The Use of Artificial Intelligence Systems for Support of Medical Decision-Making

2011 ◽  
pp. 1017-1029
Author(s):  
William Claster ◽  
Nader Ghotbi ◽  
Subana Shanmuganathan
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Statistical Analysis ◽  
Medical Records ◽  
Clinical Decision Making ◽  
Ct Scanning ◽  
Clinical Decision ◽  
Radiology Department ◽  
Self Organizing Map ◽  
One Year

There is a treasure trove of hidden information in the textual and narrative data of medical records that can be deciphered by text-mining techniques. The information provided by these methods can provide a basis for medical artificial intelligence and help support or improve clinical decision making by medical doctors. In this paper we extend previous work in an effort to extract meaningful information from free text medical records. We discuss a methodology for the analysis of medical records using some statistical analysis and the Kohonen Self-Organizing Map (SOM). The medical data derive from about 700 pediatric patients’ radiology department records where CT (Computed Tomography) scanning was used as part of a diagnostic exploration. The patients underwent CT scanning (single and multiple) throughout a one-year period in 2004 at the Nagasaki University Medical Hospital. Our approach led to a model based on SOM clusters and statistical analysis which may suggest a strategy for limiting CT scan requests. This is important because radiation at levels ordinarily used for CT scanning may pose significant health risks especially to children.

The Use of Artificial Intelligence Systems for Support of Medical Decision-Making

2010 ◽  
pp. 18-30 ◽  
Author(s):  
William Claster ◽  
Nader Ghotbi ◽  
Subana Shanmuganathan
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Statistical Analysis ◽  
Medical Records ◽  
Clinical Decision Making ◽  
Ct Scanning ◽  
Clinical Decision ◽  
Radiology Department ◽  
Self Organizing Map ◽  
One Year

There is a treasure trove of hidden information in the textual and narrative data of medical records that can be deciphered by text-mining techniques. The information provided by these methods can provide a basis for medical artificial intelligence and help support or improve clinical decision making by medical doctors. In this paper we extend previous work in an effort to extract meaningful information from free text medical records. We discuss a methodology for the analysis of medical records using some statistical analysis and the Kohonen Self-Organizing Map (SOM). The medical data derive from about 700 pediatric patients’ radiology department records where CT (Computed Tomography) scanning was used as part of a diagnostic exploration. The patients underwent CT scanning (single and multiple) throughout a one-year period in 2004 at the Nagasaki University Medical Hospital. Our approach led to a model based on SOM clusters and statistical analysis which may suggest a strategy for limiting CT scan requests. This is important because radiation at levels ordinarily used for CT scanning may pose significant health risks especially to children.

Choosing Wisely in the Era of Artificial Intelligence (Preprint)

2019 ◽  
Author(s):  
Nishila Mehta ◽  
Karen Born ◽  
Tai Huynh ◽  
Benjamin Fine
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Medical Records ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Choosing Wisely ◽  
Computing Power ◽  
Patient Resources ◽  
Patients Experience ◽  
Care Decision

UNSTRUCTURED Choosing Wisely campaigns have spread to more than 20 countries worldwide. Choosing Wisely is a clinician-led effort to produce clinician recommendations and patient resources around overused tests, procedures and medications. The digitization of medical records and workflows and growth in computing power has enabled artificial intelligence (AI) to be applied to augment health care decision-making. Overuse is an important problem for AI-enabled technologies to address. This commentary offers a roadmap of opportunities for AI-augmented efforts to reduce overuse which are presented according to a patient’s journey of care, beginning from when patients experience symptoms through clinical decision making and subsequent evaluation efforts. This roadmap can be used to guide the cross-discipline development and implementation of AI-enabled tools to reduce overuse and drive value.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

2020 ◽  
pp. 167-186
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

scholarly journals Circulating microRNAs as noninvasive biomarkers for canine Cushing's syndrome

2021 ◽  
Author(s):  
Karin Sanders ◽  
Anouk Veldhuizen ◽  
Hans S. Kooistra ◽  
Adri Slob ◽  
Elpetra P.M. Timmermans-Sprang ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Differentially Expressed ◽  
Circulating Mirnas ◽  
Circulating Micrornas ◽  
Blood Samples ◽  
Healthy Dogs ◽  
Noninvasive Biomarkers ◽  
One Year

Canine Cushing′s syndrome (hypercortisolism) can be caused by a pituitary tumor (pituitary-dependent hypercortisolism; PDH) or a cortisol-secreting adrenocortical tumor (csACT). For both cases, noninvasive biomarkers that could pre-operatively predict the risk of recurrence after surgery would greatly impact clinical decision making. The aim of this study was to determine whether circulating microRNAs (miRNAs) can be used as noninvasive biomarkers for canine Cushing′s syndrome. After a pilot study with 40 miRNAs in blood samples of healthy dogs (n = 3), dogs with PDH (n = 3) and dogs with a csACT (n = 4), we selected a total of 20 miRNAs for the definitive study. In the definitive study, these 20 miRNAs were analyzed in blood samples of healthy dogs (n = 6), dogs with PDH (n = 19, pre- and post-operative samples) and dogs with a csACT (n = 26, pre-operative samples). In dogs with PDH, six miRNAs (miR-122-5p, miR-126-5p, miR-141-3p, miR-222-3p, miR-375-3p and miR-483-3p) were differentially expressed compared to healthy dogs. Of one miRNA, miR-122-5p, the expression levels did not overlap between healthy dogs and dogs with PDH (p = 2.9x10-4), significantly decreased after hypophysectomy (p = 0.013), and were significantly higher (p = 0.017) in dogs with recurrence (n = 3) than in dogs without recurrence for at least one year after hypophysectomy (n = 7). In dogs with csACTs, two miRNAs (miR-483-3p and miR-223-3p) were differentially expressed compared to healthy dogs. Additionally, miR-141-3p was expressed significantly lower (p = 0.009) in dogs with csACTs that had a histopathological Utrecht score of ≥ 11 compared to those with a score of < 11. These results indicate that circulating miRNAs have the potential to be noninvasive biomarkers in dogs with Cushing′s syndrome that may contribute to clinical decision-making.

scholarly journals The Role of Artificial Intelligence in Many Dental Specialties

2021 ◽  
Vol 41 ◽  
pp. 03005
Author(s):  
Choirunisa Nur Humairo ◽  
Aquarina Hapsari ◽  
Indra Bramanti
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Clinic Management ◽  
Pediatric Dentist ◽  
Dental Procedures ◽  
Evolution Of Technology ◽  
Near Future

Background: Technology has become a fundamental part of human living. The evolution of technology has been advantageous to science development, including dentistry. One of the latest technology that draw many attention is Artificial Intelligence (AI). Purpose: The aim of this review is to explain the use of AI in many disciplines of dental specialties and its benefit. Reviews: The application of Artificial Intelligence may be beneficial for all dental specialties, varying from pediatric dentist to oral surgeon. In dental clinic management, AI may assist in medical record as well as other paperwork. AI would also give a valuable contribution in important dental procedures, such as diagnosis and clinical decision making. It helps the dentist deliver the best treatment for the patients. Conclusion: The latest development of Artificial Intelligence is beneficial for dental practitioner in the near future. It is considered as a breakthrough of the 21st century to support the diagnostic procedure and decision making in clinical practice. The use of AI can be applied in most of dental specialties.

Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making

2020 ◽  
pp. 084653712094143
Author(s):  
Jaryd R. Christie ◽  
Pencilla Lang ◽  
Lauren M. Zelko ◽  
David A. Palma ◽  
Mohamed Abdelrazek ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Biomarker Discovery ◽  
Surgical Techniques ◽  
Clinical Decision ◽  
Clinical Implementation ◽  
Cancer Management

Lung cancer remains the most common cause of cancer death worldwide. Recent advances in lung cancer screening, radiotherapy, surgical techniques, and systemic therapy have led to increasing complexity in diagnosis, treatment decision-making, and assessment of recurrence. Artificial intelligence (AI)–based prediction models are being developed to address these issues and may have a future role in screening, diagnosis, treatment selection, and decision-making around salvage therapy. Imaging plays an essential role in all components of lung cancer management and has the potential to play a key role in AI applications. Artificial intelligence has demonstrated value in prognostic biomarker discovery in lung cancer diagnosis, treatment, and response assessment, putting it at the forefront of the next phase of personalized medicine. However, although exploratory studies demonstrate potential utility, there is a need for rigorous validation and standardization before AI can be utilized in clinical decision-making. In this review, we will provide a summary of the current literature implementing AI for outcome prediction in lung cancer. We will describe the anticipated impact of AI on the management of patients with lung cancer and discuss the challenges of clinical implementation of these techniques.

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