scholarly journals Can artificial Intelligence Support Clinical Decision Making in the Management of Hepatocellular Carcinoma Patients? (Preprint)

JMIR Cancer ◽  
10.2196/21074 ◽  
2020 ◽  
Author(s):  
Zebin Chen ◽  
Kaiyu Sun ◽  
Ruiming Liang ◽  
Zhenwei Peng ◽  
Jingxian Shen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Hepatocellular Carcinoma ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Intelligence Support

Can artificial intelligence support the clinical decision making for Barcelona clinic liver cancer stage 0/a hepatocellular carcinoma in China?

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15634-e15634
Author(s):  
Ze-bin Chen ◽  
Shu-Ling Chen ◽  
Rui-Ming Liang ◽  
Zhen-Wei Peng ◽  
Jing-Xian Shen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Hepatocellular Carcinoma ◽  
Decision Making ◽  
Clinical Decision Support System ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Concordance Rate ◽  
Chinese Patients ◽  
Cancer Types ◽  
Ibm Watson

e15634 Background: Artificial intelligence (AI) is emerging as a revolutionary technology with the power to transform healthcare. IBM Watson for Oncology (WFO), as an AI clinical decision support system (CDSS), has been investigated about its impact on clinical decision making in some cancer types and shown potential to be an effective CDSS in cancer care. However, the feasibility of WFO in Chinese patients with hepatocellular carcinoma (HCC) has not been reported. Methods: Artificial intelligence (AI) is emerging as a revolutionary technology with the power to transform healthcare. IBM Watson for Oncology (WFO), as an AI clinical decision support system (CDSS), has been investigated about its impact on clinical decision making in some cancer types and shown potential to be an effective CDSS in cancer care. However, the feasibility of WFO in Chinese patients with hepatocellular carcinoma (HCC) has not been reported. Results: The overall concordance rate was 60.5%, with 53.7% and 61.4% in BCLC stage 0 and A respectively. After the MDT re-review, the overall, BCLC stage 0 and A concordance rate increased to 67.3%, 65.9% and 67.3%. The main discordance was that MDT recommended more aggressive treatment options (eg. hepatectomy) than WFO did. The increase in concordance rate may be due to the progress of treatment of HCC in the past 5 years. Conclusions: With the concordance and reasonability verified by MDT in this study, WFO may provide practical reference in BCLC stage 0/A HCC. Localization is required to cover the disparity in guideline and patient characteristics between China and the US.

scholarly journals Clinical decision making and research in hepatocellular carcinoma: Pivotal role of imaging techniques

Hepatology ◽  
2011 ◽  
Vol 54 (6) ◽  
pp. 2238-2244 ◽  
Author(s):  
Jordi Bruix ◽  
Maria Reig ◽  
Jordi Rimola ◽  
Alejandro Forner ◽  
Marta Burrel ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Imaging Techniques ◽  
Clinical Decision ◽  
Pivotal Role

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.

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