Application of artificial intelligence in respiratory medicine: Has the time arrived?

Lovejoy, Christopher A.; Phillips, Edward; Maruthappu, Mahiben

doi:10.1111/resp.13676

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…55 However, it has been noted elsewhere that the true clinicians' performance might have been underestimated because they received limited clinical information. 57 Irrespective of whether the clinicians' performance was underestimated, this study showed that AI has a potential role in respiratory medicine that is beyond that of image analysis. Furthermore, diagnosis is one of the areas of respiratory research in which clinicians and researchers in the primary care field feel there is a great need for urgent solutions.…”

Section: Ai In the Diagnosis Of Asthma And Chronic Obstructive Pulmon...mentioning

confidence: 76%

Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis

Kaplan

Cao

FitzGerald

et al. 2021

The Journal of Allergy and Clinical Immunology: In Practice

109

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Section: Ai In the Diagnosis Of Asthma And Chronic Obstructive Pulmon...mentioning

confidence: 76%

Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis

Kaplan

Cao

FitzGerald

et al. 2021

The Journal of Allergy and Clinical Immunology: In Practice

109

View full text Add to dashboard Cite

“…Similarly, ML developers should be educated on clinical needs and challenges, including clinical workflows, so that products avoid unnecessarily delaying clinical practice, overburdening practitioners, or negatively affecting the patient-physician relationship [ 50 , 54 ]. Engagement was repeatedly mentioned, including meaningful inclusion of clinicians, patients, and other stakeholders in the development of ML algorithms [ 22 , 39 , 49 , 57 , 88 , 89 ]. Finally, some authors argue that ML should be accompanied by understandable feedback on why a decision or classification was made [ 34 ].…”

Section: Resultsmentioning

confidence: 99%

“…To address this, some authors suggest large, multi-center prospective trials and quasi-experimental studies, embedded in clinical settings and focusing on downstream impact, including patient outcomes, safety, efficacy, and acceptance [ 36 , 50 , 63 , 78 , 89 , 93 ]. These shall be complemented by economic evaluations, evaluating cost-effectiveness relative to standard care [ 40 , 50 ].…”

Section: Resultsmentioning

confidence: 99%

Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging

et al. 2023

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Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field’s potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.

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“…As the study mentions, the localization of the tracheal rings is possible using segmentation module output as a tracheal GPS. AI can also assist physicians during the diagnosis of respiratory disorders, such as chronic obstructive pulmonary disease (COPD, asthma, and even lung cancer, by collecting data such as patient history, imaging (Ct scans, X-rays, bronchoscopy), and data from physical examination [ 175 , 176 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 ].…”

Section: Resultsmentioning

confidence: 99%

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

et al. 2022

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This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.

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Application of artificial intelligence in respiratory medicine: Has the time arrived?

Cited by 8 publications

References 9 publications

Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis

Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis

Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

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