intelligence algorithms had significantly higher accuracy for breast cancer detection (area under receiver operating characteristic curve of 0.96 vs 0.92 vs 0.92; P < .001). Combining this best algorithm with first-reader radiologists achieved the highest accuracy at 88.6% sensitivity and 93.0% specificity.■ Impact: A commercially available artificial intelligence algorithm can assess screening mammograms with comparable diagnostic accuracy as radiologists. Whether such an algorithm can act as independent readers prospectively in a screening population remains unanswered.Screening mammography is currently the clinical standard for early detection of breast cancer. However, performance of individual radiologists in screening mammography programs varies widely. Efforts to develop artificial intelligence (AI) algorithms for screening mammography have yielded mixed outcomes owing to variability in quality of training data and study methodology. External validation studies comparing the performance of different AI algorithms in a screening population are lacking.This external validation study compared the diagnostic performance of three commercially available AI algorithms both independently and in combination with radiologists in a retrospective population-based screening cohort. Women with implants or prior breast cancer were excluded. The study population included 739 women with pathology-verified breast cancer diagnosed at screening or within 12 months and 8066 randomly sampled healthy controls with negative findings by 2-year cancer-free follow-up. The AI algorithm determined suspicion of cancer on a continuous scale from 0 to 1.0 with a decision threshold for normal and abnormal set at the mean specificity of the first-reader radiologist (96.6%).Algorithm 1 performed significantly better than the other two algorithms with area under receiver operating characteristic curve of 0.96 versus 0.92 versus 0.92 (P < .001). No significant difference in sensitivity existed compared with radiologist consensus at the same specificity (81.9% vs 85.0%, P = .11), despite the algorithm having no inputs from prior imaging or clinical history. When choosing an operating point corresponding to the U.S. Breast Cancer Surveillance Consortium benchmark of 88.9% specificity, algorithm 1’s sensitivity of 88.6% was comparable to the benchmark sensitivity of 86.9%.In combination with a radiologist where abnormal was defined by either the algorithm or radiologist, the cancer detection rate increased 8% from 5.32 per 1000 for algorithm 1 alone to 5.76 per 1000 in combination with the first reader with a corresponding 77% increase in recall rate from 39.1 to 69.1 per 1000.The most accurate algorithm was trained on the largest training set and utilized pixel-level annotations and higher capacity network architecture, despite training on a patient population and vendor system different from the external validation data set. In conclusion, only one of the three algorithms performed at an independent level comparable to radiologists, setting the groundwork for prospective trial evaluation of AI systems in screening mammography.Highlighted ArticleSalim M, Wåhlin E, Dembrower K, et al. External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms. JAMA Oncol 2020;6(10):1581–1588. doi: 10.1001/jamaoncol.2020.3321Highlighted ArticleSalim M, Wåhlin E, Dembrower K, et al. External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms. JAMA Oncol 2020;6(10):1581–1588. doi: 10.1001/jamaoncol.2020.3321 Crossref, Medline, Google ScholarArticle HistoryPublished online: Nov 27 2020 FiguresReferencesRelatedDetailsRecommended Articles MRI for Evaluation of Complications of Breast AugmentationRadioGraphics2022Volume: 42Issue: 4pp. 929-946Does Reader Performance with Digital Breast Tomosynthesis Vary according to Experience with Two-dimensional Mammography?Radiology2017Volume: 283Issue: 2pp. 371-380Invited Commentary: High-Quality MRI after Breast AugmentationRadioGraphics2022Volume: 42Issue: 4pp. E113-E114Artificial Intelligence for Reducing Workload in Breast Cancer Screening with Digital Breast TomosynthesisRadiology2022Volume: 303Issue: 1pp. 69-77Breast Imaging in Transgender Patients: What the Radiologist Should KnowRadioGraphics2019Volume: 40Issue: 1pp. 13-27See More RSNA Education Exhibits What the Breast Radiologist Needs to Know About Imaging Transgender PatientsDigital Posters2018Evaluation of Breast Implants by Traditional Methods, Mammography, and Ultrasonography: When Suspicious of RuptureDigital Posters2019Imaging the Post Mastectomy and Post Implant Breast: Challenges and PitfallsDigital Posters2019 RSNA Case Collection Intracapsular Implant RuptureRSNA Case Collection2021Silicone lymph node on 18F-FDG PET/CTRSNA Case Collection2020Breast implant-associated anaplastic large cell lymphoma RSNA Case Collection2021 Vol. 2, No. 6 Metrics Altmetric Score PDF download

Impact of a Commercial Artificial Intelligence-Driven Patient Self-Assessment Solution on Waiting Times at General Internal Medicine Outpatient Departments: Retrospective Study.

BackgroundPatient waiting time at outpatient departments is directly related to patient satisfaction and quality of care, particularly in patients visiting the general internal medicine outpatient departments for the first time. Moreover, reducing wait time from arrival in the clinic to the initiation of an examination is key to reducing patients’ anxiety. The use of automated medical history–taking systems in general internal medicine outpatient departments is a promising strategy to reduce waiting times. Recently, Ubie Inc in Japan developed AI Monshin, an artificial intelligence–based, automated medical history–taking system for general internal medicine outpatient departments.ObjectiveWe hypothesized that replacing the use of handwritten self-administered questionnaires with the use of AI Monshin would reduce waiting times in general internal medicine outpatient departments. Therefore, we conducted this study to examine whether the use of AI Monshin reduced patient waiting times.MethodsWe retrospectively analyzed the waiting times of patients visiting the general internal medicine outpatient department at a Japanese community hospital without an appointment from April 2017 to April 2020. AI Monshin was implemented in April 2019. We compared the median waiting time before and after implementation by conducting an interrupted time-series analysis of the median waiting time per month. We also conducted supplementary analyses to explain the main results.ResultsWe analyzed 21,615 visits. The median waiting time after AI Monshin implementation (74.4 minutes, IQR 57.1) was not significantly different from that before AI Monshin implementation (74.3 minutes, IQR 63.7) (P=.12). In the interrupted time-series analysis, the underlying linear time trend (–0.4 minutes per month; P=.06; 95% CI –0.9 to 0.02), level change (40.6 minutes; P=.09; 95% CI –5.8 to 87.0), and slope change (–1.1 minutes per month; P=.16; 95% CI –2.7 to 0.4) were not statistically significant. In a supplemental analysis of data from 9054 of 21,615 visits (41.9%), the median examination time after AI Monshin implementation (6.0 minutes, IQR 5.2) was slightly but significantly longer than that before AI Monshin implementation (5.7 minutes, IQR 5.0) (P=.003).ConclusionsThe implementation of an artificial intelligence–based, automated medical history–taking system did not reduce waiting time for patients visiting the general internal medicine outpatient department without an appointment, and there was a slight increase in the examination time after implementation; however, the system may have enhanced the quality of care by supporting the optimization of staff assignments.

External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms

A computer algorithm that performs at or above the level of radiologists in mammography screening assessment could improve the effectiveness of breast cancer screening. To perform an external evaluation of 3 commercially available artificial intelligence (AI) computer-aided detection algorithms as independent mammography readers and to assess the screening performance when combined with radiologists. This retrospective case-control study was based on a double-reader population-based mammography screening cohort of women screened at an academic hospital in Stockholm, Sweden, from 2008 to 2015. The study included 8805 women aged 40 to 74 years who underwent mammography screening and who did not have implants or prior breast cancer. The study sample included 739 women who were diagnosed as having breast cancer (positive) and a random sample of 8066 healthy controls (negative for breast cancer). Positive follow-up findings were determined by pathology-verified diagnosis at screening or within 12 months thereafter. Negative follow-up findings were determined by a 2-year cancer-free follow-up. Three AI computer-aided detection algorithms (AI-1, AI-2, and AI-3), sourced from different vendors, yielded a continuous score for the suspicion of cancer in each mammography examination. For a decision of normal or abnormal, the cut point was defined by the mean specificity of the first-reader radiologists (96.6%). The median age of study participants was 60 years (interquartile range, 50-66 years) for 739 women who received a diagnosis of breast cancer and 54 years (interquartile range, 47-63 years) for 8066 healthy controls. The cases positive for cancer comprised 618 (84%) screen detected and 121 (16%) clinically detected within 12 months of the screening examination. The area under the receiver operating curve for cancer detection was 0.956 (95% CI, 0.948-0.965) for AI-1, 0.922 (95% CI, 0.910-0.934) for AI-2, and 0.920 (95% CI, 0.909-0.931) for AI-3. At the specificity of the radiologists, the sensitivities were 81.9% for AI-1, 67.0% for AI-2, 67.4% for AI-3, 77.4% for first-reader radiologist, and 80.1% for second-reader radiologist. Combining AI-1 with first-reader radiologists achieved 88.6% sensitivity at 93.0% specificity (abnormal defined by either of the 2 making an abnormal assessment). No other examined combination of AI algorithms and radiologists surpassed this sensitivity level. To our knowledge, this study is the first independent evaluation of several AI computer-aided detection algorithms for screening mammography. The results of this study indicated that a commercially available AI computer-aided detection algorithm can assess screening mammograms with a sufficient diagnostic performance to be further evaluated as an independent reader in prospective clinical trials. Combining the first readers with the best algorithm identified more cases positive for cancer than combining the first readers with second readers.

Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study

We examined the potential change in cancer detection when using an artificial intelligence (AI) cancer-detection software to triage certain screening examinations into a no radiologist work stream, and then after regular radiologist assessment of the remainder, triage certain screening examinations into an enhanced assessment work stream. The purpose of enhanced assessment was to simulate selection of women for more sensitive screening promoting early detection of cancers that would otherwise be diagnosed as interval cancers or as next-round screen-detected cancers. The aim of the study was to examine how AI could reduce radiologist workload and increase cancer detection. In this retrospective simulation study, all women diagnosed with breast cancer who attended two consecutive screening rounds were included. Healthy women were randomly sampled from the same cohort; their observations were given elevated weight to mimic a frequency of 0·7% incident cancer per screening interval. Based on the prediction score from a commercially available AI cancer detector, various cutoff points for the decision to channel women to the two new work streams were examined in terms of missed and additionally detected cancer. 7364 women were included in the study sample: 547 were diagnosed with breast cancer and 6817 were healthy controls. When including 60%, 70%, or 80% of women with the lowest AI scores in the no radiologist stream, the proportion of screen-detected cancers that would have been missed were 0, 0·3% (95% CI 0·0-4·3), or 2·6% (1·1-5·4), respectively. When including 1% or 5% of women with the highest AI scores in the enhanced assessment stream, the potential additional cancer detection was 24 (12%) or 53 (27%) of 200 subsequent interval cancers, respectively, and 48 (14%) or 121 (35%) of 347 next-round screen-detected cancers, respectively. Using a commercial AI cancer detector to triage mammograms into no radiologist assessment and enhanced assessment could potentially reduce radiologist workload by more than half, and pre-emptively detect a substantial proportion of cancers otherwise diagnosed later. Stockholm City Council.

Use of Artificial Intelligence for Medical Literature Search: Randomized Controlled Trial Using the Hackathon Format.

BackgroundMapping out the research landscape around a project is often time consuming and difficult.ObjectiveThis study evaluates a commercial artificial intelligence (AI) search engine (IRIS.AI) for its applicability in an automated literature search on a specific medical topic.MethodsTo evaluate the AI search engine in a standardized manner, the concept of a science hackathon was applied. Three groups of researchers were tasked with performing a literature search on a clearly defined scientific project. All participants had a high level of expertise for this specific field of research. Two groups were given access to the AI search engine IRIS.AI. All groups were given the same amount of time for their search and were instructed to document their results. Search results were summarized and ranked according to a predetermined scoring system.ResultsThe final scoring awarded 49 and 39 points out of 60 to AI groups 1 and 2, respectively, and the control group received 46 points. A total of 20 scientific studies with high relevance were identified, and 5 highly relevant studies (“spot on”) were reported by each group.ConclusionsAI technology is a promising approach to facilitate literature searches and the management of medical libraries. In this study, however, the application of AI technology lead to a more focused literature search without a significant improvement in the number of results.

Futuros traçados e experiências poéticas: cartografia, performance e vigilância

A partir da noção do capitalismo de vigilância, caracterizado pela coleta massiva e análise de dados, apresentaremos como esse tipo de sistema ultrapassa as discussões da tecnologia, tendo forte configuração política e resultados estéticos que instauram novas formas de visibilidade. Com os, cada vez mais comuns, algoritmos de aprendizado de máquina, e a grande quantidade de informações que esses sistemas podem extrair das mais diversas ações cotidianas, é realizada uma construção de perfil detalhada de cada indivíduo, com objetivos de análises, predições de decisão e exposição mediada ao conteúdo mais adequado. Passa-se à construção de uma subjetividade calcada no futuro, mas atualizada no presente com experiências já previstas e calculadas por um sistema de informação. Numa dualidade de informação e sensação, a experiência dá lugar a predição. Partindo de uma contextualização bibliográfica de teóricos que estudam esses assuntos será estabelecido o cenário de discussão. Em seguida, utilizando-se da pesquisa poética em desenvolvimento pelo autor, será apresentado o projeto “Em busca de um lugar”: uma performance audiovisual que utiliza sistemas comerciais de inteligência artificial como dispositivos disparadores de ações poéticas. Na performance, realiza-se aproximações algorítmicas de lugares geograficamente distantes, a partir de imagens de vigilância feitas pelo artista. Como resultado, são criadas novas cartografias de um espaço que é construído com os mecanismos de vigilância, mas que, de certo modo os subverte, tirando a função e a objetividade esperadas pelo sistema. Desse modo, conclui-se, apresentando a subversão das funcionalidades da lógica algorítmica para a criação poética.

Malicious Use of Artificial Intelligence

The article identifies the main risks and threats related to national and international psychological security (PS) in BRICS countries (particularly China, India, and Russia) and posed by the malicious use of artificial intelligence (AI). The main methods of research are systemic, scenario, and case analyses. The authors maintain that PS threats, both national and international, created by the malicious use of AI should be considered at three levels. At the first level, a false negative image of AI is spread. The second level of PS threats is directly related to the malicious use of AI (MUAI), but an attack on public consciousness is not its main goal. The MUAI designed primarily to cause psychological damage belongs to the third, and highest, level of PS threats. Synthetic AI products (combining a number of technologies, which can increase the damage from their hacking or malicious use) create a whole range of new risks and threats to BRICS countries. The reorientation of commercial AI systems, the malicious use of deepfakes and chatbots, the use of bots to set agendas, deranking, and AI phishing technologies also pose a threat. The main methods of destructive impact through MUAI are illustrated by the examples China, India, and Russia. BRICS policy documents state readiness for joint action against the MUAI. At this point bilateral agreements play the leading role in the development of AI cooperation of BRICS member states, but their declarations clearly state the intention to join forces against the misuse of information and communications technology (ICT). National regulation pertaining to AI and efforts to counter its malicious use are still at the fledgling stage in most countries. This makes it all the more important for each of the BRICS member states to develop international cooperation and share experience. BRICS’s potential in this sphere is still very far from being fully tapped.

Comparing the predictive ability of a commercial artificial intelligence early warning system with physician judgement for clinical deterioration in hospitalised general internal medicine patients: a prospective observational study

ObjectiveOur study compares physician judgement with an automated early warning system (EWS) for predicting clinical deterioration of hospitalised general internal medicine patients.DesignProspective observational study of clinical predictions made at the...

Designing educational technologies in the age of AI: A learning sciences‐driven approach

AbstractInterdisciplinary research from the learning sciences has helped us understand a great deal about the way that humans learn, and as a result we now have an improved understanding about how best to teach and train people. This same body of research must now be used to better inform the development of Artificial Intelligence (AI) technologies for use in education and training. In this paper, we use three case studies to illustrate how learning sciences research can inform the judicious analysis, of rich, varied and multimodal data, so that it can be used to help us scaffold students and support teachers. Based on this increased understanding of how best to inform the analysis of data through the application of learning sciences research, we are better placed to design AI algorithms that can analyse rich educational data at speed. Such AI algorithms and technology can then help us to leverage faster, more nuanced and individualised scaffolding for learners. However, most commercial AI developers know little about learning sciences research, indeed they often know little about learning or teaching. We therefore argue that in order to ensure that AI technologies for use in education and training embody such judicious analysis and learn in a learning sciences informed manner, we must develop inter‐stakeholder partnerships between AI developers, educators and researchers. Here, we exemplify our approach to such partnerships through the EDUCATE Educational Technology (EdTech) programme. Practitioner NotesWhat is already known about this topic? The progress of AI Technology and learning analytics lags behind the adoption of these approaches and technologies in other fields such as medicine or finance. Data are central to the empirical work conducted in the learning sciences and to the development of machine learning Artificial Intelligence (AI). Education is full of doubts about the value that any technology can bring to the teaching and learning process. What this paper adds? We argue that the learning sciences have an important role to play in the design of educational AI, through their provision of theories that can be operationalised and advanced. Through case studies, we illustrate that the analysis of data appropriately informed by interdisciplinary learning sciences research can be used to power AI educational technology. We provide a framework for inter‐stakeholder, interdisciplinary partnerships that can help educators better understand AI, and AI developers better understand education. Implications for practice and/or policy? AI is here to stay and that it will have an increasing impact on the design of technology for use in education and training. Data, which is the power behind machine learning AI, can enable analysis that can vastly increase our understanding of when and how the teaching and learning process is progressing positively. Inter‐stakeholder, interdisciplinary partnerships must be used to make sure that AI provides some of the educational benefits its application in other areas promise us.

The Business of AI Startups

New machine learning techniques have led to an acceleration of artificial intelligence (AI). Numerous papers have projected substantial job losses based on assessments of technical feasibility. But what is the actual impact? This paper reports on a survey of commercial AI startups, documenting rich detail about their businesses and their impacts on their customers. These firms report benefits of AI that are more often about enhancing human capabilities than replacing them. Their applications more often increase professional, managerial, and marketing jobs and decrease manual, clerical, and frontline service jobs. These startups sell to firms of different sizes, in different industries and nations, but the distribution of activity is distinct from that of larger firms. Firms serving EU customers appear to use higher levels of data protection.

The use of neural networks in developing novel embryo culture media-formulations

Rapid advances in computational technology, especially the development of commercially available, user-friendly software running on high-capacity and high-speed computer workstations, are presenting new tools for analyzing complex, interactive data. One of the important applications of these emerging techniques is to study the intricate interactions between molecules in artificial culture media and their importance for in vitro cell propagation. In this paper we describe our experience using a commercial artificial intelligence system to identify substrates and substrate concentrations essential for in vitro bovine embryonic development. Using the “optimize” function of the software system, we were able to significantly improve the progression of bovine embryos to the blastocyst stage.

Logic as an integrated modeling framework

There is a growing interest in the application of Artificial Intelligence (AI) tools and techniques to management science problems. A rich variety of commercial AI software products is now emerging to facilitate this. However, there has been little integration between AI and Management Science (MS) at a theoretical level. This paper proposes the use of formal logic as the basis for this conceptual bridge, and the use of logic programming as a modeling language for management science applications. The framework is illustrated by a logic-based representation, called PM, for specifying production, distribution and inventory problems.

The AAAI-86 conference exhibits: new directions for commercial AI

The annual conference of the Association for the Advancement of Artificial Intelligence (AAAI) is the premier U.S. gathering for artificial intelligence (AI) theoreticians and practitioners. On the commercial side, AAAI is the only event with a comprehensive exhibition that includes most significant U.S. vendors of AI products and services. In 1986 some 5100 people attended AAAI- a very good showing considering that the 1987 International Joint Conference on Artificial Intelligence (IJCAI) drew about the same number of people even with its substantial international support. The commercial exhibits at AAAI-86 (110 exhibitors; 80,000 square feet) gave us opportunity to take a snapshot of an industry in transition. What I saw was a dramatic increase in the commercialization of AI technology and a decrease in the mystique, smoke, and hype. A preliminary tour of the AAAI-86 exhibits indicated that participants could expect substantial changes from the situation at IJCAI-85.