| 1. |
- Ingvar, Åsa, et al.
(författare)
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Minimum labelling requirements for dermatology artificial intelligence-based Software as Medical Device (SaMD) : A consensus statement
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Ingår i: Australasian Journal of Dermatology. - 0004-8380.
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Tidskriftsartikel (refereegranskat)abstract
- Background/Objectives: Artificial intelligence (AI) holds remarkable potential to improve care delivery in dermatology. End users (health professionals and general public) of AI-based Software as Medical Devices (SaMD) require relevant labelling information to ensure that these devices can be used appropriately. Currently, there are no clear minimum labelling requirements for dermatology AI-based SaMDs. Methods: Common labelling recommendations for AI-based SaMD identified in a recent literature review were evaluated by an Australian expert panel in digital health and dermatology via a modified Delphi consensus process. A nine-point Likert scale was used to indicate importance of 10 items, and voting was conducted to determine the specific characteristics to include for some items. Consensus was achieved when more than 75% of the experts agreed that inclusion of information was necessary. Results: There was robust consensus supporting inclusion of all proposed items as minimum labelling requirements; indication for use, intended user, training and test data sets, algorithm design, image processing techniques, clinical validation, performance metrics, limitations, updates and adverse events. Nearly all suggested characteristics of the labelling items received endorsement, except for some characteristics related to performance metrics. Moreover, there was consensus that uniform labelling criteria should apply across all AI categories and risk classes set out by the Therapeutic Goods Administration. Conclusions: This study provides critical evidence for setting labelling standards by the Therapeutic Goods Administration to safeguard patients, health professionals, consumers, industry, and regulatory bodies from AI-based dermatology SaMDs that do not currently provide adequate information about how they were developed and tested.
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| 2. |
- Moloney, Kristen, et al.
(författare)
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Development of a surgical competency assessment tool for sentinel lymph node dissection by minimally invasive surgery for endometrial cancer
- 2021
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Ingår i: International Journal of Gynecological Cancer. - : BMJ. - 1048-891X .- 1525-1438. ; 31:5, s. 647-655
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Tidskriftsartikel (refereegranskat)abstract
- Sentinel lymph node dissection is widely used in the staging of endometrial cancer. Variation in surgical techniques potentially impacts diagnostic accuracy and oncologic outcomes, and poses barriers to the comparison of outcomes across institutions or clinical trial sites. Standardization of surgical technique and surgical quality assessment tools are critical to the conduct of clinical trials. By identifying mandatory and prohibited steps of sentinel lymph node (SLN) dissection in endometrial cancer, the purpose of this study was to develop and validate a competency assessment tool for use in surgical quality assurance. A Delphi methodology was applied, included 35 expert gynecological oncology surgeons from 16 countries. Interviews identified key steps and tasks which were rated mandatory, optional, or prohibited using questionnaires. Using the surgical steps for which consensus was achieved, a competency assessment tool was developed and subjected to assessments of validity and reliability. Seventy percent consensus agreement standardized the specific mandatory, optional, and prohibited steps of SLN dissection for endometrial cancer and informed the development of a competency assessment tool. Consensus agreement identified 21 mandatory and three prohibited steps to complete a SLN dissection. The competency assessment tool was used to rate surgical quality in three preselected videos, demonstrating clear separation in the rating of the skill level displayed with mean skills summary scores differing significantly between the three videos (F score=89.4; P<0.001). Internal consistency of the items was high (Cronbach α=0.88). Specific mandatory and prohibited steps of SLN dissection in endometrial cancer have been identified and validated based on consensus among a large number of international experts. A competency assessment tool is now available and can be used for surgeon selection in clinical trials and for ongoing, prospective quality assurance in routine clinical care.
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| 3. |
- Oloruntoba, Ayooluwatomiwa, et al.
(författare)
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Examining labelling guidelines for AI-based software as a medical device : A review and analysis of dermatology mobile applications in Australia
- 2024
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Ingår i: Australasian Journal of Dermatology. - 0004-8380.
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Forskningsöversikt (refereegranskat)abstract
- In recent years, there has been a surge in the development of AI-based Software as a Medical Device (SaMD), particularly in visual specialties such as dermatology. In Australia, the Therapeutic Goods Administration (TGA) regulates AI-based SaMD to ensure its safe use. Proper labelling of these devices is crucial to ensure that healthcare professionals and the general public understand how to use them and interpret results accurately. However, guidelines for labelling AI-based SaMD in dermatology are lacking, which may result in products failing to provide essential information about algorithm development and performance metrics. This review examines existing labelling guidelines for AI-based SaMD across visual medical specialties, with a specific focus on dermatology. Common recommendations for labelling are identified and applied to currently available dermatology AI-based SaMD mobile applications to determine usage of these labels. Of the 21 AI-based SaMD mobile applications identified, none fully comply with common labelling recommendations. Results highlight the need for standardized labelling guidelines. Ensuring transparency and accessibility of information is essential for the safe integration of AI into health care and preventing potential risks associated with inaccurate clinical decisions.
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