Developing an AI-assisted clinical decision support system to enhance in-patient holistic health care

Juang, Wang‐Chuan; Hsu, Ming-Hsia; Cai, Zheng-Xun; Chen, Chia-Mei

doi:10.1371/journal.pone.0276501

Cited by 14 publications

(6 citation statements)

References 20 publications

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“…Reliable AI is essential for clinical decision support in the biomedical domain to avoid adverse patient outcomes [21,22]. This study includes the multiple experiments on different datasets such as difference device, and out-of-distribution dataset to explore the model transparency.…”

Section: Reliable Ai For Clinical Decision Supportmentioning

confidence: 99%

Developing a deep learning model to predict the breast implant texture types with ultrasonography image: feasibility study

Kim,

Jeong,

et al. 2024

Preprint

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Introduction Breast implants, including textured variants, have been widely used in aesthetic and reconstructive mammoplasty. However, the textured type, which is one of the shell types of breast implants, has been identified as a possible carcinogenic factor for lymphoma, specifically breast implant-associated anaplastic large cell lymphoma (BIA-ALCL). Identifying the texture type of the implant is critical to the diagnosis of BIA-ALCL. However, distinguishing the shell type can be difficult due to human memory or loss of medical history. An alternative approach is to use ultrasonography, but this method also has limitations in quantitative assessment. Objective The objective of this study is to determine the feasibility of using a deep learning model to classify the textured shell type of breast implants and make robust predictions from ultrasonography images from heterogeneous sources. Methods A total of 19,502 breast implant images were retrospectively collected from heterogeneous sources, including images from both Canon (D1) and GE (D2), images of ruptured implants (D3), and images without implants (D4), as well as publicly available images (D5). The Canon (D1) images were trained using Resnet-50. The performance of the model on D1 was evaluated using stratified 5-fold cross-validation. Additionally, external validation was conducted using D2 and D5. The AUROC and PRAUC were calculated based on the contribution of the pixels with Grad-CAM. To identify the significant pixels for classification, we masked the pixels that contributed less than 10%, up to a maximum of 100%. To assess model robustness to uncertainty, Shannon entropy was calculated for four image groups: Canon (D1), GE (D2), ruptured implant (D3), and without implants (D5). Results The deep learning model achieved an average AUROC of 0.98 and a PRAUC of 0.88 in the Canon dataset (D1). For images captured with GE (D2), the model achieved an AUROC of 0.985 and a PRAUC of 0.748. Additionally, the model predicted an AUROC of 0.909 and a PRAUC of 0.958 for a dataset available online. For quantitative validation, this model maintained PRAUC up to 90% masking of less contributing pixels, and the remnant pixels located in breast shell layers. Furthermore, the prediction uncertainty increased in the following order: Canon (D1), GE (D2), ruptured implant (D3), no implant (D5) (0.066; 0072; 0.371; 0.777, respectively). Conclusion We have demonstrated the feasibility of using deep learning to predict the shell types of breast implants. With this approach, the textured shell types of breast implants can be quantified, supporting the first step in the diagnosis of BIA-ALCL.

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Section: Reliable Ai For Clinical Decision Supportmentioning

confidence: 99%

Developing a deep learning model to predict the breast implant texture types with ultrasonography image: feasibility study

Kim,

Jeong,

et al. 2024

Preprint

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“…By automating burdensome administrative duties, AI can allow physicians to devote more time to holistically engaged interactions with patients. 19 More attentive and personalized care can in turn enhance patients’ trust and satisfaction with their treatments. 20 The increased time available for patient interaction also provides a valuable opportunity for physicians to demonstrate empathy, a key factor in patient satisfaction and treatment adherence.…”

Section: Impact On Physician-patient Relationshipsmentioning

confidence: 99%

Transforming Healthcare with AI: Promises, Pitfalls, and Pathways Forward

Shuaib

2024

IJGM

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This perspective paper provides a comprehensive examination of artificial intelligence (AI) in healthcare, focusing on its transformative impact on clinical practices, decision-making, and physician-patient relationships. By integrating insights from evidence, research, and real-world examples, it offers a balanced analysis of AI’s capabilities and limitations, emphasizing its role in streamlining administrative processes, enhancing patient care, and reducing physician burnout while maintaining a human-centric approach in medicine. The research underscores AI’s capacity to augment clinical decision-making and improve patient interactions, but it also highlights the variable impact of AI in different healthcare settings. The need for context-specific adaptations and careful integration of AI technologies into existing healthcare workflows is emphasized to maximize benefits and minimize unintended consequences. Significant attention is given to the implications of AI on the roles and competencies of healthcare professionals. The emergence of AI necessitates new skills in data literacy and technology use, prompting a shift in educational curricula towards digital health and AI training. Ethical considerations are a pivotal aspect of the discussion. The paper explores the challenges posed by data privacy concerns, algorithmic biases, and ensuring equitable access to AI-driven healthcare. It advocates for the development of comprehensive ethical frameworks and ongoing research to guide the responsible use of AI in healthcare. Conclusively, the paper advocates for a balanced approach to AI adoption in healthcare, highlighting the importance of ongoing research, strategic implementation, and the synergistic combination of human expertise with AI technologies for optimal patient care.

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“…Furthermore, the CDSS could also facilitate decisions regarding whether a patient's symptoms or illness are better managed through TCIM or if conventional medicine would be a more appropriate treatment approach. 17 , 29 …”

Section: Ai and Tcimmentioning

confidence: 99%

Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare

Ng,

Cramer,

Lee

et al. 2024

Integrative Medicine Research

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Developing an AI-assisted clinical decision support system to enhance in-patient holistic health care

Cited by 14 publications

References 20 publications

Developing a deep learning model to predict the breast implant texture types with ultrasonography image: feasibility study

Developing a deep learning model to predict the breast implant texture types with ultrasonography image: feasibility study

Transforming Healthcare with AI: Promises, Pitfalls, and Pathways Forward

Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare

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