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A theoretical approach to artificial intelligence systems in medicine.

The various theoretical models of disease, the nosology which is accepted by the medical community and the prevalent logic of diagnosis determine both the medical approach as well as the development of the relevant technology including the structure and function of the A.I. systems involved. A.I. systems in medicine, in addition to the specific parameters which enable them to reach a diagnostic and/or therapeutic proposal, entail implicitly theoretical assumptions and socio-cultural attitudes which prejudice the orientation and the final outcome of the procedure. The various models -causal, probabilistic, case-based etc. -are critically examined and their ethical and methodological limitations are brought to light. The lack of a self-consistent theoretical framework in medicine, the multi-faceted character of the human organism as well as the non-explicit nature of the theoretical assumptions involved in A.I. systems restrict them to the role of decision supporting "instruments" rather than regarding them as decision making "devices". This supporting role and, especially, the important function which A.I. systems should have in the structure, the methods and the content of medical education underscore the need of further research in the theoretical aspects and the actual development of such systems.

Artificial Intelligence↗

Cognitive ability patterns and nurses' clinical decision making.

The present study concerns the clinical decision-making process in a naturalistic context and its relationship to the nurses' abilities of critical thinking, creative thinking and emotional intelligence. Apart from describing a model of the clinical decision-making process in nursing, the results showed its continuous and interactive nature as well as that different patterns of decisions correspond to distinct patterns of those cognitive abilities. Despite some limitations, we consider that this study has implications in the caring, administration, education and nursing informatics areas.

Decision Support Systems, Clinical↗

Cognitive abilities and clinical decision making in nursing.

This investigation focused the clinical decision-making process in a naturalistic context and its relationship to the nurses' abilities of critical thinking, creative thinking and emotional intelligence. Apart from describing a model of the clinical decision-making process in nursing, the results showed its continuous and interactive nature as well as that different patterns of decisions correspond to distinct patterns of those cognitive abilities. Despite some limitations, we consider that this study has implications in the caring, administration, education and nursing informatics areas.

Cognition↗

Effectiveness and usability of artificial intelligence-powered assistive technologies in Supporting daily activities of children with cerebral palsy: a systematic review.

BACKGROUND: Cerebral Palsy (CP) is the main cause of motor disabilities in childhood, necessitating innovative approaches to rehabilitation and assistive technology (AT). Simultaneously, artificial intelligence (AI) is increasingly being integrated into devices to create more adaptive, personalized, and effective AT. This systematic review aimed to evaluate the effectiveness and usability of AI-powered assistive technologies designed to support daily activities and rehabilitation in children with CP. MATERIALS AND METHODS: Five databases, including Scopus, Web of Science, PubMed, Embase, and IEEE Xplore, were systematically searched, and 23 articles were included in the final analysis. Articles were identified, selected, and categorized into emerging thematic areas based on the primary function and application of the technology. RESULTS: Five key thematic topics were identified: 1) AI-driven motor rehabilitation and gait training for functional mobility; 2) intelligent assessment and monitoring systems for clinical decision support; 3) AI-supported communication, social interaction, and intention recognition tools; 4) gamified and virtual reality-based interventions to enhance engagement and usability; and 5) smart assistive systems supporting daily living and independent mobility. The findings demonstrate a strong trend toward the application of AI technologies in personalized, engaging, and data-driven interventions for children with CP. However, the field is predominantly in the proof-of-concept stage, with limitations including small sample sizes, lack of long-term clinical validation, challenges in user-centered design, and usability for children with CP. CONCLUSION: AI-powered assistive technologies hold significant potential for transforming the care of children with CP by enabling highly personalized and engaging interventions. To actualize this potential, future work must realize that practical application remains challenging owing to limited clinical validation, technological integration, and usability barriers for children with CP. Future research must prioritize user-centered design and multidisciplinary collaboration to ensure that AI and robotic advancements improve the usability and quality of life for children with CP.

Humans↗

Neural network modeling for surgical decisions on traumatic brain injury patients.

Computerized medical decision support systems have been a major research topic in recent years. Intelligent computer programs were implemented to aid physicians and other medical professionals in making difficult medical decisions. This report compares three different mathematical models for building a traumatic brain injury (TBI) medical decision support system (MDSS). These models were developed based on a large TBI patient database. This MDSS accepts a set of patient data such as the types of skull fracture, Glasgow Coma Scale (GCS), episode of convulsion and return the chance that a neurosurgeon would recommend an open-skull surgery for this patient. The three mathematical models described in this report including a logistic regression model, a multi-layer perceptron (MLP) neural network and a radial-basis-function (RBF) neural network. From the 12,640 patients selected from the database. A randomly drawn 9480 cases were used as the training group to develop/train our models. The other 3160 cases were in the validation group which we used to evaluate the performance of these models. We used sensitivity, specificity, areas under receiver-operating characteristics (ROC) curve and calibration curves as the indicator of how accurate these models are in predicting a neurosurgeon's decision on open-skull surgery. The results showed that, assuming equal importance of sensitivity and specificity, the logistic regression model had a (sensitivity, specificity) of (73%, 68%), compared to (80%, 80%) from the RBF model and (88%, 80%) from the MLP model. The resultant areas under ROC curve for logistic regression, RBF and MLP neural networks are 0.761, 0.880 and 0.897, respectively (P < 0.05). Among these models, the logistic regression has noticeably poorer calibration. This study demonstrated the feasibility of applying neural networks as the mechanism for TBI decision support systems based on clinical databases. The results also suggest that neural networks may be a better solution for complex, non-linear medical decision support systems than conventional statistical techniques such as logistic regression.

Adolescent↗

Should dentistry be part of the National Health Information Infrastructure?

BACKGROUND: The National Health Information Infrastructure, or NHII, proposes to improve the effectiveness, efficiency and overall quality of health in the United States by establishing a national, electronic information network for health care. To date, dentistry's integration into this network has not been discussed widely. METHODS: The author reviews the NHII and its goals and structure through published reports and background literature. The author evaluates the advantages and disadvantages of the NHII regarding their implications for the dental care system. RESULTS: The NHII proposes to implement computer-based patient records, or CPRs, for most Americans by 2014, connect personal health information with other clinical and public health information, and enable different types of care providers to access CPRs. Advantages of the NHII include transparency of health information across health care providers, potentially increased involvement of patients in their care, better clinical decision making through connecting patient-specific information with the best clinical evidence, increased efficiency, enhanced bioterrorism defense and potential cost savings. Challenges in the implementation of the NHII in dentistry include limited use of CPRs, required investments in information technology, limited availability and adoption of standards, and perceived threats to privacy and confidentiality. CONCLUSIONS: The implementation of the NHII is making rapid strides. Dentistry should become an active participant in the NHII and work to ensure that the needs of dental patients and the profession are met. Practice Implications. The NHII has far-reaching implications on dental practice by making it easier to access relevant patient information and by helping to improve clinical decision making.

Artificial Intelligence↗

AI in medical education--another grand challenge for medical informatics.

The potential benefits of artificial intelligence in medicine (AIM) were never realized as anticipated. This paper addresses ways in which such potential can be achieved. Recent discussions of this topic have proposed a stronger integration between AIM applications and health information systems, and emphasize computer guidelines to support the new health care paradigms of evidence-based medicine and cost-effectiveness. These proposals, however, promote the initial definition of AIM applications as being AI systems that can perform or aid in diagnoses. We challenge this traditional philosophy of AIM and propose a new approach aiming at empowering health care workers to become independent self-sufficient problem solvers and decision makers. Our philosophy is based on findings from a review of empirical research that examines the relationship between the health care personnel's level of knowledge and skills, their job satisfaction, and the quality of the health care they provide. This review supports addressing the quality of health care by empowering health care workers to reach their full potential. As an aid in this empowerment process we argue for reviving a long forgotten AIM research area, namely, AI based applications for medical education and training. There is a growing body of research in artificial intelligence in education that demonstrates that the use of artificial intelligence can enhance learning in numerous domains. By examining the strengths of these educational applications and the results from previous AIM research we derive a framework for empowering medical personnel and consequently raising the quality of health care through the use of advanced AI based technology.

Artificial Intelligence↗

Stanford-Binet, Fourth Edition and the WISC--R for children in the lower range of intelligence.

The Stanford-Binet IV and the WISC--R were administered to 30 children, ages 8 to 15 yr., whose scores were in the range of mild mental retardation. The mean interval between testings was 7 mo. The correlation was .83, with a median difference of 4 points. The WISC--R mean IQ was significantly lower than the Stanford-Binet-IV Composite mean score for the group. The disparity in scores points to the need to evaluate measures of intelligence together with other indices of functioning in decision-making for mildly retarded children.

Adolescent↗

Decision aids in radiology.

Computer systems can help radiologists decide which tests to perform and which diagnoses to consider. Repositories of clinical data and general medical knowledge can provide information on demand for decision-making tasks; many of these information resources are available remotely via the Internet. Decision support systems can incorporate the techniques of artificial intelligence to apply their general knowledge to the features of a particular patient. Successful use of these technologies requires careful attention to design, implementation, and rigorous evaluation. Computer-based decision aids can improve the cost effectiveness and diagnostic accuracy of radiologic practice and are poised to play an important role in the future of radiology.

Decision Support Techniques↗

Utterance-based proposed spot diagnostic system of vocal tract malfunction.

It is not surprising that speech recognition by machine, has received a great deal of attention through the techniques of artificial intelligence (AI), like expert systems to support decisions in various intended fields. One proposal that is based on the expert system paradigm is to diagnose a malfunction of the vocal tract during uttering recommended utterances for this purpose. The choice of these utterances is achieved according to the position and the manner of articulation. Four important features of acoustic analysis of speech are, fundamental frequency, F0, Formants, (F1-F5), amplitude, and the harmonic structure (tone vs. noise). The most Candidate features in the proposed diagnostic system are both fundamental frequency and/or the formants. These are considered to be the Core of the intended work. The throat, mouth and nose as the resonating champers will support this attitude and will affect, negatively, the range of the mentioned frequencies when they are out of the anatomical and/or physical functions. The discrete speech (isolated words) as the most recognizable utterances. Will be considered to put aside the difficulties of both connected and continuous word-based recognition. In a diagnostic systems, the generality is an essential issue, that is to consider "Speaker independent" recognizer which needs more efforts during the system training phase. The paper presents a rough (initial) spotting diagnostic system to be the base for a future detailed system for specific defects of precised organs belonging to the vocal tract. Arabic Vowels as well as some Consonants would be the target, taking into account the age range, that is (20-25) years-aged matures. Different recommended utterances, Arabic segmented alphabetic, focused on various points through the vocal apparatus. Speech related waveforms, as well as the associated fundamental frequencies and formants have been considered in the normal and the Corresponding abnormal Cases. The deviations that appeared in the frequency pattern have indicated the defected articulator that is dominant in the intended utterance production. The results illustrated, would be the base for designing a dedicated hardware unit, which may be reliable for the physicians interesting in this field. Human beings communicate with one another primarily by speech, and speech brings human beings closer together speech sounds travel through the air at the rate of about 330 meter per second, whereas impulses travel a long nerve pathways in the body at a rate of about 60 meter per second. The time it takes for a spoken word to be heard and understood by a listener may be shorter than the time it takes as a neural message to travel to the brain, [1]. Speech not only for human Communication, but it also has many applications in different fields. Some of these applications are machine control commands base systems, speech-to-text, and Text-to-speech systems, natural language-based systems, and medical diagnosis systems for vocal tract malfunction, the issue of this paper. One difficulty of speech based-systems is the fact that not everyone speaks the same way, even those who supposedly speak the same language at the same way. The term dialect is used to refer to this variability and is emphasized in case of uttering with different languages. The above discussion is concerning with the social and emotional variability which can be modified with reasonable efforts. The great variabilities, which are difficulty to be manipulated, are belonging to the inheritance and anatomical aspects. So it is worthy and to propose a methodology that can be used globally inspite of different social communities.

Adult↗

Neural basis of deciding, choosing and acting.

The ability and opportunity to make decisions and carry out effective actions in pursuit of goals is central to intelligent life. Recent research has provided significant new insights into how the brain arrives at decisions, makes choices, and produces and evaluates the consequences of actions. In fact, by monitoring or manipulating specific neurons, certain choices can now be predicted or manipulated.

Animals↗

Comparative validity of three Wechsler short forms for delinquents.

Investigated the validity of Vocabulary-Block Design short forms for the WISC-R, WAIS, and WAIS-R in a clinical population of 126 adjudicated male delinquents age 16 years. The difference between the means of each short form and its corresponding Full Scale IQ was small and nonsignificant. The correlations between each short form and corresponding Full Scale IQ ranged between .88 and .92 and therefore accounted for between 77% and 84% of the variance shared by the measures. The WAIS and WAIS-R were superior to the WISC-R in correctly classifying Ss by intelligence category. It was concluded that Wechsler short forms, especially the WAIS and WAIS-R, are useful as screening devices for older delinquents, but inadequate for making individual treatment program decisions.

Adolescent↗

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↗

Knowledge-based system for structured examination, diagnosis and therapy in treatment of traumatised teeth.

Dental trauma in children and adolescents is a common problem, and the prevalence of these injuries has increased in the last 10-20 years. A dental injury should always be considered an emergency and, thus, be treated immediately to relieve pain, facilitate reduction of displaced teeth, reconstruct lost hard tissue, and improve prognosis. Rational therapy depends upon a correct diagnosis, which can be achieved with the aid of various examination techniques. It must be understood that an incomplete examination can lead to inaccurate diagnosis and less successful treatment. Good knowledge of traumatology and models of treatments can also reduce stress and anxiety for both the patient and the dental team. Knowledge-based Systems (KBS) are a practical implementation of Artificial Intelligence. In complex domains which humans find difficult to understand, KBS can assist in making decisions and can also add knowledge. The aim of this paper is to describe the structure of a knowledge-based system for structured examination, diagnosis and therapy for traumatised primary and permanent teeth. A commercially available program was used as developmental tool for the programming (XpertRule, Attar, London, UK). The paper presents a model for a computerised decision support system for traumatology.

Adolescent↗

Stability of IQ measures in teenagers and young adults with developmental dyslexia.

A follow-up study was performed to investigate the stability of IQ measures in a group of dyslexic teenagers and young adults. Earlier research had shown contradictory results. The 65 subjects, 12 years old on the average at first test, were retested after a mean interval of six and a half years. There was a significant relative decrease in verbal IQ (VIQ), which was interpreted as either an effect of low reliability of tests used, or an effect of the dyslexic individuals' less experience with reading and writing, and as a consequence, a lag in verbal ability, the second interpretation being in line with earlier findings in groups of children with learning disabilities. Performance IQ improved significantly and the tentative interpretation was that of a compensatory process, in the sense that the dyslexic children might develop a more visual, intuitive and creative way to process information and solve problems. The conclusion was that caution should be taken, before making important decisions on the basis of a single intelligence test, and that dyslexic children might be at risk to lag behind their peers in terms of VIQ, especially if they are not provided with suitable special education.

Adolescent↗

Pharmacogenomic and drug interactions risk in cardio-oncology: A precision medicine perspective for India.

Cardio-oncology patients may face complex treatment regimens due to the concurrent existence of cancer and cardiovascular disease, leading to a considerable polypharmacy burden. This significantly increases the prospect of drug-drug interactions (DDIs) and gene-drug interactions. The majority of these interactions arise from comparable pharmacokinetic and pharmacological pathways associated with drug transporters and cytochrome P450 enzymes. The significance of pharmacogenomics in tailored treatment strategies are emphasised by the fact that genetic variability enhances individual differences in drug response, safety, and efficacy. This narrative review focus on the effects of key genetic polymorphisms (e.g., DPYD, CYP2C19, and CYP2C9) on the metabolism and efficacy of commonly prescribed anticancer and cardiovascular medications such as fluoropyrimidines, clopidogrel, and warfarin. In addition it explore the role of pharmacogenomic variants on drug-drug interactions within the field of cardio-oncology. The study ultimately emphasizes the necessity of precision medicine in India to address the genetic diversity and underrepresentation in global genomic databases. The absence of pharmacogenomic testing, infrastructural deficiencies, financial constraints, and insufficient clinical integration hinder the widespread use of this technology in India. The Genome India Project and other national initiatives establish the foundation for pharmacogenomic-guided therapy. Utilizing genetic data, together with artificial intelligence-based predictive tools, for clinical decision-making may enhance medication safety and yield optimal outcomes in Indian cardio-oncology patients.

Humans↗

Prediction of vascular tissue engineering results with artificial neural networks.

Tissue engineers are often confused on finding the most successful strategy for specific patient. In this study, we used artificial neural networks to predict the outcomes of different vascular tissue engineering strategies, thus providing advisory information for experimental designers. Over 30 variables were used as features of the tissue engineering strategies. Different architectures of artificial neural networks with back propagation algorithm were tested to obtain the best model configuration for the prediction of the tissue engineering strategies. In the computational experiments, the artificial neural networks with one and two hidden layers could, respectively, detect unsuccessful strategies with the highest predictive accuracy of 91.45 and 94.24%. In conclusion, artificial intelligence has great potential in tissue engineering decision support. It can provide accurate advisory information for tissue engineers, thus reducing failures and improving therapeutic effects.

Animals↗

Advance directives in patients with Alzheimer's disease. Ethical and clinical considerations.

Advance patient directives are various forms of anticipatory medical directives made by competent individuals for the eventuality of future incompetence. They are therefore appropriate instruments for competent patients in the early stage of Alzheimer's disease to document their self-determined will in the advanced stages of dementia. Theoretical objections have been expressed against the concept of advance patient directives (problems of authenticity and identity) which, however, cannot negate the fundamental moral authority of advance patient directives. Therefore, patients, family members, and physicians should make use of the appropriate form of advance directive as part of common treatment and care planning. Advance directives, when utilized intelligently, represent appropriate instruments for shared decision-making by patient, family members and physician. They should be utilized to a greater extent, particularly for the treatment planning of demented patients.

Advance Directives↗