AI

NewsBot · Oct 21, 2024

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AI discernment in foot and ankle surgery research: A survey investigation
Steven R Cooperman, Abisola Olaniyan, Roberto A Brandão
Foot Ankle Surg. 2024 Oct 9:S1268-7731(24)00226-1

Background: This study evaluated the ability to differentiate between AI-generated and human-authored abstracts in foot and ankle surgery.

Methods: An AI system (ChatGPT 3.0) was trained on 21 published abstracts to create six novel case abstracts. Nine foot and ankle surgeons participated in a blinded survey, tasked with distinguishing AI-generated from human-written abstracts, rating their confidence in their responses. Surveys were completed twice at two different time points to evaluate intra-/inter-observer reliability.

Results: The overall accuracy rate for distinguishing AI-generated from human-written abstracts was 50.5 % (n = 109/216), indicating no better performance than random chance. Reviewer experience and AI familiarity did not significantly affect accuracy. Inter-rater reliability was moderate initially but decreased over time, and intra-rater reliability was poor.

Conclusions: In their current form, AI-generated abstracts are nearly indistinguishable from human-written ones, posing challenges for consistent identification in foot and ankle surgery.
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NewsBot · Oct 21, 2024

Related:
Artificial Intelligence in Diabetic Foot Medicine

NewsBot · Oct 24, 2024

Can ChatGPT-4 Diagnose and Treat Like an Orthopaedic Surgeon? Testing Clinical Decision Making and Diagnostic Ability in Soft-Tissue Pathologies of the Foot and Ankle
Hartman, Hayden BS; Essis, Maritza Diane MS, MD; Tung, Wei Shao BS; Oh, Irvin MD; Peden, Sean MD; Gianakos, Arianna L. DO
Journal of the American Academy of Orthopaedic Surgeons ():10.5435/JAAOS-D-24-00595, October 15, 2024. | DOI: 10.5435/JAAOS-D-24-00595

Introduction:
ChatGPT-4, a chatbot with an ability to carry human-like conversation, has attracted attention after demonstrating aptitude to pass professional licensure examinations. The purpose of this study was to explore the diagnostic and decision-making capacities of ChatGPT-4 in clinical management specifically assessing for accuracy in the identification and treatment of soft-tissue foot and ankle pathologies.

Methods:
This study presented eight soft-tissue–related foot and ankle cases to ChatGPT-4, with each case assessed by three fellowship-trained foot and ankle orthopaedic surgeons. The evaluation system included five criteria within a Likert scale, scoring from 5 (lowest) to 25 (highest possible).

Results:
The average sum score of all cases was 22.0. The Morton neuroma case received the highest score (24.7), and the peroneal tendon tear case received the lowest score (16.3). Subgroup analyses of each of the 5 criterion using showed no notable differences in surgeon grading. Criteria 3 (provide alternative treatments) and 4 (provide comprehensive information) were graded markedly lower than criteria 1 (diagnose), 2 (treat), and 5 (provide accurate information) (for both criteria 3 and 4: P = 0.007; P = 0.032; P < 0.0001). Criteria 5 was graded markedly higher than criteria 2, 3, and 4 (P = 0.02; P < 0.0001; P < 0.0001).

Conclusion:
This study demonstrates that ChatGPT-4 effectively diagnosed and provided reliable treatment options for most soft-tissue foot and ankle cases presented, noting consistency among surgeon evaluators. Individual criterion assessment revealed that ChatGPT-4 was most effective in diagnosing and suggesting appropriate treatment, but limitations were seen in the chatbot's ability to provide comprehensive information and alternative treatment options. In addition, the chatbot successfully did not suggest fabricated treatment options, a common concern in prior literature. This resource could be useful for clinicians seeking reliable patient education materials without the fear of inconsistencies, although comprehensive information beyond treatment may be limited.
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NewsBot · Nov 5, 2024

AI models for the early detection of diabetic feet
Lim, Qian Hui
Source

Diabetic foot ulcers (DFUs), characterised by wounds or open sores on the feet, are common implications of diabetes, resulting from impaired wound healing and increased susceptibility to infection. This can lead to amputations in severe cases. With the advancement of artificial intelligence and machine learning frameworks, computer-aided diagnosis has allowed for more accurate and efficient diagnosis of DFUs. Consequently, this has led to the development of newer computer vision models such as Vision Transformers (ViT) and Vision Graph Neural Networks (ViG). In this study, we explore the newly developed ViT and ViG models in addition to the classic convolutional neural network models (CNNs) for the classification of healthy and ulcerated diabetic foot images. The proposed approach compares three pre-trained models: DenseNet121, ViTB/16, and ViG-Ti to extract features and classify the foot sample images. Experimental results demonstrate the effectiveness of such newly developed models in DFU image classification, with ViT-B/16 achieving the best results of 99.05% accuracy, 99.02% precision, 99.02% recall and 99.02% F1-score. The findings of this study hold meaningful implications for clinical practice, offering great potential in the use of ViT and ViG frameworks for automated early diagnosis of DFUs.
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NewsBot · Jan 2, 2025

Utilization of artificial intelligence in the diagnosis of pes planus and pes cavus with a smartphone camera
Samir Ghandouret al
World J Orthop. 2024 Dec 18;15(12):1146-1154.

Background: Pes planus (flatfoot) and pes cavus (high arch foot) are common foot deformities, often requiring clinical and radiographic assessment for diagnosis and potential subsequent management. Traditional diagnostic methods, while effective, pose limitations such as cost, radiation exposure, and accessibility, particularly in underserved areas.

Aim: To develop deep learning algorithms that detect and classify such deformities using smartphone cameras.

Methods: An algorithm that integrated a deep convolutional neural network (CNN) into a smartphone camera was utilized to detect pes planus and pes cavus deformities. This case control study was conducted at a tertiary hospital with participants recruited from two orthopaedic foot and ankle clinics. The CNN was trained and tested using photographs of the medial aspect of participants' feet, taken under standardized conditions. Participants included subjects with standard foot alignment, pes planus, or pes cavus determined by an expert clinician using the foot posture index. The model's performance was assessed in comparison to clinical assessment and radiographic measurements, specifically lateral tarsal-first metatarsal angle and calcaneal inclination angle.

Results: The CNN model demonstrated high accuracy in diagnosing both pes planus and pes cavus, with an optimized area under the curve of 0.90 for pes planus and 0.90 for pes cavus. It showed a specificity and sensitivity of 84% and 87% for pes planus detection, respectively; and 97% and 70% for pes cavus, respectively. The model's prediction correlated moderately with radiographic lateral Meary's angle measurements, indicating the model's excellent reliability in assessing food arch deformity (P < 0.05).

Conclusion: This study highlights the potential of using a smartphone-based CNN model as a screening tool that is reliable and accessible for the detection of pes planus and pes cavus deformities, which is especially beneficial for underserved communities and patients with pain generated by subtle foot arch deformities.
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NewsBot · Feb 23, 2025

Prioritizing Trust in Podiatrists’ Preference for AI in Supportive Roles Over Diagnostic Roles in Health Care: Qualitative Interview and Focus Group Study
Mohammed A Tahtali et al
Preprint

Background: As artificial intelligence (AI) evolves, its roles have expanded from helping out with routine tasks to making complex decisions, once the exclusive domain of human experts. This shift is pronounced in health care, where AI aids in tasks ranging from image recognition in radiology to personalized treatment plans, demonstrating the potential to, at times, surpass human accuracy and efficiency. Despite AI’s accuracy in some critical tasks, the adoption of AI in health care is a challenge, in part because of skepticism about being able to rely on AI decisions.

Objective: This study aimed to identify and delve into more effective and acceptable ways of integrating AI into a broader spectrum of health care tasks.

Methods: We included 2 qualitative phases to explore podiatrists’ views on AI in health care. Initially, we interviewed 9 podiatrists (7 women and 2 men) with a mean age of 41 (SD 12) years and aimed to capture their sentiments regarding the use and role of AI in their work. Subsequently, a focus group with 5 podiatrists (4 women and 1 man) with a mean age of 54 (SD 10) years delved into AI’s supportive and diagnostic roles on the basis of the interviews. All interviews were recorded, transcribed verbatim, and analyzed using Atlas.ti and QDA-Miner, using both thematic analysis for broad patterns and framework analysis for structured insights per established guidelines.

Results: Our research unveiled 9 themes and 3 subthemes, clarifying podiatrists’ nuanced views on AI in health care. Key overlapping insights in the 2 phases included a preference for using AI in supportive roles, such as triage, because of its efficiency and process optimization capabilities. There is a discernible hesitancy toward leveraging AI for diagnostic purposes, driven by concerns regarding its accuracy and the essential nature of human expertise. The need for transparency and explainability in AI systems emerged as a critical factor for fostering trust in both phases.

Conclusions: The findings highlight a complex view from podiatrists on AI, showing openness to its application in supportive roles while exercising caution with diagnostic use. This result is consistent with a careful introduction of AI into health care in roles, such as triage, in which there is initial trust, as opposed to roles that ask the AI for a complete diagnosis. Such strategic adoption can mitigate initial resistance, gradually building the confidence to explore AI’s capabilities in more nuanced tasks, including diagnostics, where skepticism is currently more pronounced. Adopting AI stepwise could thus enhance trust and acceptance across a broader range of health care tasks, aligning technology integration with professional comfort and patient care standards.
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