Artificial intelligence (AI) will enable improved diagnosis and treatment outcomes

Holloway, Lois; Bezak, Eva; Baldock, Clive

doi:10.1007/s13246-021-01034-x

Cited by 5 publications

(3 citation statements)

References 17 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While safety is critical in type A applications, risk mitigation is more important in type B applications. Epistemic uncertainty the scientific uncertainty in the model is far lower in type B applications (Holloway et al, 2021). Because errors are less common in type B applications, safety is less crucial (Pérez & Grande, 2020).…”

Section: Artificial Intelligence and Safetymentioning

confidence: 99%

Artificial intelligence applications in disease diagnosis and treatment: recent progress and outlook

Goswami,

Sachan,

Goswami

2023

RIP

View full text Add to dashboard Cite

The use of computers and other technologies to replicate human-like intelligent behaviour and critical thinking is known as artificial intelligence (AI).The development of AI-assisted applications and big data research has accelerated as a result of the rapid advancements in computing power, sensor technology, and platform accessibility that have accompanied advances in artificial intelligence. AI models and algorithms for planning and diagnosing endodontic procedures. The search engine evaluated information on artificial intelligence (AI) and its function in the field of endodontics, and it also incorporated databases like Google Scholar, PubMed, and Science Direct with the search criterion of original research articles published in English. Online appointment scheduling, online check-in at medical facilities, digitization of medical records, reminder calls for follow-up appointments and immunisation dates for children and pregnant women, as well as drug dosage algorithms and adverse effect warnings when prescribing multidrug combinations, are just a few of the tasks that already use artificial intelligence. Data from the review supported the conclusion that AI can play a significant role in endodontics, including the identification of apical lesions, classification and numbering of teeth, detection of dental caries, periodontitis, and periapical disease, diagnosis of various dental problems, aiding dentists in making referrals, and helping them develop more precise treatment plans for dental disorders. Although artificial intelligence (AI) has the potential to drastically alter how medicine is practised in ways that were previously unthinkable, many of its practical applications are still in their infancy and need additional research and development. Over the past ten years, artificial intelligence in ophthalmology has grown significantly and will continue to do so as imaging techniques and data processing algorithms improve.

show abstract

Section: Artificial Intelligence and Safetymentioning

confidence: 99%

Artificial intelligence applications in disease diagnosis and treatment: recent progress and outlook

Goswami,

Sachan,

Goswami

2023

RIP

View full text Add to dashboard Cite

show abstract

“…Diagnosis of most diseases follows a more predictable path, including computational modelling of risk assessments (Holloway, Bezak, and Baldock 2021). Generally, diagnosis is based on questions and answers, with the physician's questions guiding and structuring the answers, followed sometimes by physical exam and imaging or lab tests.…”

Section: Diagnosis: Past and Presentmentioning

confidence: 99%

Narratives of Space and Time in Multiple Sclerosis Diagnosis

Leverage¹

2023

Perspectives in Biology and Medicine

View full text Add to dashboard Cite

This article examines the complexity of narratives of space and time which support the diagnosis of multiple sclerosis, and the consequences for patients disadvantaged by deficits in social cognition or socioeconomic status in areas relating to literacy. In the context of demonstrating the significance of narratives of space and time in the diagnostic process for multiple sclerosis, the article discusses new strategies for treatment that engage the arts and humanities and presents a brief history of the disease and current diagnostic criteria, before discussing the MS Society of Great Britain and Ireland's awareness and fundraising campaign from the 1980s, and Tom Kempinski's play Duet for One, inspired by the life of cellist Jacqueline du Pré. The conclusion addresses the urgency of heightening patient and clinician awareness of how the presentation, interpretation, and sharing of narrative accounts of disease influence health outcomes and makes suggestions about how to adjust for these biases. Multiple sclerosis is what a good clinician would call multiple sclerosis. -Compston et al. (2006)Narratives, Multiple Sclerosis, and the Problem N arrative has been integral to the history of multiple sclerosis (MS) in several important ways. This is reflected explicitly in the distinctly literary

show abstract

“…In recent years, radiomics and machine learning methods have been used to diagnose and even predict a variety of diseases, such as brain tumors [ 14 ], breast cancer [ 15 ], cardiovascular disease [ 16 , 17 ], and Leukemia [ 18 ]. During the spread of COVID-19 disease, several studies have been conducted to use radiomics and machine learning algorithms for segmentation and categorization of CT images and consequently for detection of COVID-19 infection and prediction of patient’s condition [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of stage of lung changes in COVID-19 disease based on CT images: a radiomics approach

et al. 2022

View full text Add to dashboard Cite

The aim of this study is to classify patients suspected from COVID-19 to five stages as normal, early, progressive, peak, and absorption stages using radiomics approach based on lung computed tomography images. Lung CT scans of 683 people were evaluated. A set of statistical texture features was extracted from each CT image. The people were classified using the random forest algorithm as an ensemble method based on the decision trees outputs to five stages of COVID-19 disease. Proposed method attains the highest result with an accuracy of 93.55% (96.25% in normal, 74.39% in early, 100% in progressive, 82.19% in peak, and 96% in absorption stage) compared to the other three common classifiers. Radiomics method can be used for the classification of the stage of COVID-19 disease with good accuracy to help decide the length of time required to hospitalize patients, determine the type of treatment process required for patients in each category, and reduce the cost of care and treatment for hospitalized individuals.

show abstract

Artificial intelligence (AI) will enable improved diagnosis and treatment outcomes

Cited by 5 publications

References 17 publications

Artificial intelligence applications in disease diagnosis and treatment: recent progress and outlook

Artificial intelligence applications in disease diagnosis and treatment: recent progress and outlook

Narratives of Space and Time in Multiple Sclerosis Diagnosis

Detection of stage of lung changes in COVID-19 disease based on CT images: a radiomics approach

Contact Info

Product

Resources

About