Development of a Cloud-Based Clinical Decision Support System for Ophthalmology Triage Using Decision Tree Artificial Intelligence

Tanya, Stuti M.; Nguyen, Anne Xuan-Lan; Buchanan, Sean G.; Jackman, Christopher S.

doi:10.1016/j.xops.2022.100231

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…Inspired by the immense potential of CDSS, a recent and first study of its type was conducted to supplement the decision process, in particular in triage and referral. By utilizing precise diagnosis, streamlining the collection of information, and simultaneous elimination of obsolete paper-based consultations, the cloud-based CDSS has demonstrated higher levels of accuracy and urgency of consultations [91]. In brief, AI should be considered as a valuable tool to aid clinicians in supplementing their expertise, not a substituent of manpower.…”

Section: Artificial Intelligence Recent Technological Advancements An...mentioning

confidence: 99%

Evaluating the Efficacy of Teleophthalmology in Delivering Ophthalmic Care to Underserved Populations: A Literature Review

Dolar-Szczasny

Barańska

Ręjdak

2023

JCM

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Technological advancement has brought commendable changes in medicine, advancing diagnosis, treatment, and interventions. Telemedicine has been adopted by various subspecialties including ophthalmology. Over the years, teleophthalmology has been implemented in various countries, and continuous progress is being made in this area. In underserved populations, due to socioeconomic factors, there is little or no access to healthcare facilities, and people are at higher risk of eye diseases and vision impairment. Transportation is the major hurdle for these people in obtaining access to eye care in the main hospitals. There is a dire need for accessible eye care for such populations, and teleophthalmology is the ray of hope for providing eye care facilities to underserved people. Numerous studies have reported the advantages of teleophthalmology for rural populations such as being cost-effective, timesaving, reliable, efficient, and satisfactory for patients. Although it is being practiced in urban populations, for rural populations, its benefits amplify. However, there are certain obstacles as well, such as the cost of equipment, lack of steady electricity and internet supply in rural areas, and the attitude of people in certain regions toward acceptance of teleophthalmology. In this review, we have discussed in detail eye health in rural populations, teleophthalmology, and its effectiveness in rural populations of different countries.

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Section: Artificial Intelligence Recent Technological Advancements An...mentioning

confidence: 99%

Evaluating the Efficacy of Teleophthalmology in Delivering Ophthalmic Care to Underserved Populations: A Literature Review

Dolar-Szczasny

Barańska

Ręjdak

2023

JCM

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“…They must undertake a significant amount of analysis and judgment day after day, testing not only their professional knowledge but also their work efficiency. Against this backdrop, improving the speed and accuracy of diagnoses can not only improve patient outcomes but also significantly enhance overall medical quality [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Especially in the diagnosis and treatment of ocular fundus lesions, the use of artificial intelligence (AI) applications such as optical coherence tomography (OCT) and intelligent image processing techniques has become a key tool for effectively improving diagnostic accuracy and efficiency [ 6 – 10 ]. For example, the CDSS developed by Vellakani S et al based on OCT technology can assist ophthalmologists in more accurately detecting and classifying eye diseases [ 11 ], while the cloud-based CDSS developed by Tanya S M et al facilitates remote diagnosis and treatment, demonstrating the wide range of applications and potential of CDSS in ophthalmology [ 5 ]. However, despite significant progress in AI technology for image analysis, the value of large volumes of medical record text data is also worth exploring.…”

Section: Introductionmentioning

confidence: 99%

Improve the efficiency and accuracy of ophthalmologists’ clinical decision-making based on AI technology

Guo,

Huang,

Sheng

et al. 2024

BMC Med Inform Decis Mak

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Background As global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform’s effectiveness in enhancing doctors’ diagnostic efficiency and accuracy. Methods In this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the platform in improving diagnostic efficiency and accuracy was evaluated through a controlled experiment comparing experienced and junior doctors. Results The use of the diagnostic intelligent decision support platform resulted in significant improvements in both diagnostic efficiency and accuracy for both experienced and junior doctors (P < 0.05). Notably, the gap in diagnostic speed and precision between junior doctors and experienced doctors narrowed considerably when the platform was used. Although the platform also provided some benefits to experienced doctors, the improvement was less pronounced compared to junior doctors. Conclusion The diagnostic intelligent decision support platform established in this study, based on the XGBoost algorithm and NER, effectively enhances the diagnostic efficiency and accuracy of junior doctors in ocular fundus diseases. This has significant implications for optimizing clinical diagnosis and treatment.

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Perceptions and attitudes of nurse practitioners toward artificial intelligence adoption in health care

Rony,

Numan,

Johra

et al. 2024

Health Science Reports

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BackgroundWith the ever‐increasing integration of artificial intelligence (AI) into health care, it becomes imperative to gain an in‐depth understanding of how health care professionals, specifically nurse practitioners, perceive and approach this transformative technology.ObjectivesThis study aimed to gain insights into nurse practitioners' perceptions and attitudes toward AI adoption in health care.MethodsThis qualitative research employed a descriptive and phenomenological approach using in‐depth interviews. Data were collected through a semi‐structured questionnaire with 37 nurse practitioners selected through purposive sampling, specifically Maximum Variation Sampling and Expert Sampling techniques, to ensure diversity in characteristics. Trustworthiness of the research was maintained through member checking and peer debriefing. Thematic analysis was employed to uncover recurring themes and patterns in the data.ResultsThe thematic analysis revealed nine main themes that encapsulated nurse practitioners' perceptions and attitudes toward AI adoption in health care. These included nurse practitioners' perceptions of AI implementation, attitudes toward AI adoption, patient‐centered care and AI, quality of health care delivery and AI, ethical and regulatory aspects of AI, education and training needs, collaboration and interdisciplinary relationships, obstacles in integrating AI, and AI and health care policy. While this study found that nurse practitioners held a wide range of perspectives, with many viewings AI as a tool to enhance patient care.ConclusionsThis research provides a valuable contribution to the evolving discourse surrounding AI adoption in health care. The findings underscore the necessity for comprehensive education and training in AI, accompanied by clear and robust ethical and regulatory guidelines to ensure the responsible integration of AI in health care practice. Furthermore, fostering collaboration and interdisciplinary relationships is pivotal for the successful incorporation of AI in health care. Policymakers should also address the challenges and opportunities that AI presents in the health care sector. This study enhances the ongoing conversation on AI adoption in health care by shedding light on the perspectives of nurses, thereby shaping future strategies for AI integration.

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Development of a Cloud-Based Clinical Decision Support System for Ophthalmology Triage Using Decision Tree Artificial Intelligence

Cited by 9 publications

References 21 publications

Evaluating the Efficacy of Teleophthalmology in Delivering Ophthalmic Care to Underserved Populations: A Literature Review

Evaluating the Efficacy of Teleophthalmology in Delivering Ophthalmic Care to Underserved Populations: A Literature Review

Improve the efficiency and accuracy of ophthalmologists’ clinical decision-making based on AI technology

Perceptions and attitudes of nurse practitioners toward artificial intelligence adoption in health care

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