Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources

Islam, Azharul; Chang, KyungHi

doi:10.3390/app11136237

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…Combining information from various imaging modalities, such as CT, MRI, and histopathology, can provide a more comprehensive understanding of post-mortem findings and enhance diagnostic accuracy [29] . Real-time Decision Support AI algorithms capable of providing real-time feedback and decision support during autopsies have the potential to streamline workflows, reduce errors, and improve efficiency in forensic investigations [30] .…”

Section: Integration Of Multimodal Datamentioning

confidence: 99%

A Review Article on the Transformative Impact of Artificial Intelligence-powered Autopsy in Forensic Medicine

2024

IJFMR

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Forensic medicine has traditionally relied on conventional autopsy methods for determining the cause of death and supporting criminal investigations. However, the introduction of artificial intelligence (AI) has sparked a new era in forensic medicine, fundamentally altering the autopsy process. This comprehensive review investigates the transformative impact of AI-powered autopsy techniques on forensic medicine, emphasizing the advancements, challenges, and future possibilities. Through a meticulous analysis of current research and case studies, the article illustrates how AI significantly improves accuracy, efficiency, and objectivity in forensic investigations, resulting in more dependable outcomes. The integration of AI into autopsy procedures represents a revolutionary shift in forensic medicine, with emerging technologies and methodologies reshaping investigative practices. Alongside traditional autopsies, the utilization of VIRTOPSY—an advanced imaging technique—is increasingly prominent, further enhancing the forensic examination process. By delving into recent advancements, applications, and potential developments, this review provides a holistic overview of how AI is reshaping forensic investigations, enhancing reliability, and contributing to justice. This review underscores the transformative role of AI in forensic medicine, highlighting its potential to reshape practices and contribute significantly to societal well-being.

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Section: Integration Of Multimodal Datamentioning

confidence: 99%

A Review Article on the Transformative Impact of Artificial Intelligence-powered Autopsy in Forensic Medicine

2024

IJFMR

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“…By using this paradigm, Islam A. et al (Islam 2021) can transform data mining's unstructured output into labelled data, which can then be used to construct an advisory and decision-making system (DMSS). Data mining techniques have allowed them to amass vast amounts of information, but the real challenge lies in sifting through all of that noise to find the gems of insight.…”

Section: Related Workmentioning

confidence: 99%

A decision-making framework utilizing machine learning techniques, based on analysing the sentiment of tweets

Durga,

Sudhakar

2023

Multidiscip. Sci. J.

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Before making any decision, especially a financial one, people use social media to chat to, learn about, and obtain advice from others. How can social media opinions be relied on and utilised economically? Due to social media sites like Facebook and Twitter, where millions of individuals offer real-time thoughts on anything using slang and emoticons, data science researchers have access to more unstructured yet useful information. Social media includes Twitter. 326 million active social media users sent 500 million tweets daily in July 2018, indicating the sector's rapid expansion. Tweets can be viewed, edited, and sent. Knowing how to interpret tweets, which might be facts or views, is helpful. Such a study may predict the stock market, election, response event, news, and subjectivity scales. Tweets are analysed using Sentiment Analysis (SA). On the other hand, Sentiment analysis provides a broad overview of tweet polarity and is not beneficial for decision-making. In this research, we design a natural language processing (NLP) Twitter SA model to overcome the challenges of tweet sentiment recognition and build a hybrid system that can recognise and classify sentiment in Twitter's real-time reactions to any topic. We use a three-classifier machine learning method to analyse tweets from the Sentiment140 dataset (Logistic Regression, Bernoulli Naive Bayes, and SVM). Our findings are supported by the Term Frequency-Inverse Document Frequency analysis (TF-IDF). After that, accuracy and F1 Scores are used to evaluate these classifiers.

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“…Customizing is another effective approach for making the CDSS outputs more meaningful, valuable, and secure to use. According to a quantitative analysis in [14], all outstanding organizations spent a significant amount of time customizing their CDSS. To reduce warning tiredness, one form of customisation may be to analyze and enhance the relevance of warnings.…”

Section: Commissioningmentioning

confidence: 99%

An Analysis of Artificial Intelligence Based Clinical Decision Support Systems

2021

JBSHA

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The growing availability of medical data has sparked fresh interests in Computerized Clinical Decision Support Systems (CDSS), thanks to recent breakthroughs in machine and deep learning. CDSS has showed a lot of promise in terms of improving healthcare, enhancing the safety of patients and minimizing treatment costs. The application of CDSS, nonetheless, is unsafe since an insufficient or defective CDSS may possibly degrade healthcare quality and place patients at potential threat. Furthermore, the deployment of a CDSS may fail when the CDSS's output is ignored by its intended users owing to a lack of confidence, relevance, or actionability. We offer literature-based advice for the various elements of CDSS adoption, with a particular emphasis on Artificial Intelligence (AI) and Machine Learning (ML) systems: quality assurance, deployment, commissioning, acceptability tests, and selection, in this research. A critical selection process will assist in the process of identifying CDSS, which effectively suits the localized sites’ needs and preferences. Acceptance testing ensures that the chosen CDSS meets the specified standards and meets the safety criteria. The CDSS will be ready for safe clinical usage at the local site once the commissioning procedure is completed. An efficient system implementation must result in a smooth rollout of the CDSS to well-trained end-users with reasonable expectations. Furthermore, quality assurance will ensure that the CDSS's levels are maintained and that any problems are discovered and resolved quickly. We conclude this research by discussing the methodical adoption process for CDSS to assist in avoiding issues, enhance the safety of patients and increasing quality of service.

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Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources

Cited by 8 publications

References 32 publications

A Review Article on the Transformative Impact of Artificial Intelligence-powered Autopsy in Forensic Medicine

A Review Article on the Transformative Impact of Artificial Intelligence-powered Autopsy in Forensic Medicine

A decision-making framework utilizing machine learning techniques, based on analysing the sentiment of tweets

An Analysis of Artificial Intelligence Based Clinical Decision Support Systems

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