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At least 397 records · Page 22Linked to original sources
PubMed2026

Recent advances in immunotherapy for breast cancer: An updated review.

Immunotherapy has revolutionized the treatment landscape of breast cancer, particularly for triple-negative breast cancer (TNBC), yet primary and acquired resistance remain formidable obstacles limiting durable clinical benefit. This review provides a comprehensive update on recent advances in breast cancer immunotherapy, with a focused emphasis on the molecular and cellular mechanisms driving treatment resistance and emerging strategies to overcome them. We dissect tumor-intrinsic resistance pathways, including loss of tumor antigens, defects in antigen processing and presentation machinery, insensitivity to interferon-γ signaling, metabolic reprogramming, and epigenetic dysregulation. Tumor-extrinsic mechanisms, such as infiltration of immunosuppressive cells, abnormal angiogenesis, extracellular matrix remodeling, and FGF/FGFR genomic amplification, are highlighted as key barriers to effective immune checkpoint blockade. Emerging evidence implicates novel resistance mediators, including the DUSP22-LGALS1 axis, THSD4-driven T cell exclusion, and the MTDH-SND1 complex impairing antigen presentation, etc. We critically evaluate current strategies to surmount resistance, encompassing combination regimens with chemotherapy, targeted therapies, radiotherapy, and novel immunomodulators. The review also addresses challenges in managing immune-related adverse events, controversies surrounding patient selection biomarkers, and the urgent need for optimized efficacy evaluation systems beyond RECIST criteria. Finally, we discuss future directions, including novel immune checkpoints, microbiome modulation, artificial intelligence-assisted decision-making, and innovative trial designs. By integrating mechanistic insights with clinical evidence, this review provides a framework for understanding and overcoming immunotherapy resistance, advancing the paradigm from "effective" to "precise" immuno-oncology in breast cancer.

Humans↗
PubMed1984

Management of the unanticipated neural tube defect in late pregnancy.

The physician who encounters a patient in late pregnancy with a neural tube defect is presented with a difficult task of management and treatment. A thorough examination of the defect(s) is mandatory. This can be accomplished best with ultrasound examination. It is then imperative to assess the severity and survivability of the fetus, and to counsel properly the patient as to diagnosis, severity, and options available. After discussion with the proper consultants, patients should have considerable input into the management, and should be informed adequately to make an intelligent and proper decision. After careful assessment and thorough consultation and counseling with the patient and her support person, therapy can be initiated, followed by some type of grief counseling with appropriate follow-up. Patients also should be informed as to subsequent risks and what, if anything, could possibly be done to prevent recurrences. By this careful and thorough approach it is hoped that physicians who encounter neural tube defects late in pregnancy will make a considerable difference in the physical as well as the emotional health of the patient.

Counseling↗
PubMed1992

Hemodynamic and oxygen transport patterns for outcome prediction, therapeutic goals, and clinical algorithms to improve outcome. Feasibility of artificial intelligence to customize algorithms.

A generalized decision tree or clinical algorithm for treatment of high-risk elective surgical patients was developed from a physiologic model based on empirical data. First, a large data bank was used to do the following: (1) describe temporal hemodynamic and oxygen transport patterns that interrelate cardiac, pulmonary, and tissue perfusion functions in survivors and nonsurvivors; (2) define optimal therapeutic goals based on the supranormal oxygen transport values of high-risk postoperative survivors; (3) compare the relative effectiveness of alternative therapies in a wide variety of clinical and physiologic conditions; and (4) to develop criteria for titration of therapy to the endpoints of the supranormal optimal goals using cardiac index (CI), oxygen delivery (DO2), and oxygen consumption (VO2) as proxy outcome measures. Second, a general purpose algorithm was generated from these data and tested in preoperatively randomized clinical trials of high-risk surgical patients. Improved outcome was demonstrated with this generalized algorithm. The concept that the supranormal values represent compensations that have survival value has been corroborated by several other groups. We now propose a unique approach to refine the generalized algorithm to develop customized algorithms and individualized decision analysis for each patient's unique problems. The present article describes a preliminary evaluation of the feasibility of artificial intelligence techniques to accomplish individualized algorithms that may further improve patient care and outcome.

Algorithms↗
PubMedDate not supplied

A microcomputer teaching and decision-support system for emergency medicine: use of hypermedia and artificial intelligence in combination.

Hospital emergency units are submitted to a continuous intensive and polyvalent practice of medicine. In addition to the few experienced physicians, the medical staff is often made up of young and unskilled students and residents. The ability to reach at any time a wide and flexible knowledge is of the utmost importance to improve the quality of care given to patients and to perfect bedside teaching. The purpose of this work was to present a computerized system, a kind of shell, using, in combination, artificial intelligence and hypertext/hypermedia tools. A modular architecture is presented integrating two entities: an illustrated encyclopedic hypertext network and several expert modules based on production rules concerning well-limited fields of medicine (basic clinical problem-solving, metabolic and acid-base disorders). An interface using the World Wide Web (WWW) will soon be proposed.

Artificial Intelligence↗
PubMed2006

Balance marks cognitive changes in old age because it reflects global brain atrophy and cerebro-arterial blood-flow.

In healthy old age biomarkers such as Balance robustly correlate with measures of mental abilities such as scores on tests of intelligence, reaction times and memory. A plausible explanation is that balance reflects general physiological fitness and so also neurophysiological integrity, but direct evidence is lacking. Brain scans measured age-associated loss of brain volume and cerebro-arterial blood flow (CBf) in 69 volunteers aged from 62 to 81 years who also took the Tinetti Balance test battery, 3 tests of fluid intelligence, 3 tests of decision speed and a memory test. Balance, but not atrophy or CBf, predicted intelligence test scores. Balance, atrophy, and CBf all independently predicted speed and memory scores but, after variance in atrophy and CBf had been considered, predictions from Balance were no longer significant. It appears that in these tests Balance marks cognitive performance in old age because it reflects gross age-related neurophysiological changes.

Aged↗
PubMed2001

Medical decision support via the internet: PROforma and Solo.

This paper describes the development and application of an integrated technology to support the authoring of intelligent medical knowledge services, such as decision and guideline support, and disseminate them over the Internet. Decision support and guideline enactment are provided by the PROforma technology which is described in detail elsewhere. Solo is a communications infrastructure that supports the delivery of PROforma services over the Internet using a web browser. The PROforma-Solo technology brings together techniques from knowledge engineering and artificial intelligence with software engineering and the Internet to flexibly support decision making and patient management at the point of care. A variety of clinical applications have been implemented and are briefly described.

Artificial Intelligence↗
PubMed2006

Overview: Computational analysis and decision support systems in oncology.

Computational analysis tools and decision support systems have increased their penetration in the support of clinical processes and management of medical data and knowledge. Applications range from adjunct tools for diagnosis and disease investigation to the treatment and monitoring of therapeutic procedures. As all medical fields, the field of oncology is affected. This special issue includes studies presenting research and applications of computational intelligence in oncology, covering four main areas: i) decision support systems (DSS) and artificial intelligence (AI) applications in oncology; ii) design and assessment of classification tools in oncology; iii) intelligent accessing, retrieving, and storing of medical images; and iv) intelligent telemedicine and telehealth applications in oncology.

Artificial Intelligence↗
PubMed1997

Clinical-HINTS: integrated intelligent ICU patient monitoring and information management system.

Clinical-HINTS (Health Intelligence System) is a horizontally integrated decision support system (DSS) designed to meet the requirements for intelligent real-time clinical information management in critical care medical environments and to lay the foundation for the development of the next generation of intelligent medical instrumentation. The system presented was developed to refine and complement the information yielded by clinical laboratory investigations, thereby benefiting the management of the intensive care unit (ICU) patient. More specifically, Clinical-HINTS was developed to provide computer-based assistance with the acquisition, organisation and display, storage and retrieval, communication and generation of real-time patient-specific clinical information in an ICU. Clinical-HINTS is an object-oriented system developed in C+2 to run under Microsoft Windows as an embryo intelligent agent. Current generic reasoning skills include perception and reactive cognition of patient status but exclude therapeutic action. The system monitors the patient by communicating with the available sources of data and uses generic reasoning skills to generate intelligent alarms, or HINTS, on various levels of interpretation of an observed dysfunction, even in the presence of complex disorders. The system's communication and information management capabilities are used to acquire physiological data, and to store them along with their interpretations and any interventions for the dynamic recognition of interrelated pathophysiological states or clinical events.

Artificial Intelligence↗
PubMed2005

Developing decision support for dialysis treatment of chronic kidney failure.

The complexity, variability, quantitative nature, and data density of treatment for chronic kidney failure make dialysis information systems excellent candidates for computerized decision support. We describe the development of an intelligent system building on existing knowledge and susceptible to reconfiguration on the basis of knowledge acquired during the use of the system. Various decision support techniques were used to design and develop the decision support modules. This paper briefly reviews the literature on clinical decision support, discusses some of the problems faced by practitioners in managing chronic kidney failure (end-stage renal disease) patients, and sets forth the decision support techniques used in developing a dialysis decision support system.

Anemia↗
PubMedDate not supplied

Intuition in critical care nursing practice.

Research that addresses intuition as experienced by nurses in critical care settings is rare; however, evidence to support the usefulness of intuition in making complex clinical decisions is mounting. The research reported here suggests that intuition is not a second-rate substitute for intelligent and rational decision-making; rather, it is a legitimate adjunct to empirical observation and linear analysis.

Adult↗
PubMed2025

Integrating expert knowledge into large language models improves performance for psychiatric reasoning and diagnosis.

BACKGROUND AND METHODS: The authors sought to evaluate the performance of common large language models (LLMs) in psychiatric diagnosis, and the impact of integrating expert-derived reasoning on their performance. Clinical case vignettes and associated diagnoses were retrieved from the DSM-5-TR Clinical Cases book. Diagnostic decision trees were retrieved from the DSM-5-TR Handbook of Differential Diagnosis and refined for LLM use. Three LLMs were prompted to provide diagnosis candidates for the vignettes either by directly prompting or using the decision trees. These candidates and diagnostic categories were compared against the correct diagnoses. The positive predictive value (PPV), sensitivity, and F1 statistic were used to measure performance. RESULTS: When directly prompted to predict diagnoses, the best LLM by F1 statistic (gpt-4o) had sensitivity of 76.7 % and PPV of 40.4 %. When making use of the refined decision trees, PPV was significantly increased (65.3 %) without a significant reduction in sensitivity (70.9 %). Across all experiments, the use of the decision trees statistically significantly increased the PPV, significantly increased the F1 statistic in 5/6 experiments, and significantly reduced sensitivity in 4/6 experiments. DISCUSSION: When used to predict psychiatric diagnoses from case vignettes, direct prompting of the LLMs yielded most true positive diagnoses but had significant overdiagnosis. Integrating expert-derived reasoning into the process using decision trees improved LLM performance (as measured by F1 statistic), primarily by suppressing overdiagnosis with a lower-magnitude negative impact on sensitivity. This suggests that the integration of clinical expert-derived reasoning could improve the performance of LLM-based tools in the behavioral health setting.

Humans↗
PubMed2003

Decision making with the "adaptive toolbox": influence of environmental structure, intelligence, and working memory load.

In 2 experiments with a total of 220 participants, the tendency to use simple heuristics such as the take the best heuristic in an adaptive manner was investigated. In a simulated stock market paradigm, the payoff structure of environments was varied, favoring either compensatory or noncompensatory decision strategies in terms of expected long-term payoff. In both experiments, the majority of participants were classified as using strategies that were adequate for the environment, supporting the notion of adaptive strategy selection. These strategy shifts were moderated by intelligence, as measured with common tests. Neither an additional learning phase (Experiment 1) nor working memory load or working memory capacity (Experiment 2) had additional effects on strategy selection.

Adult↗
PubMed2001

Computerized radiographic mass detection--part II: Decision support by featured database visualization and modular neural networks.

Based on the enhanced segmentation of suspicious mass areas, further development of computer-assisted mass detection may be decomposed into three distinctive machine learning tasks: 1) construction of the featured knowledge database; 2) mapping of the classified and/or unclassified data points in the database; and 3) development of an intelligent user interface. A decision support system may then be constructed as a complementary machine observer that should enhance the radiologists performance in mass detection. We adopt a mathematical feature extraction procedure to construct the featured knowledge database from all the suspicious mass sites localized by the enhanced segmentation. The optimal mapping of the data points is then obtained by learning the generalized normal mixtures and decision boundaries, where a is developed to carry out both soft and hard clustering. A visual explanation of the decision making is further invented as a decision support, based on an interactive visualization hierarchy through the probabilistic principal component projections of the knowledge database and the localized optimal displays of the retrieved raw data. A prototype system is developed and pilot tested to demonstrate the applicability of this framework to mammographic mass detection.

Artificial Intelligence↗