Developing a new intelligent system for the diagnosis of oral medicine with case‐based reasoning approach

Ehtesham, Hamideh; Sedaghat, Reza; Mansourian, Arash; Tahmasebian, Shahram; Mohammadzadeh, Niloofar; Pourshahidi, Sara

doi:10.1111/odi.13108

Cited by 17 publications

(9 citation statements)

References 19 publications

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“…The most common methods of data collection were questionnaires/surveys and semi-structured interviews. Only one randomised controlled trial was reviewed [ 17 ], and the Delphi method was used in four studies [ 14 , 19 , 20 , 21 ].…”

Section: Resultsmentioning

confidence: 99%

Determinants of Clinical Decision Making under Uncertainty in Dentistry: A Scoping Review

et al. 2023

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Clinical decision-making for diagnosing and treating oral and dental diseases consolidates multiple sources of complex information, yet individual clinical judgements are often made intuitively on limited heuristics to simplify decision making, which may lead to errors harmful to patients. This study aimed at systematically evaluating dental practitioners’ clinical decision-making processes during diagnosis and treatment planning under uncertainty. A scoping review was chosen as the optimal study design due to the heterogeneity and complexity of the topic. Key terms and a search strategy were defined, and the articles published in the repository of the National Library of Medicine (MEDLINE/PubMed) were searched, selected, and analysed in accordance with PRISMA-ScR guidelines. Of the 478 studies returned, 64 relevant articles were included in the qualitative synthesis. Studies that were included were based in 27 countries, with the majority from the UK and USA. Articles were dated from 1991 to 2022, with all being observational studies except four, which were experimental studies. Six major recurring themes were identified: clinical factors, clinical experience, patient preferences and perceptions, heuristics and biases, artificial intelligence and informatics, and existing guidelines. These results suggest that inconsistency in treatment recommendations is a real possibility and despite great advancements in dental science, evidence-based practice is but one of a multitude of complex determinants driving clinical decision making in dentistry. In conclusion, clinical decisions, particularly those made individually by a dental practitioner, are potentially prone to sub-optimal treatment and poorer patient outcomes.

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Section: Resultsmentioning

confidence: 99%

Determinants of Clinical Decision Making under Uncertainty in Dentistry: A Scoping Review

et al. 2023

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“…A variety of algorithms were described, including hypothetical classes of products (e.g., “digital phenotyping” of psychiatric symptoms using biosensors, Bourla et al, 2020 ), specific types of algorithms (e.g., case based reasoning for diagnosis, Ehtesham et al, 2019 ), and existing proprietary tools (e.g., “Brilliant Doctor,” Wang et al, 2021 ) (see “Algorithms used” columns in Table 3 ).…”

Section: Resultsmentioning

confidence: 99%

Inclusion of Clinicians in the Development and Evaluation of Clinical Artificial Intelligence Tools: A Systematic Literature Review

et al. 2022

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The application of machine learning (ML) and artificial intelligence (AI) in healthcare domains has received much attention in recent years, yet significant questions remain about how these new tools integrate into frontline user workflow, and how their design will impact implementation. Lack of acceptance among clinicians is a major barrier to the translation of healthcare innovations into clinical practice. In this systematic review, we examine when and how clinicians are consulted about their needs and desires for clinical AI tools. Forty-five articles met criteria for inclusion, of which 24 were considered design studies. The design studies used a variety of methods to solicit and gather user feedback, with interviews, surveys, and user evaluations. Our findings show that tool designers consult clinicians at various but inconsistent points during the design process, and most typically at later stages in the design cycle (82%, 19/24 design studies). We also observed a smaller amount of studies adopting a human-centered approach and where clinician input was solicited throughout the design process (22%, 5/24). A third (15/45) of all studies reported on clinician trust in clinical AI algorithms and tools. The surveyed articles did not universally report validation against the “gold standard” of clinical expertise or provide detailed descriptions of the algorithms or computational methods used in their work. To realize the full potential of AI tools within healthcare settings, our review suggests there are opportunities to more thoroughly integrate frontline users’ needs and feedback in the design process.

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“…Different studies have been carried out to design a method to give appropriate weight to the results in clinical decision-making and to understand the effectiveness of the procedures. [ 20 21 22 ] Recording of data in a structured framework can make these studies more effective.…”

Section: Discussionmentioning

confidence: 99%

Management of the essential data element in the differential diagnosis of oral medicine: An effective step in promoting oral health

Ehtesham

Safdari

Mansourian

et al. 2020

J Edu Health Promot

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BACKGROUND: Oral soft tissue diseases include a broad spectrum, and the wide array of patient data elements need to be processed in their diagnosis. One of the biggest and most basic challenges is the analysis of this huge amount of complex patient data in an increasing number of complicated clinical decisions. This study seeks to identify the necessary steps for collecting and management of these data elements through establishing a consensus-based framework. METHODS: This research was conducted as a descriptive, cross-sectional study from April 2016 to January 2017, which has been performed in several steps: literature review, developing the initial draft (v. 0), submitting the draft to experts, validating by an expert panel, applying expert opinions and creating version v.i, performing Delphi rounds, and creating the final framework. RESULTS: The administrative data category with 17 and the historical data category with 23 data elements were utilized in recording data elements in the diagnosis of all of the different oral diseases. In the paraclinical indicator and clinical indicator categories, the necessary data elements were considered with respect to the 6 main axes of oral soft tissue diseases, according to Burket's Oral Medicine: ulcerative, vesicular, and bullous lesions; red and white lesions of the oral mucosa; pigmented lesions of the oral mucosa; benign lesions of the oral cavity and the jaws; oral and oropharyngeal cancer; and salivary gland diseases. CONCLUSIONS: The study achieved a consensus-based framework for the essential data element in the differential diagnosis of oral medicine using a comprehensive search with rich keywords in databases and reference texts, providing an environment for discussion and exchange of ideas among experts and the careful use of the Delphi decision technique.

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Developing a new intelligent system for the diagnosis of oral medicine with case‐based reasoning approach

Cited by 17 publications

References 19 publications

Determinants of Clinical Decision Making under Uncertainty in Dentistry: A Scoping Review

Determinants of Clinical Decision Making under Uncertainty in Dentistry: A Scoping Review

Inclusion of Clinicians in the Development and Evaluation of Clinical Artificial Intelligence Tools: A Systematic Literature Review

Management of the essential data element in the differential diagnosis of oral medicine: An effective step in promoting oral health

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