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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↗

PubMed1991

Enzymes and theoretical biology: sketch of an informational perspective of the cell.

In the theoretical scenarios of biology, new insights can be gained by the introduction of information-processing and artificial intelligence concepts, helping to organize the explanation of the many intra- and inter-cellular phenomena that molecular biology is accumulating. Enzymes contain some of the immediate clues; the whole informational processing of prokaryotic cells is another central subject of search. Additionally, prolonging the informational perspective of the cell, a significant parallel can be drawn between informational processes in biological, social and artificial intelligence systems. A more tangible definition of biological complexity and biological intelligence emerges.

Cells↗

PubMed1995

Development of a connectionist expert system to identify foot problems based on under-foot pressure patterns.

The first phase of developing a connectionist expert system is described in this study. Dynamic pressure patterns below the foot obtained by the emed f system of 18 subjects were classified into one of the three categories: normals, pes cavus and hallux valgus. As opposed to the traditional approach for the analysis of the pressure patterns by quantifying isolated segments, a neural network based expert system was trained to perform objective classification of the patterns while keeping their integrity. The results of the backpropagation paradigm were unexpectedly good considering the limiting factors which hindered the performance of the neural networks. The successes indicate the potential of artificial intelligence in automated analysis of foot pathologies based on theemed f system. RELEVANCE:--This paper describes the first steps towards a novel automated screening system, which classifies the patient with foot abnormalities into the pathological category he or she belongs to, based on the pressure pattern below the foot. The decision making is objective, and shows a practical example of the use of artificial intelligence.

Journal Article↗

PubMed2001

An application of artificial neural intelligence for personal dose assessment using a multi-area OSL dosimetry system.

Significant advances have been made in recent years to improve measurement technology and performance of phosphor materials in the fields of optically stimulated luminescence (OSL) dosimetry. Pulsed and continuous wave OSL studies recently carried out on alpha-Al2O3:C have shown that the material seems to be the most promising for routine application of OSL for dosimetric purposes. The main objective of the study is to propose a new personal dosimetry system using alpha-Al2O3:C by taking advantage of its optical properties and energy dependencies. In the process of the study, a new dose assessment algorithm was developed using artificial neural networks in hopes of achieving a higher degree of accuracy and precision in personal OSL dosimetry system. The original hypothesis of this work is that the spectral information of X- and gamma-ray fields may be obtained by the analysis of the response of a multi-element system. In this study, a feedforward neural network using the error back-propagation method with Bayesian optimization was applied for the response unfolding procedure. The validation of the proposed algorithm was investigated by unfolding the 10 measured responses of alpha-Al2O3:C for arbitrarily mixed photon fields which range from 20 to 662 keV.

Algorithms↗

PubMed1998

An interim determination of health gain from oral cancer and precancer screening: 3. Preselecting high risk individuals.

OBJECTIVE: To perform a sensitivity analysis using a decision model for simulating opportunistic screening for oral cancer and precancer in order to obtain an estimate of health gain from screening high risk dental patients, identified with the aid of artificial intelligence. DESIGN: A hypothetical opportunistic screening programme based on preselection of high risk patients was compared with screening all eligible patients and with a 'do nothing' scenario. The basic assumptions and data used in the model have been described previously. SETTING: Screening would be carried out in dental practices. PARTICIPANTS: A notional population of 100,000 adults of average age 55 years and 20 years life expectancy. INTERVENTIONS: Systematic clinical examination of the oral mucosa of high risk individuals identified by artificial intelligence on the basis of personal characteristics and life style. OUTCOME MEASURES: QALYs and equivalent lives saved RESULTS: The following estimates of health gain were obtained: the preselected screened group (n = 25,000) would achieve 1,993,294 QALYs. Without preselection, screening an unselected population of 100,000 eligible adults would result in 1,993,094 QALYs. Under the 'do nothing' scenario, 100,000 unscreened individuals would achieve 1,992,982 QALYs. Selective screening could avoid the equivalent of 15 deaths per 100,000 subjects examined. Screening preselected subjects at heightened risk would save the equivalent of two to three times the number of healthy lives compared with non-selective screening. Only one quarter of the population would need to be examined. CONCLUSIONS: Opportunistic screening of preselected dental patients for oral cancer and precancer appears to be a promising health promotion strategy and should be subject to formal economic appraisal.

Cost-Benefit Analysis↗

PubMed1989

The use of computers in the control of diabetes in children and adolescents.

The actual and possible applications of computers in diabetes management are manifold. There are programs for medical information, storage and analysis, pertinent literature search, artificial intelligence, algorithms assisting the patient to adjust the insulin dose and education programs. One very useful microcomputer program DIACON is described in detail. This program is both an educational and therapeutic tool in that it increases both knowledge and long term compliance; it stores, displays and analyses the large amount of self blood glucose monitoring (SBGM) data, HbA1, C-peptide, nutritional data, sport activities and insulin dose. It is concluded that the use of computers in the management of diabetes has become a necessity, but despite the programs of artificial intelligence and algorithms, it is critical that the physician retains the ultimate responsibility for diagnostic and therapeutic strategies.

Adolescent↗

PubMed2026

Induced pluripotent stem cell reprogramming: methodological evolution and challenges in clinical translation.

Cell reprogramming can transform somatic cells into induced pluripotent stem cells providing a platform for patient-specific disease modeling, drug screening and regenerative medicine research. Since the advent of OKSM-mediated reprogramming, the system of technical approaches has evolved continuously - from integrated viral vectors to non-integrated episomal systems and, more recently, chemical reprogramming and CRISPR approaches. The simultaneous advances in single-cell multi-omics, biomaterials engineering, and artificial intelligence have further refined the controllability and precision of the reprogramming process. Despite these innovations, problems persist that hinder clinical translation: incomplete epigenetic resetting, ongoing clonal heterogeneity, genomic instability in long-term culture, and the lack of standardized Good Manufacturing Practice protocols for large-scale manufacturing. This review summarizes the trajectory of iPSC reprogramming technologies, with special emphasis on the translational applicability of each modality. We evaluated viral and nonviral delivery systems, chemical reprogramming, strategies that aid gene editing, and emerging engineering platforms, including microfluidics, smart biomaterials, and artificial-intelligence-driven process optimization. We further identify the core "translational triltrilas", namely, the inherent tradeoffs between security, homogeneity, and scalability, and propose a comprehensive strategy to overcome these bottlenecks. By linking basic mechanistic understandings with industrial and regulatory considerations, this review aims to provide a route for transitioning iPSC technology from a laboratory tool to a clinically viable manufacturing platform.

clinical translation↗

PubMed1989

The use of inference strategies in the differential diagnosis of microcytic anemia.

A new expert system developed on a Macintosh personal computer using a commercially available artificial intelligence shell was compared with four different discriminant functions (DFs) for the differentiation of microcytic anemia into etiologic categories. Several databases were used with a different composition but all contained at least some samples from thalassemic individuals and from patients with iron deficiency anemia. The DFs analyzed were those proposed by England and Fraser, Green and colleagues, Mentzer, and by Shine and Lal. None of the databases performed satisfactorily when used singly, whereas very high false-positive rates were obtained by one of them. The diagnostic efficiency was somewhat improved by combining several DFs. An expert system using an artificial intelligence "shell" with an "interference engine" was developed using cluster analysis and a set of learning examples. The input necessary for the system to achieve a conclusion consists of MCV, RBC, and RDW as well as a statement as to whether the patient has anemia. Based upon the values of these parameters, the expert system will give an "advice" regarding the probabilities for thalassemia, iron deficiency, and/or other probabilities such as previous transfusions, anemia of chronic disease, laboratory error, etc. In a prospective trial, the system functioned with an accuracy of better than 85%.

Anemia, Hypochromic↗

PubMed2025

The Application of Preventive Medicine in the Future Digital Health Era.

A number of seismic shifts are expected to reshape the future of medicine. The global population is rapidly aging, significantly impacting the global disease burden. Medicine is undergoing a paradigm shift, defining and diagnosing diseases at earlier stages and shifting the health care focus from treating diseases to preventing them. The application and purview of digital medicine are expected to broaden significantly. Furthermore, the COVID-19 pandemic has further accelerated the shift toward predictive, preventive, personalized, and participatory (P4) medicine, and has identified health care accessibility, affordability, and patient empowerment as core values in the future digital health era. This "left shift" toward preventive care is anticipated to redefine health care, emphasizing health promotion over disease treatment. In the future, the traditional triad of preventive medicine-primary, secondary, and tertiary prevention-will be realized with technologies such as genomics, artificial intelligence, bioengineering and wearable devices, and telemedicine. Breast cancer and diabetes serve as case studies to demonstrate how these technologies such as personalized risk assessment, artificial intelligence-assisted and app-based technologies, have been developed and commercialized to provide personalized preventive care, identifying those at a higher risk and providing instructions and interventions for healthier lifestyles and improved quality of life. Overall, preventive medicine and the use of advanced technology will hold great potential for improving health care outcomes in the future.

Humans↗

PubMed2000

A quality and safety framework for point-of-care clinical guidelines.

The electronic dissemination of medical knowledge in the form of executable clinical guidelines and decision support systems must be accompanied by comprehensive methods for ensuring the quality of their knowledge content and their safety in use. This paper outlines a set of quality and safety requirements, and reviews three current guideline technologies, the Arden Syntax, GLIF and PROforma, against these requirements. The approaches used in these technologies have different strengths, and we propose a general framework for ensuring quality and safety that combines them. This framework brings together the normal documentation standards of medical publishing, rigorous design methods from software engineering, and active safety management techniques from artificial intelligence.

Artificial Intelligence↗

PubMed2004

Learning obstacle avoidance with an operant behavior model.

Artificial intelligence researchers have been attracted by the idea of having robots learn how to accomplish a task, rather than being told explicitly. Reinforcement learning has been proposed as an appealing framework to be used in controlling mobile agents. Robot learning research, as well as research in biological systems, face many similar problems in order to display high flexibility in performing a variety of tasks. In this work, the controlling of a vehicle in an avoidance task by a previously developed operant learning model (a form of animal learning) is studied. An environment in which a mobile robot with proximity sensors has to minimize the punishment for colliding against obstacles is simulated. The results were compared with the Q-Learning algorithm, and the proposed model had better performance. In this way a new artificial intelligence agent inspired by neurobiology, psychology, and ethology research is proposed.

Avoidance Learning↗

PubMed2026

Creating bottom-up RNA transfer vehicles from synthetic protein assemblies.

Evolution guides biological systems to populate ecological niches, with viruses among the most successful examples of this principle. Viruses evolved over billions of years to efficiently transfer genetic information. Although viruses are highly diverse, most have converged towards remarkable similarity in the size and shape of their capsids1,2. By contrast, generative models for protein design enable the creation of protein architectures that are absent from nature3-5. Here we investigate whether protein assemblies designed by artificial intelligence can be functionalized to construct nucleic acid transport vehicles that are independent of evolutionary trajectories. By combining natural protein domains with synthetic protein assemblies, we create more than 100 bottom-up RNA transfer vehicles with unique sizes and shapes. These vehicles surpass the RNA transfer efficiency of widely used delivery vehicles by several orders of magnitude. In addition, we demonstrate that their tropism can be programmed by incorporation of computationally designed peptide binders and use them to deliver therapeutically relevant cargo RNAs into a wide range of cellular models. We show the in vivo biodistribution of one of these vehicles in a mouse at near-single-cell resolution, confirm its safety, and use it to perform a gene-editing treatment strategy for Duchenne muscular dystrophy in patient-derived cells and a pig. Our work demonstrates how proteins created by generative artificial intelligence can be harnessed for the rational engineering of RNA transport systems with the desired properties by overcoming the limitations of natural protein diversity.

Journal Article↗

PubMed2003

Virtual reality surgery: neurosurgery and the contemporary landscape.

OBJECTIVE: Virtual reality-simulated environments have been used for the training of personnel, most notably for military applications, for more than 35 years. The advantages conferred by being able to train novice personnel in a low- to no-risk simulated environment have long been appreciated by the medical community. The recent availability of affordable gigahertz-range microprocessors (once the exclusive domain of the Cray supercomputer) has made photorealistic graphical rendering and manipulation of virtual surgical substrates a reality. Concomitant advances in artificial intelligence systems and the portability of patient-specific magnetic resonance imaging, computed tomographic scanning, and angiographic image data presage the emergence of the surgical simulator as a modern surgical training adjunct. An overview of the status of surgical simulation with regard to its adaptability to current surgical training regimens is presented. METHODS: Extensive MEDLINE, Internet, and other database searches spanning the years 1960 to 2002 were conducted in an effort to delineate the status of simulated surgical environments. RESULTS: As would be expected, most articles addressing surgical simulation as their primary focus have been published in the past decade. A review of this literature demonstrates the broadest application in the field of endoscopic (and laparoscopic) procedures, most likely as a result of the reduced engineering burden with respect to incorporation of a haptic interface. CONCLUSION: The realization of ergonomically acceptable haptic interfaces remains elusive. Improvements in graphical rendering and the incorporation of artificial intelligence functions signal the certain emergence of surgical simulators as a viable supplement to the Halstedian method of surgical training.

Humans↗

PubMed1994

Image analysis and diagnostic classification of hepatocellular carcinoma using neural networks and multivariate discriminant functions.

BACKGROUND: Hepatocellular carcinoma (HCC) is often difficult to diagnose in cytologic material and small tissue biopsies since histomorphologic information is minimal or absent. The potential for misdiagnosis is greatest in attempting to discriminate well-differentiated HCC from dysplastic hepatocytes in cirrhosis. We investigated the feasibility of developing artificial intelligence classification methods based on nuclear image analysis data for use as adjuncts to the morphologic diagnosis of HCC. EXPERIMENTAL DESIGN: Ninety hematoxylin-eosin stained histologic slides including 56 with well- to poorly differentiated HCC and 34 showing a morphologic continuum from normal to markedly dysplastic benign hepatocytes were assembled from four laboratories. A relatively inexpensive PC-based image analysis system was used to measure 35 nuclear morphometric and densitometric parameters of 100 nuclei in each specimen. The data were randomized into classification training and testing sets containing equal numbers of benign and HCC samples. Objective diagnostic classification criteria for HCC based on neural networks and multivariate discriminant functions (DFs) were developed for the most discriminatory subsets of morphometric, densitometric, and combined morphometric/densitometric variables as selected by stepwise discriminant analysis of training data. RESULTS: Morphometric parameters provided the best results with the following testing data positive and negative predictive values (PV+ and PV-) for HCC classification: 86.2% PV+ and 81.3% PV- for a linear DF, 85.7% PV+ and 76.5% PV- for a quadratic DF and 100% PV+ and 85.0% PV- for a neural network. CONCLUSIONS: Our results demonstrate that nuclear image analysis-based objective classification criteria for HCC can be developed using artificial intelligence methods and that histologic material prepared at different institutions can be reliably classified. Neural networks for HCC classification were superior to linear and quadratic DFs. Morphometric data yielded the best results compared with densitometric or combined morphometric/densitometric data.

Carcinoma, Hepatocellular↗

PubMed2026

Microbial partnerships and molecular mechanisms in plant stress physiology for climate-resilient and sustainable farming.

Plant-microbial partnerships and their underlying molecular mechanisms are indispensable, natural drivers of improved nutrient acquisition and stress tolerance in the face of climate-driven environmental challenges. Modern multi-omics tools, when coupled with artificial intelligence and synthetic biology, enable the precise design of targeted bioinoculants and synthetic microbial consortia. Translating these advanced microbiome-based strategies into scalable, field-level agricultural applications provides a sustainable path toward securing global food production while maintaining soil health. Global climate change imposes multifaceted abiotic and biotic stresses on crops, disrupting physiological and molecular processes and threatening agricultural productivity. Plant-associated microbes represent an underexplored yet powerful ally in enhancing crop resilience. This review presents current knowledge of plant-microbe interactions and the molecular mechanisms governing plant stress physiology, with an emphasis on climate-resilient and sustainable farming. Hence, ever-changing environmental cues pose a significant burden on agricultural productivity, and plant-associated microbial communities modulate a cascade of physiological and molecular responses, including production of phytohormones, signaling, regulation of reactive oxygen species homeostasis, and activation of plant immune responses to help plants withstand stress and enhance productivity. Moreover, root exudates, phytohormones, and quorum sensing mediate the central communication networks, facilitating plant-microbe cross talk. Additionally, the advances in OMICs approaches aid in disentangling the molecular underpinnings of these interactions by providing mechanistic insights and potential candidate gene targets for crop improvement and stress resilience. In the post-genomic era, integrating artificial intelligence and big data analysis to optimize microbiome-based strategies for sustainable agriculture is a new frontier for disentangling plant-microbe symbiosis to improve soil health, enhance crop yields, and improve stress tolerance. Thus, by integrating the ecological, physiological, and molecular perspectives, this review highlights the transformative potential of harnessing plant-microbe symbiosis for climate-resilient and sustainable agriculture.

Stress, Physiological↗

PubMed2003

[Techniques and current practice of urodynamics. Problems and traps].

Development of urodynamics particularly during the past decade are highlighted, as well as a number of issues to be faced in the near future: research into the etiopathogenesis of different conditions, finding more sensitive and specific diagnostic procedures to overcome the current ones, establishing more comprehensive indications for urodynamics examinations and, as a result of all the above achieving greater improvement of certain surgical procedures. Both the technique and interpretation of the different urodynamic examinations, as well as the more recent innovations, implementation issues and controversies are detailed to a highly up-to-date level. Neuromodulation and ambulatory urodynamics deserve thoughtful consideration in this paper. Finally, attention is given to the controversies and future challenges such as urodynamic research providing accurate diagnosis of lower urinary tract obstruction in women, establishing the indication of urodynamic studies in women with urinary exertional incontinence, outlining the indications of neuromodulation and ambulatory urodynamics, applicability of artificial intelligence systems, improvement of artificial sphincter materials, tissue growth for bladder enlargement and actual prevention of myelodysplasia.

Adult↗

PubMed2026

Biallelic loss of RB1 in hepatocellular carcinoma as synthetic lethal target for artificial intelligence-guided therapy.

The retinoblastoma (RB1) gene is a critical tumor suppressor that regulates cell cycle progression and genomic stability. Although RB1 alterations have been reported in hepatocellular carcinoma (HCC), the biological and clinical consequences of biallelic RB1 inactivation (RB1-Bi) remain poorly defined. We performed a comprehensive allele-specific genomic analysis of HCC patients from the TCGA-LIHC (n = 355) and in-house AMC (n = 206) cohorts, collectively comprising the AMC-TCGA discovery cohort. In this combined cohort, RB1-Bi was identified in 14.6% of tumors, was enriched in poorly differentiated HCCs and was independently associated with significantly reduced overall survival (adjusted hazard ratio 3.32, 95% CI 1.93-5.72, p < 0.001). Additionally, a deep learning-based histopathology model using hematoxylin and eosin-stained slides (i.e., FR-MIL model) accurately predicted RB1-Bi status (F1 score 84.39% [95% CI, ±0.02]), making it readily identifiable in routine clinical practice. The prevalence and prognostic impact of RB1-Bi, as well as FR-MIL model performance, were consistent across independent validation cohorts, including advanced-stage tumors and external institutions. High-throughput drug screening in isogenic HCC models revealed that RB1-Bi HCC cells were particularly sensitive to inhibitors targeting mitotic regulators (e.g., AURKA, PLK1, KSP) and DNA damage response pathways (e.g., PARP inhibitors). Synthetic lethal interactions between RB1-Bi and these compounds were demonstrated in vitro and in vivo, and combination treatment with mitotic and PARP inhibitors had synergistic effects with acceptable tolerability. We conclude that RB1-Bi represents a clinically actionable biomarker that identifies a high-risk HCC subtype with specific therapeutic vulnerabilities, offering new opportunities for precision medicine.

Humans↗

PubMedDate not supplied

A brief overview and introduction to artificial neural networks.

This article is designed to acquaint professionals working in the field of substance use intervention with a range of artificial intelligence nonlinear, powerful tools, artificial neural networks, concepts, and paradigms. The family of ANNs, when appropriately selected and used, permits the maximization of what can be derived from available data as well as our studying and understanding the many people, processes, and phenomena which comprise substance use and its intervention. The latter represent complex, dynamic, multidimensional phenomena which are unpredictable and uncontrollable in the traditional "cause and effect" sense. As such they are likely to be nonlinear in their very essence. Using linear-based paradigms for planned intervention with nonlinear phenomena brooks the all-too-common possibility of using inappropriate intervention paradigms and/or drawing misleading conclusions about what is and/or has happened.

Alcoholism↗