scholarly journals Bradycardia in Recent Heart Transplant: Will the Microscope Illuminate the True Answer?

2021 ◽  
Vol 17 (2) ◽  
pp. e14-e17
Author(s):  
Amit Alam ◽  
Philip F Halloran ◽  
Christo Mathew ◽  
Samreen Fathima ◽  
Alexia Ghazi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Heart Transplant ◽  
Biopsy Specimen ◽  
Clinical Decision Making ◽  
Clinical Presentation ◽  
Clinical Decision ◽  
Transplant Recipients ◽  
Significant Morbidity ◽  
True Answer

Transplant recipients are at risk of developing rejection that may cause significant morbidity and mortality following transplantation. The clinical presentation of rejection may be atypical, leading to difficulties in diagnosis and management especially in cases with a nondiagnostic biopsy specimen. The emergence of artificial intelligence may aid in clinical decision making when traditional techniques are inconclusive.

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 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 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.

scholarly journals The Impact of Artificial Intelligence on Health Equity in Oncology: A Scoping Review

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4934-4934
Author(s):  
Paul Istasy ◽  
Wen Shen Lee ◽  
Alla Iansavitchene ◽  
Ross Upshur ◽  
Bekim Sadikovic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Health Equity ◽  
Scoping Review ◽  
Clinical Decision Making ◽  
Ethical Issues ◽  
Clinical Decision ◽  
Resource Limited Settings ◽  
Resource Limited ◽  
The Impact

Abstract Introduction: The expanding use of Artificial Intelligence (AI) in hematology and oncology research and practice creates an urgent need to consider the potential impact of these technologies on health equity at both local and global levels. Fairness and equity are issues of growing concern in AI ethics, raising problems ranging from bias in datasets and algorithms to disparities in access to technology. The impact of AI on health equity in oncology, however, remains underexplored. We conducted a scoping review to characterize, evaluate, and identify gaps in the existing literature on AI applications in oncology and their implications for health equity in cancer care. Methodology: We performed a systematic literature search of MEDLINE (Ovid) and EMBASE from January 1, 2000 to March 28, 2021 using key terms for AI, health equity, and cancer. Our search was restricted to English language abstracts with no restrictions by publication type. Two reviewers screened a total of 9519 abstracts, and 321 met inclusion criteria for full-text review. 247 were included in the final analysis after assessment by three reviewers. Studies were analysed descriptively, by location, type of cancer and AI application, as well as thematically, based on issues pertaining to health equity in oncology. Results: Of the 247 studies included in our analysis, 150 (60.7%) were based in North America, 57 (23.0%) in Asia, 29 (11.7%) in Europe, 5 (2.1%) in Central/South America, 4 (1.6%) in Oceania, and 2 (0.9%) in Africa. 71 (28.6%) were reviews and commentaries, and 176 were (71.3%) clinical studies. 25 (10.1%) focused on AI applications in screening, 42 (17.0%) in diagnostics, 46 (18.6%) in prognostication, and 7 (2.9%) in treatment. 40 (16.3%) used AI as a tool for clinical/epidemiological research and 87 (35.2%) discussed multiple applications of AI. A diverse range of cancers were represented in the studies, including hematologic malignancies. Our scoping review identified three overarching themes in the literature, which largely focused on how AI might improve health equity in oncology. These included: (1) the potential for AI reduce disparities by improving access to health services in resource-limited settings through applications such as low-cost cancer screening technologies and decision support systems; (2) the ability of AI to mitigate bias in clinical decision-making through algorithms that alert clinicians to potential sources of bias thereby allowing for more equitable and individualized care; (3) the use of AI as a research tool to identify disparities in cancer outcomes based on factors such as race, gender and socioeconomic status, and thus inform health policy. While most of the literature emphasized the positive impact of AI in oncology, there was only limited discussion of AI's potential adverse effects on health equity . Despite engaging with the use of AI in resource-limited settings, ethical issues surrounding data extraction and AI trials in low-resource settings were infrequently raised. Similarly, AI's potential to reinforce bias and widen disparities in cancer care was under-examined despite engagement with related topics of bias in clinical decision-making. Conclusion: The overwhelming majority of the literature identified by our scoping review highlights the benefits of AI applications in oncology, including its potential to improve access to care in low-resource settings, mitigate bias in clinical decision-making, and identify disparities in cancer outcomes. However, AI's potential negative impacts on health equity in oncology remain underexplored: ethical issues arising from deploying AI technologies in low-resources settings, and issues of bias in datasets and algorithms were infrequently discussed in articles dealing with related themes. Disclosures No relevant conflicts of interest to declare.

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