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Results for “Generative Artificial Intelligence”

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PubMed1993

An evaluation of explanations of probabilistic inference.

Providing explanations of the conclusions of decision-support systems can be viewed as presenting inference results in a manner that enhances the user's insight into how these results were obtained. The ability to explain inferences has been demonstrated to be an important factor in making medical decision-support systems acceptable for clinical use. Although many researchers in artificial intelligence have explored the automatic generation of explanations for decision-support systems based on symbolic reasoning, research in automated explanation of probabilistic results has been limited. We present the results of an evaluation study of INSITE, a program that explains the reasoning of decision-support systems based on Bayesian belief networks. In the domain of anesthesia, we compared subjects who had access to a belief network with explanations of the inference results to control subjects who used the same belief network without explanations. We show that, compared to control subjects, the explanation subjects demonstrated greater diagnostic accuracy, were more confident about their conclusions, were more critical of the belief network, and found the presentation of the inference results more clear.

Bayes Theorem↗

PubMed2004

Evaluating alternative gait strategies using evolutionary robotics.

Evolutionary robotics is a branch of artificial intelligence concerned with the automatic generation of autonomous robots. Usually the form of the robot is predefined and various computational techniques are used to control the machine's behaviour. One aspect is the spontaneous generation of walking in legged robots and this can be used to investigate the mechanical requirements for efficient walking in bipeds. This paper demonstrates a bipedal simulator that spontaneously generates walking and running gaits. The model can be customized to represent a range of hominoid morphologies and used to predict performance parameters such as preferred speed and metabolic energy cost. Because it does not require any motion capture data it is particularly suitable for investigating locomotion in fossil animals. The predictions for modern humans are highly accurate in terms of energy cost for a given speed and thus the values predicted for other bipeds are likely to be good estimates. To illustrate this the cost of transport is calculated for Australopithecus afarensis. The model allows the degree of maximum extension at the knee to be varied causing the model to adopt walking gaits varying from chimpanzee-like to human-like. The energy costs associated with these gait choices can thus be calculated and this information used to evaluate possible locomotor strategies in early hominids.

Animals↗

PubMed1992

An evaluation of explanations of probabilistic inference.

Providing explanations of the conclusions of decision-support systems can be viewed as presenting inference results in a manner that enhances the user's insight into how these results were obtained. The ability to explain inferences has been demonstrated to be an important factor in making medical decision-support systems acceptable for clinical use. Although many researchers in artificial intelligence have explored the automatic generation of explanations for decision-support systems based on symbolic reasoning, research in automated explanation of probabilistic results has been limited. We present the results of an an evaluation study of INSITE, a program that explains the reasoning of decision-support systems based on Bayesian belief networks. In the domain of anesthesia, we compared subjects who had access to a belief network with explanations of the inference results, to control subjects who used the same belief network without explanations. We show that, compared to control subjects, the explanation subjects demonstrated greater diagnostic accuracy, were more confident about their conclusions, were more critical of the belief network, and found the presentation of the inference results more clear.

Anesthesia↗

PubMed1993

SPROUT: a program for structure generation.

SPROUT is a new computer program for constrained structure generation that is designed to generate molecules for a range of applications in molecular recognition. It uses artificial intelligence techniques to moderate the combinatorial explosion that is inherent in structure generation. The program is presented here for the design of enzyme inhibitors. Structure generation is divided into two phases: (i) primary structure generation to produce molecular graphs to fit the steric constraints; and (ii) secondary structure generation which is the process of introducing appropriate functionality to the graphs to produce molecules that satisfy the secondary constraints, e.g., electrostatics and hydrophobicity. Primary structure generation has been tested on two enzyme receptor sites; the p-amidino-phenyl-pyruvate binding site of trypsin and the acetyl pepstatin binding site of HIV-1 protease. The program successfully generates structures that resemble known substrates and, more importantly, the predictive power of the program has been demonstrated by its ability to suggest novel structures.

Artificial Intelligence↗

PubMed2005

GenIE: an intelligent system for writing genetic counseling patient letters.

We are developing GenIE, a prototype intelligent system to create first drafts of genetic counseling patient letters. GenIE will apply natural language generation techniques to construct the first draft of a letter for subsequent review and editing, if needed, by the genetic counselor. For purposes of knowledge acquisition, we have been analyzing a corpus of patient letters. Based on the corpus analysis we are developing a knowledge base and text generation strategies.

Artificial Intelligence↗

PubMed2000

Subjective symptoms acquisition system in a health promotion system for the elderly. Committee of System Development, Council of Japan AMHTS Institutions.

A previous report was concerned with the evaluation of quality of life using a Health Promotion System for the Elderly. In the present report, we describe one part of that system: a subjective symptoms acquisition and reporting system. The main purpose of this system is to permit any physician or nurse to uniformly employ questionnaires to acquire accurate subjective symptoms. This system is applied in three steps. First, the subjective answers to 21 questions displayed on a personal computer are obtained. These answers correspond to the basic subjective symptoms. Second, if a basic subjective symptom is "positive", more detailed questions are automatically generated. Finally, clear sentences regarding subjective symptoms are generated and output as a "finding report". This information is helpful to physicians and nurses in their health-counseling work. An artificial intelligence (AI) program based on "XpertRule" produces detailed questions which are generated by an interactive questionnaire using branching logical rules.

Aged↗

PubMed2024

Drug repurposing in status epilepticus.

The treatment of status epilepticus (SE) has changed little in the last 20 years, largely because of the high risks and costs of new drug development for SE. Moreover, SE poses specific challenges to drug development, such as patient diversity, logistical hurdles, and the need for acute treatment strategies that differ from chronic seizure prevention. This has reduced the appetite of industry to develop new drugs in this area. Drug repurposing is an attractive approach to address this unmet need. It offers significant advantages, including reduced development time, lower costs, and higher success rates, compared to novel drug development. Here I demonstrate how novel methods integrating biological knowledge and computational methods can be applied to drug repurposing in status epilepticus. Biological approaches focus on addressing mechanisms underlying drug resistance in SE (using for example ketamine, tacrolimus and safinamide) and longer-term consequences (using for example omaveloxolone, celecoxib and losartan). Additionally, artificial intelligence platforms, such as ChatGPT, can rapidly generate promising drug lists, while in silico methods can analyze gene expression changes to predict molecular targets. Combining AI and in silico approaches has identified several candidate drugs, including metformin, sirolimus and riluzole, for SE treatment. Despite the promise of repurposing, challenges remain, such as intellectual property issues and regulatory barriers. Nonetheless, drug repurposing presents a viable solution to the high costs and slow progress of traditional drug development for SE. This paper is based on a presentation made at the 9th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures, in April 2024.

Animals↗

PubMed1996

The contribution of image cytometry and artificial intelligence-related methods of numerical data analysis for adipose tumor histopathologic classification.

Thirty-five lipomatous tumors were quantitatively described using 47 variables generated by means of computer-assisted microscope analysis. Of these 47 quantitative variables, 27 were computed on Feulgen-stained specimens (25 on cytologic and 2 on histologic samples) and, of the remaining 20, 8 related to vimentin and S-100 protein immunostaining patterns and the other 12 to the glycohistochemical staining patterns of peanut agglutinin, succinylated wheat germ agglutinin, and concavalin A agglutinin. The 35 lipomatous tumors included 6 atypical lipomas and 8 well differentiated, 5 dedifferentiated, 6 myxoid, and 10 pleomorphic liposarcomas. The actual diagnostic value contributed by each of the 47 variables with respect to the 5 lipomatous tumor groups was determined by means of the decision tree technique, an artificial intelligence-related algorithm that forms part of the supervised learning algorithms. Of the 47 quantitative variables, the decision tree technique retained 8: i.e., 2 tissue architecture-, 2 DNA ploidy level-, 2 morphonuclear-, 1 lectin histochemical-, and 1 vimentin immunostain-related variables. The decision tree technique made use of these 8 variables to set up logical rules that make it possible to identify atypical lipomas from well differentiated liposarcomas, on the one hand, and dedifferentiated liposarcomas from those that are well differentiated and pleomorphic, on the other. Thus, the combination of an artificial intelligence algorithm analyzing quantitative variables generated by means of the computer-assisted microscope analysis of cytologic and histologic samples from lipomatous tumors can be considered an expert system contributing significant diagnostic information to conventional diagnosis.

Algorithms↗

PubMed2026

AI-Based 3D Heterogeneous Network Model for Functional Prediction of Epigenetics.

Human biology and diseases are the result of constantly evolving processes within an intricately complex molecular network of interactions, such as epigenetic regulation. Epigenetics refers to heritable changes in gene expression that occur without alterations to the underlying DNA sequence. These changes, driven by mechanisms such as DNA methylation, histone modifications, and noncoding RNAs, play critical roles in regulating chromatin structure and gene activity. Epigenetic regulation offers valuable insights into biological systems, and when integrated with sophisticated analyses, it enables us to gain insights into gene regulation and cellular behavior. Here, we describe an artificial intelligence (AI)-based model that is capable of generating 3-dimensional (3D) heterogeneous network by integrating multimodal data for the functional prediction of epigenetic mechanisms, emphasizing its applications in medicine, developmental biology, and personalized therapeutics. Heterogeneous networks in biology are powerful tools for understanding the complex interactions and interdependencies within biological systems. Key advancements in AI and multiomics data integration have propelled this field, offering new insights into disease mechanisms, biomarker discovery, and therapeutic interventions.

Epigenesis, Genetic↗

PubMed2005

A novel artificial intelligence method for weekly dietary menu planning.

OBJECTIVES: Menu planning is an important part of personalized lifestyle counseling. The paper describes the results of an automated menu generator (MenuGene) of the web-based lifestyle counseling system Cordelia that provides personalized advice to prevent cardiovascular diseases. METHODS: The menu generator uses genetic algorithms to prepare weekly menus for web users. The objectives are derived from personal medical data collected via forms in Cordelia, combined with general nutritional guidelines. The weekly menu is modeled as a multilevel structure. RESULTS: Results show that the genetic algorithm-based method succeeds in planning dietary menus that satisfy strict numerical constraints on every nutritional level (meal, daily basis, weekly basis). The rule-based assessment proved capable of manipulating the mean occurrence of the nutritional components thus providing a method for adjusting the variety and harmony of the menu plans. CONCLUSIONS: By splitting the problem into well determined sub-problems, weekly menu plans that satisfy nutritional constraints and have well assorted components can be generated with the same method that is for daily and meal plan generation.

Algorithms↗

PubMedDate not supplied

The goal of PACS in Nagoya University Hospital.

In Nagoya University Hospital, a Radiology Intelligent Information System (RIIS) is under construction which will be linked with the Hospital Intelligent Information System (HIIS). RIIS is composed of the radiation oncology information system and the diagnostic radiology information system which is named Imaging Diagnosis Intelligent Information System (IDIIS). IDIIS consists of three parts: (a) the Imaging Diagnosis Management System (IDMS); (b) the Picture Archiving and Communication System (PACS); (c) the Report Generation Support System for Imaging Diagnosis (RGSS-ID). Artificial intelligence methodology is applied to RGSS-ID and IDMS which includes the ordering and scheduling system of diagnostic imaging. IDIIS has an important role to improve the quality of patient care and medical education as well as image management and is an essential component for the implementation of HIIS.

Computer Systems↗

PubMed2025

In silico generation of synthetic cancer genomes using generative AI.

Understanding how genomic alterations drive cancer is key to advancing precision oncology. To detect these alterations, accurate algorithms are used; however, due to privacy concerns, few deeply sequenced cancer genomes can be shared, limiting benchmarking and representing a major obstacle to the improvement of analytic tools. To address this, we developed OncoGAN, a generative AI model combining adversarial networks and variational autoencoders to create realistic synthetic cancer genomes. Trained on large-scale genomic datasets, OncoGAN accurately reproduces somatic mutations, copy number alterations, and structural variants across cancer types while preserving donors' privacy. The synthetic genomes reflect tumor-specific mutational signatures and positional mutation patterns. Using DeepTumour, we validated the synthetic data's fidelity, showing high concordance between generated and predicted tumors. Moreover, augmenting the training data with synthetic genomes improved DeepTumour's accuracy, underscoring OncoGAN's potential to generate shareable datasets with known ground truths for benchmarking and enhancement of cancer genome analysis tools.

Humans↗

PubMed1989

[RGSS-IDJ and its application to cranial computed tomography].

RGSS-IDJ is developed as the Japanese version of Report Generation Support System for Imaging Diagnosis (RGSS-ID), which is a developmental computer system that applies artificial intelligence (AI) methods to a reporting system. Now RGSS-IDJ supports the report generation of cranial computed tomography. A representation scheme called Generalized Finding Representation (GFR) is proposed, to bridge the gap between natural language expressions in the radiographic report and AI methods. GRF for RGSS-IDJ is the same as for RGSS-ID. The basic style for entering the findings on the radiograph is the dialogue system with the routine of query and answering it by selecting items with a mouse. This system encodes the input findings into the network expressions, which are represented as the list form in the LISP computer language. And, it reserves them into the knowledge data base. The content of the report will be able to be utilized for various analyses within AI paradigm. The final radiographic report is made in the natural Japanese language.

Artificial Intelligence↗

PubMed1984

Implementation of a computer-based test generator to evaluate health professions continuing education.

A variety of artificial-intelligence-based expert medical systems have been adapted to evaluate a learner's performance in the information areas in which the systems are expert. This paper describes a similar adaptation of a computer-based health sciences tutor (called the COMMES system). The Evaluation Consultant system to be described adapts the COMMES system to become a test generator. This computer-based consultant generates tests entirely on its own, covering programs of study that the COMMES system previously constructed to satisfy a user's identified needs. A health professional is awarded continuing education credits after (1) finishing a study unit constructed by COMMES and (2) completing successfully a test created by the Evaluation Consultant. This system is being implemented in several test sites and has significant advantages for the support of continuing education, especially in rural or isolated areas.

Computer-Assisted Instruction↗

PubMed2005

Novel specificities emerge by stepwise duplication of functional modules.

A functional module can be defined as a spatially or chemically isolated set of functionally associated components that accomplishes a discrete biological process. Modularity is a key attribute of cellular systems, but the mechanisms that underlie the evolution of functional modules are largely unknown. Duplication of modules has been shown to be an efficient mechanism for the generation of functional innovation in the field of artificial intelligence, but has not been studied in biological networks. Therefore, we ask whether module duplication occurs in cellular networks. We developed a generic framework for the analysis of module duplication, and use it in a large-scale analysis of Saccharomyces cerevisiae protein complexes. Protein complexes are well defined, experimentally derived, functional modules. We observe that at least 6%-20% of the protein complexes have strong similarity to other complexes; thus a considerable fraction has evolved by duplication. Our results indicate that many complexes evolved by step-wise partial duplications. We show that duplicated complexes retain the same overall function, but have different binding specificities and regulation, revealing that duplication of these modules is associated with functional specialization.

Evolution, Molecular↗

PubMed2006

How a visual surveillance system hypothesizes how you behave.

In the last few years, the installation of a large number of cameras has led to a need for increased capabilities in video surveillance systems. It has, indeed, been more and more necessary for human operators to be helped in the understanding of ongoing activities in real environments. Nowadays, the technology and the research in the machine vision and artificial intelligence fields allow one to expect a new generation of completely autonomous systems able to reckon the behaviors of entities such as pedestrians, vehicles, and so forth. Hence, whereas the sensing aspect of these systems has been the issue considered the most so far, research is now focused mainly on more newsworthy problems concerning understanding. In this article, we present a novel method for hypothesizing the evolution of behavior. For such purposes, the system is required to extract useful information by means of low-level techniques for detecting and maintaining track of moving objects. The further estimation of performed trajectories, together with objects classification, enables one to compute the probability distribution of the normal activities (e.g., trajectories). Such a distribution is defined by means of a novel clustering technique. The resulting clusters are used to estimate the evolution of objects' behaviors and to speculate about any intention to act dangerously. The provided solution for hypothesizing behaviors occurring in real environments was tested in the context of an outdoor parking lot

Algorithms↗

PubMed2004

Distributed interactive virtual environments for collaborative experiential learning and training independent of distance over Internet2.

Medical knowledge and skills essential for tomorrow's healthcare professionals continue to change faster than ever before creating new demands in medical education. Project TOUCH (Telehealth Outreach for Unified Community Health) has been developing methods to enhance learning by coupling innovations in medical education with advanced technology in high performance computing and next generation Internet2 embedded in virtual reality environments (VRE), artificial intelligence and experiential active learning. Simulations have been used in education and training to allow learners to make mistakes safely in lieu of real-life situations, learn from those mistakes and ultimately improve performance by subsequent avoidance of those mistakes. Distributed virtual interactive environments are used over distance to enable learning and participation in dynamic, problem-based, clinical, artificial intelligence rules-based, virtual simulations. The virtual reality patient is programmed to dynamically change over time and respond to the manipulations by the learner. Participants are fully immersed within the VRE platform using a head-mounted display and tracker system. Navigation, locomotion and handling of objects are accomplished using a joy-wand. Distribution is managed via the Internet2 Access Grid using point-to-point or multi-casting connectivity through which the participants can interact. Medical students in Hawaii and New Mexico (NM) participated collaboratively in problem solving and managing of a simulated patient with a closed head injury in VRE; dividing tasks, handing off objects, and functioning as a team. Students stated that opportunities to make mistakes and repeat actions in the VRE were extremely helpful in learning specific principles. VRE created higher performance expectations and some anxiety among VRE users. VRE orientation was adequate but students needed time to adapt and practice in order to improve efficiency. This was also demonstrated successfully between Western Australia and UNM. We successfully demonstrated the ability to fully immerse participants in a distributed virtual environment independent of distance for collaborative team interaction in medical simulation designed for education and training. The ability to make mistakes in a safe environment is well received by students and has a positive impact on their understanding, as well as memory of the principles involved in correcting those mistakes. Bringing people together as virtual teams for interactive experiential learning and collaborative training, independent of distance, provides a platform for distributed "just-in-time" training, performance assessment and credentialing. Further validation is necessary to determine the potential value of the distributed VRE in knowledge transfer, improved future performance and should entail training participants to competence in using these tools.

Computer Simulation↗

PubMed1987

A rule-based process control method with feedback.

This paper describes a method for developing a rule-based control algorithm for process control that includes feedback and modification of the rule base from samples of the process output. The rules are used to guide the process toward the desired goal, or goals, and as the process operates new data samples allow the inference of new rules so that the process is automatically optimized and the rules for controlling the process are automatically generated.

Algorithms↗