scholarly journals OpenClinical.net: Artificial intelligence and knowledge engineering at the point of care

2020 ◽  
Vol 27 (2) ◽  
pp. e100141
Author(s):  
John Fox ◽  
Matthew South ◽  
Omar Khan ◽  
Catriona Kennedy ◽  
Peter Ashby ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Guidelines ◽  
Cognitive Skills ◽  
Knowledge Engineering ◽  
Care Quality ◽  
Patient Specific ◽  
Supporting Evidence ◽  
Care Planning ◽  
Link Type ◽  
Wide Range

ObjectiveOpenClinical.net is a way of disseminating clinical guidelines to improve quality of care whose distinctive feature is to combine the benefits of clinical guidelines and other human-readable material with the power of artificial intelligence to give patient-specific recommendations. A key objective is to empower healthcare professionals to author, share, critique, trial and revise these ‘executable’ models of best practice.DesignOpenClinical.net Alpha (www.openclinical.net) is an operational publishing platform that uses a class of artificial intelligence techniques called knowledge engineering to capture human expertise in decision-making, care planning and other cognitive skills in an intuitive but formal language called PROforma.3 PROforma models can be executed by a computer to yield patient-specific recommendations, explain the reasons and provide supporting evidence on demand.ResultsPROforma has been validated in a wide range of applications in diverse clinical settings and specialties, with trials published in high impact peer-reviewed journals. Trials have included patient workup and risk assessment; decision support (eg, diagnosis, test and treatment selection, prescribing); adaptive care pathways and care planning. The OpenClinical software platform presently supports authoring, testing, sharing and maintenance. OpenClinical’s open-access, open-source repository Repertoire currently carries approximately 50+ diverse examples (https://openclinical.net/index.php?id=69).ConclusionOpenClinical.net is a showcase for a PROforma-based approach to improving care quality, safety, efficiency and better patient experience in many kinds of routine clinical practice. This human-centred approach to artificial intelligence will help to ensure that it is developed and used responsibly and in ways that are consistent with professional priorities and public expectations.

General AI

2020 ◽  
pp. 194-206
Author(s):  
Stephen K. Reed
Keyword(s):  
Artificial Intelligence ◽  
Standard Model ◽  
Nobel Prize ◽  
Cognitive Skills ◽  
General Purpose ◽  
Intelligence Tests ◽  
Grand Challenges ◽  
Wide Range ◽  
The Mind

People use their cognitive skills to solve a wide range of problems whereas computers solve only a limited number of specific problems. A goal of artificial intelligence (AI) is to build on its previous success in specific environments to advance toward the generality of human level intelligence. People are efficient general-purpose learners who can adapt to many situations such as navigating in spatial environments and communicating by using language. To compare human and machine reasoning the AI community has proposed a standard model of the mind. Measuring progress in achieving general AI will require a wide variety of intelligence tests. Grand challenges, such as helping scientists win a Nobel prize, should stimulate development efforts.

scholarly journals Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study

2020 ◽  
Author(s):  
Yuchen Luo ◽  
Yi Zhang ◽  
Ming Liu ◽  
Yihong Lai ◽  
Panpan Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
High Performance ◽  
Detection System ◽  
Random Order ◽  
Colon Polyps ◽  
Clinical Environment ◽  
Polyp Detection ◽  
Link Type ◽  
Polyp Detection Rate

Abstract Background and aims Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov. (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265

scholarly journals mSphere of Influence: the Rise of Artificial Intelligence in Infection Biology

mSphere ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Artur Yakimovich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Label Free ◽  
Edge Label ◽  
Bacterial Colony ◽  
Link Type ◽  
Colony Counting ◽  
Anthrax Spores ◽  
Infection Biology

ABSTRACT Artur Yakimovich works in the field of computational virology and applies machine learning algorithms to study host-pathogen interactions. In this mSphere of Influence article, he reflects on two papers “Holographic Deep Learning for Rapid Optical Screening of Anthrax Spores” by Jo et al. (Y. Jo, S. Park, J. Jung, J. Yoon, et al., Sci Adv 3:e1700606, 2017, https://doi.org/10.1126/sciadv.1700606) and “Bacterial Colony Counting with Convolutional Neural Networks in Digital Microbiology Imaging” by Ferrari and colleagues (A. Ferrari, S. Lombardi, and A. Signoroni, Pattern Recognition 61:629–640, 2017, https://doi.org/10.1016/j.patcog.2016.07.016). Here he discusses how these papers made an impact on him by showcasing that artificial intelligence algorithms can be equally applicable to both classical infection biology techniques and cutting-edge label-free imaging of pathogens.

scholarly journals Stem cell therapies and benefaction of somatic cell nuclear transfer cloning in COVID-19 era

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Birbal Singh ◽  
Gorakh Mal ◽  
Vinod Verma ◽  
Ruchi Tiwari ◽  
Muhammad Imran Khan ◽  
...  
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Regenerative Medicine ◽  
Somatic Cell ◽  
Somatic Cell Nuclear Transfer ◽  
Nuclear Transfer ◽  
Drug Testing ◽  
Human Interaction ◽  
Patient Specific ◽  
Wide Range

Abstract Background The global health emergency of COVID-19 has necessitated the development of multiple therapeutic modalities including vaccinations, antivirals, anti-inflammatory, and cytoimmunotherapies, etc. COVID-19 patients suffer from damage to various organs and vascular structures, so they present multiple health crises. Mesenchymal stem cells (MSCs) are of interest to treat acute respiratory distress syndrome (ARDS) caused by SARS-CoV-2 infection. Main body Stem cell-based therapies have been verified for prospective benefits in copious preclinical and clinical studies. MSCs confer potential benefits to develop various cell types and organoids for studying virus-human interaction, drug testing, regenerative medicine, and immunomodulatory effects in COVID-19 patients. Apart from paving the ways to augment stem cell research and therapies, somatic cell nuclear transfer (SCNT) holds unique ability for a wide range of health applications such as patient-specific or isogenic cells for regenerative medicine and breeding transgenic animals for biomedical applications. Being a potent cell genome-reprogramming tool, the SCNT has increased prominence of recombinant therapeutics and cellular medicine in the current era of COVID-19. As SCNT is used to generate patient-specific stem cells, it avoids dependence on embryos to obtain stem cells. Conclusions The nuclear transfer cloning, being an ideal tool to generate cloned embryos, and the embryonic stem cells will boost drug testing and cellular medicine in COVID-19.

scholarly journals Corporate Governance of Artificial Intelligence in the Public Interest

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 275
Author(s):  
Peter Cihon ◽  
Jonas Schuett ◽  
Seth D. Baum
Keyword(s):  
Artificial Intelligence ◽  
Corporate Governance ◽  
Law Enforcement ◽  
Nonprofit Organizations ◽  
Public Interest ◽  
Department Of Defense ◽  
The Public ◽  
Wide Range ◽  
The Media ◽  
The U.S

Corporations play a major role in artificial intelligence (AI) research, development, and deployment, with profound consequences for society. This paper surveys opportunities to improve how corporations govern their AI activities so as to better advance the public interest. The paper focuses on the roles of and opportunities for a wide range of actors inside the corporation—managers, workers, and investors—and outside the corporation—corporate partners and competitors, industry consortia, nonprofit organizations, the public, the media, and governments. Whereas prior work on multistakeholder AI governance has proposed dedicated institutions to bring together diverse actors and stakeholders, this paper explores the opportunities they have even in the absence of dedicated multistakeholder institutions. The paper illustrates these opportunities with many cases, including the participation of Google in the U.S. Department of Defense Project Maven; the publication of potentially harmful AI research by OpenAI, with input from the Partnership on AI; and the sale of facial recognition technology to law enforcement by corporations including Amazon, IBM, and Microsoft. These and other cases demonstrate the wide range of mechanisms to advance AI corporate governance in the public interest, especially when diverse actors work together.

The State of the Art of Rubber-Seal Technology

1987 ◽  
Vol 60 (3) ◽  
pp. 381-416 ◽  
Author(s):  
B. S. Nau
Keyword(s):  
State Of The Art ◽  
Test Procedure ◽  
Working Party ◽  
The State ◽  
Supporting Evidence ◽  
Wide Range ◽  
Lip Seals ◽  
Alternative Hypotheses ◽  
Series Of Experiments ◽  
Range Of Values

Abstract The understanding of the engineering fundamentals of rubber seals of all the various types has been developing gradually over the past two or three decades, but there is still much to understand, Tables V–VII summarize the state of the art. In the case of rubber-based gaskets, the field of high-temperature applications has scarcely been touched, although there are plans to initiate work in this area both in the U.S.A. at PVRC, and in the U.K., at BHRA. In the case of reciprocating rubber seals, a broad basis of theory and experiment has been developed, yet it still is not possible to design such a seal from first principles. Indeed, in a comparative series of experiments run recently on seals from a single batch, tested in different laboratories round the world to the same test procedure, under the aegis of an ISO working party, a very wide range of values was reported for leakage and friction. The explanation for this has still to be ascertained. In the case of rotary lip seals, theories and supporting evidence have been brought forward to support alternative hypotheses for lubrication and sealing mechanisms. None can be said to have become generally accepted, and it remains to crystallize a unified theory.

Oncology care planning. Elements for care quality and management improving

2006 ◽  
Vol 8 (3) ◽  
pp. 178-184 ◽  
Author(s):  
José Expósito Hernández ◽  
Carmen Domínguez Nogueira
Keyword(s):  
Care Quality ◽  
Care Planning ◽  
Oncology Care

The Research and Application of Artificial Intelligence in the Field of Intelligent Robot

2014 ◽  
Vol 898 ◽  
pp. 763-766
Author(s):  
Zhi Hao Li
Keyword(s):  
Artificial Intelligence ◽  
Environmental Changes ◽  
Vision System ◽  
Mineral Exploration ◽  
Intelligent Robot ◽  
Application Range ◽  
Intelligent Robots ◽  
External Data ◽  
Wide Range ◽  
Artificial Intelligence Application

The research and application of artificial intelligence has a very wide range in intelligent robot field. Intelligent robot can not only make use of artificial intelligence gain access to external data, information, (such as stereo vision system, face recognition and tracking, etc.), and then deal with it so as to exactly describe external environment, and complete a task independently, owing the ability of learning knowledge, but also have self-many kinds of artificial intelligence like judgment and decision making, processing capacity and so on. It can make corresponding decision according to environmental changes. Its application range is expanding. In deep sea exploration, star exploration, mineral exploration, heavy pollution, domestic service, entertainment clubs, health care and so on, the figure of intelligent robots artificial intelligence application can all be seen.

scholarly journals Could metabolomics drive the fate of COVID-19 pandemic? A narrative review on lights and shadows

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Michele Mussap ◽  
Vassilios Fanos
Keyword(s):  
Molecular Mechanisms ◽  
Vascular Dysfunction ◽  
Asymptomatic Infection ◽  
Multiple Organ ◽  
Therapeutic Interventions ◽  
Patient Specific ◽  
Wide Range ◽  
Life Threatening ◽  
The Individual ◽  
Host Metabolism

Abstract Human Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection activates a complex interaction host/virus, leading to the reprogramming of the host metabolism aimed at the energy supply for viral replication. Alterations of the host metabolic homeostasis strongly influence the immune response to SARS-CoV-2, forming the basis of a wide range of outcomes, from the asymptomatic infection to the onset of COVID-19 and up to life-threatening acute respiratory distress syndrome, vascular dysfunction, multiple organ failure, and death. Deciphering the molecular mechanisms associated with the individual susceptibility to SARS-CoV-2 infection calls for a system biology approach; this strategy can address multiple goals, including which patients will respond effectively to the therapeutic treatment. The power of metabolomics lies in the ability to recognize endogenous and exogenous metabolites within a biological sample, measuring their concentration, and identifying perturbations of biochemical pathways associated with qualitative and quantitative metabolic changes. Over the last year, a limited number of metabolomics- and lipidomics-based clinical studies in COVID-19 patients have been published and are discussed in this review. Remarkable alterations in the lipid and amino acid metabolism depict the molecular phenotype of subjects infected by SARS-CoV-2; notably, structural and functional data on the lipids-virus interaction may open new perspectives on targeted therapeutic interventions. Several limitations affect most metabolomics-based studies, slowing the routine application of metabolomics. However, moving metabolomics from bench to bedside cannot imply the mere determination of a given metabolite panel; rather, slotting metabolomics into clinical practice requires the conversion of metabolic patient-specific data into actionable clinical applications.

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