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PubMed2026

CAUSAL artificial intelligence and data-driven decision intelligence in personalized medicine: a review of healthcare informatics systems.

This review examines the integration of causal artificial intelligence (AI) and data-driven decision intelligence within healthcare informatics systems to advance personalized medicine and clinical decision-making. A narrative review methodology was employed, synthesizing interdisciplinary literature from major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Studies focusing on causal inference, decision intelligence, and healthcare informatics applications in personalized medicine were included. Data were extracted on methodological approaches, healthcare settings, analytical techniques, and clinical applications, followed by thematic synthesis. Findings indicate that causal AI enhances clinical decision support by enabling estimation of treatment effects and simulation of intervention outcomes at the individual patient level. Integration of multimodal health data such as electronic health records, genomic data, and real-time monitoring improves prediction accuracy and supports tailored treatment strategies. Additionally, causal models improve interpretability, fostering clinician trust and facilitating transparent decision-making. Robust healthcare informatics infrastructures, including interoperable systems and data warehouses, were identified as critical enablers of causal analytics. Overall, causal AI represents a transformative advancement in healthcare analytics, supporting more informed, individualized, and evidence-based clinical decisions. Its integration within healthcare informatics systems has significant potential to improve patient outcomes and guide the future of intelligent, personalized healthcare delivery.

Precision Medicine↗
PubMed1992

Natural language processing in psychiatry. Artificial intelligence technology and psychopathology.

The potential benefit of artificial intelligence (AI) technology as a tool of psychiatry has not been well defined. In this essay, the technology of natural language processing and its position with regard to the two main schools of AI is clearly outlined. Past experiments utilizing AI techniques in understanding psychopathology are reviewed. Natural language processing can automate the analysis of transcripts and can be used in modeling theories of language comprehension. In these ways, it can serve as a tool in testing psychological theories of psychopathology and can be used as an effective tool in empirical research on verbal behavior in psychopathology.

Computer Simulation↗
PubMed1997

Data mining: sophisticated forms of managed care modeling through artificial intelligence.

Data mining is a recent development in computer science that combines artificial intelligence algorithms and relational databases to discover patterns automatically, without the use of traditional statistical methods. Work with data mining tools in health care is in a developmental stage that holds great promise, given the combination of demographic and diagnostic information.

Algorithms↗
PubMed1989

Predicate calculus, artificial intelligence, and workers' compensation.

Application of principles of predicate calculus (PC) and artificial intelligence (AI) search methods to occupational medicine can meet several goals. First, they can improve understanding of the diagnostic process and recognition of the sources of uncertainty in knowledge and in case specific information. Second, PC provides a rational means of resolving differences in conclusion based upon the same premises. Third, understanding of these principles allows separation of knowledge (facts) from the process by which they are used and therefore facilitates development of AI-based expert systems. Application of PC to recognizing causation of pulmonary fibrosis is demonstrated in this paper, providing a method that can be generalized to other problems in occupational medicine. Application of PC and understanding of AI search routines may be particularly applicable to workers' compensation where explicit statement of rational and inferential process is necessary. This approach is useful in the diagnosis of occupational lung disease and may be particularly valuable in workers' compensation considerations, wherein explicit statement of rationale is needed.

Artificial Intelligence↗
PubMed2026

Artificial Intelligence and Machine Learning Applications in Fibromuscular Dysplasia: Transforming Diagnosis, Risk Stratification, and Clinical Decision-Making.

Fibromuscular dysplasia (FMD) is a non-atherosclerotic vascular disorder with heterogeneous presentations, making diagnosis and management highly dependent on imaging and clinical expertise. This narrative review examines how artificial intelligence (AI) and machine learning (ML) are transforming FMD care. AI-enhanced imaging, particularly convolutional neural network-based analysis, improves detection of the characteristic "string-of-beads" pattern on CT angiography, magnetic resonance angiography, and ultrasound, although FMD-specific validation remains limited. ML models facilitate risk stratification, prediction of disease progression, and early identification of complications such as aneurysms and stroke by integrating clinical, imaging, and genomic data. AI-driven clinical decision support systems further enable personalized treatment selection through pharmacogenomic insights and robot-assisted interventions. Despite promising real-world applications, challenges persist, including limited large-scale datasets, workflow integration, regulatory barriers, and algorithmic bias affecting underrepresented populations. Future advances in explainable AI, federated learning, and digital health integration may enable a shift toward predictive, patient-centered FMD management.

Humans↗
PubMed1993

Molecular scene analysis: the integration of direct-methods and artificial-intelligence strategies for solving protein crystal structure.

A knowledge-based approach to crystal structure determination is presented. The approach integrates direct-methods and artificial-intelligence strategies to rephrase the structure determination process as an exercise in scene analysis. A general joint probability distribution framework, which allows the incorporation of isomorphous replacement, anomalous scattering and a priori structural information, forms the basis of the direct-methods strategies. The accumulated knowledge on crystal and molecular structures is exploited through the use of artificial-intelligence strategies, which include techniques of knowledge representation, search and machine learning.

Journal Article↗
PubMed2003

An advanced artificial intelligence tool for menu design.

The computer-assisted menu design still remains a difficult task. Usually knowledge that aids in menu design by a computer is hard-coded and because of that a computerised menu planner cannot handle the menu design problem for an unanticipated client. To address this problem we developed a menu design tool, MIKAS (menu construction using incremental knowledge acquisition system), an artificial intelligence system that allows the incremental development of a knowledge-base for menu design. We allow an incremental knowledge acquisition process in which the expert is only required to provide hints to the system in the context of actual problem instances during menu design using menus stored in a so-called Case Base. Our system incorporates Case-Based Reasoning (CBR), an Artificial Intelligence (AI) technique developed to mimic human problem solving behaviour. Ripple Down Rules (RDR) are a proven technique for the acquisition of classification knowledge from expert directly while they are using the system, which complement CBR in a very fruitful way. This combination allows the incremental improvement of the menu design system while it is already in routine use. We believe MIKAS allows better dietary practice by leveraging a dietitian's skills and expertise. As such MIKAS has the potential to be helpful for any institution where dietary advice is practised.

Artificial Intelligence↗
PubMed2006

How artificial intelligence tools can be used to assess individual patient risk in cardiovascular disease: problems with the current methods.

BACKGROUND: In recent years a number of algorithms for cardiovascular risk assessment has been proposed to the medical community. These algorithms consider a number of variables and express their results as the percentage risk of developing a major fatal or non-fatal cardiovascular event in the following 10 to 20 years DISCUSSION: The author has identified three major pitfalls of these algorithms, linked to the limitation of the classical statistical approach in dealing with this kind of non linear and complex information. The pitfalls are the inability to capture the disease complexity, the inability to capture process dynamics, and the wide confidence interval of individual risk assessment. Artificial Intelligence tools can provide potential advantage in trying to overcome these limitations. The theoretical background and some application examples related to artificial neural networks and fuzzy logic have been reviewed and discussed. SUMMARY: The use of predictive algorithms to assess individual absolute risk of cardiovascular future events is currently hampered by methodological and mathematical flaws. The use of newer approaches, such as fuzzy logic and artificial neural networks, linked to artificial intelligence, seems to better address both the challenge of increasing complexity resulting from a correlation between predisposing factors, data on the occurrence of cardiovascular events, and the prediction of future events on an individual level.

Artificial Intelligence↗
PubMed1989

Use of artificial intelligence for the preoperative diagnosis of pulmonary lesions.

The relatively new field of artificial intelligence has spawned a variety of techniques associated with computer-assisted diagnosis. These techniques have been applied to the diagnosis of pulmonary lesions, but previous reports have focused on medical rather than surgical populations and the results have been evaluated using only retrospective patient surveys. We used a Bayesian algorithm to develop a diagnostic computer model for prospectively evaluating patients undergoing thoracotomy for suspected pulmonary malignancy. Patients who had a preoperative diagnosis were not included. Preoperative clinical and radiographic parameters for 100 consecutive patients were prospectively entered into the diagnostic model, which then categorized the lesion as benign or malignant. The computer predictions agreed with the final histological diagnosis in 95 of the 100 patients. The sensitivity was 96% and the specificity was 89% for this prospective series. These results indicate that the computer-assisted diagnosis of pulmonary lesions may have a role in this clinical setting.

Adult↗
PubMed1998

EMG pattern recognition based on artificial intelligence techniques.

This paper presents an electromyographic (EMG) pattern recognition method to identify motion commands for the control of a prosthetic arm by evidence accumulation based on artificial intelligence with multiple parameters. The integral absolute value, variance, autoregressive (AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters from several time segments of EMG signals. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for EMG pattern recognition.

Arm↗
PubMed1995

The use of artificial intelligence in the analysis of sports performance: a review of applications in human gait analysis and future directions for sports biomechanics.

Computers have played an important supporting role in the development of experimental and theoretical sports biomechanics. The role of the computer now extends from data capture and data processing through to mathematical and statistical modelling and simulation and optimization. This paper seeks to demonstrate that elevation of the role of the computer to involvement in the decision-making process, through the use of artificial intelligence techniques, would be a potentially rewarding future direction for the discipline. In the absence of significant previous work in this area, this paper reviews experiences in a parallel field of medical informatics, namely gait analysis. Research into the application of expert systems and neural networks to gait analysis is reviewed, observations made and comparisons drawn with the biomechanical analysis of sports performance. Brief explanations of the artificial intelligence techniques discussed in the paper are provided. The paper concludes that the creation of an expert system for a specific well-defined sports technique would represent a significant advance in the development of sports biomechanics.

Artificial Intelligence↗
PubMed1991

Mental, neuropsychic, and brain patterns of defense. Neuropsychic defense continua from psychopathology to the particularly human parallel networks that are problematic for artificial intelligence.

The synthesis of psychodynamic and neuroscientific data may be advanced by the study of similarly configured specific patterns of defense along continua from the normal and neurotic mental mechanisms of defenses, through what might be termed neuropsychiatric defenses influenced by the neurological state, to the more clearly neurological cortical reactions. These similar patterns and continua are the particularly human way brain adds its flavor of organism to mind, and the way in which all the psychodynamic mechanisms are specifically imbedded in brain. A purely psychological psychodynamics lacking this medical perspective fails to model human mental processes and reflects problems with artificial intelligence models that do not model brain. The pathological reactions that we address in psychiatry and neuropsychiatry interfere with and thereby reveal parallel processing features in (1) analogical and metaphorical thinking; (2) reduplication, redundancy, and repetitiveness; (3) self, person, and environmental recognition, including transference processes; (4) approximation and statistical inference; (5) spatiality and motor control; (6) affectivity, perception of sensations and sexuality; (7) projective mechanisms; and (8) problem solving by optimization, vectorial summation, and the mutual interaction of interconnected agencies. The new concepts in artificial intelligence built upon brainlike structures called neural nets portend great economies, emergent natural features that are more like human processes, and hierarchical and cyclic organizational demands. Medical psychoanalysts who comprehend dynamic and brain mechanisms and can describe them in terms that refer to both domains and their interaction should find increasing theoretical and practical convergence of their work with the emerging study and modeling of these processes by new forms of computers.

Adult↗
PubMed1989

Quantitative microscopy and artificial intelligence: some philosophical reflections.

The interdisciplinary field of quantitative microscopy (computer-aided microscopy) and artificial image understanding systems is explored, with an emphasis on the philosophical aspects of pathology and artificial intelligence. Three methodological problems of traditional diagnostic pathology are identified: those of validity, variability and organisation. Quantitative microscopy is a potential research strategy for solving these problems. In practice, however, the quantitative microscopy program is handicapped by the difficulty of building artificial image-understanding systems. We discuss the segmentation problem in image understanding, and four general strategies, three cognitivistic and one connectionistic, are reviewed.

Artificial Intelligence↗
PubMed2001

The socio-organizational age of artificial intelligence in medicine.

The increasing pressure on Health Care Organizations (HCOs) to ensure efficiency and cost-effectiveness, balancing quality of care and cost containment, will drive them towards a more effective management of medical knowledge derived from research findings. The relation between science and health services has until recently been too casual. The primary job of medical research has been to understand the mechanisms of disease and produce new treatments, not to worry about the effectiveness of the new treatments or their implementation. As a result many new treatments have taken years to become part of routine practice, ineffective treatments have been widely used, and medicine has been opinion rather than evidence based. This results in suboptimal care for patients. Knowledge management technology may provide effective approaches in speeding up the diffusion of innovative medical procedures whose clinical effectiveness have been proved: the most interesting one is represented by computer-based utilization of evidence-based clinical guidelines. As researchers in Artificial Intelligence in Medicine (AIM), we are committed to foster the strategic transition from opinion to evidence-based decision making. Reviews of the effectiveness of various methods of guideline dissemination show that the most predictable impact is achieved when the guideline is made accessible through computer-based and patient specific reminders that are integrated into the clinician's workflow. However, the traditional single doctor-patient relationship is being replaced by one in which the patient is managed by a team of health care professionals, each specializing in one aspect of care. Such shared care depends critically on the ability to share patient-specific information and medical knowledge easily among them. Strategically there is a need to take a more clinical process view of health care delivery and to identify the appropriate organizational and information infrastructures to support this process. Thus, the great challenge for AIM researchers is to exploit the astonishing capabilities of new technologies to disseminate their tools to benefit HCOs by assuring the conditions of knowledge management and organizational learning at the fullest extent possible. To achieve such a strategic goal, a guideline can be viewed as a model of the care process. It must be combined with an organization model of the specific HCO to build patient careflow management systems. Artificial intelligence can be extensively used to design innovative tools to support all the development stages of those systems. However, exploiting the knowledge represented in a guideline to build them requires to extend today's workflow technology by solving some challenging problems.

Artificial Intelligence↗
PubMed1986

Artificial intelligence and psychiatry.

This paper provides a brief historical introduction to the new field of artificial intelligence and describes some applications to psychiatry. It focuses on two successful programs: a model of paranoid processes and an expert system for the pharmacological management of depressive disorders. Finally, it reviews evidence in favor of computerized psychotherapy and offers speculations on the future development of research in this area.

Artificial Intelligence↗