Eyeing a Role for Artificial Intelligence in Clinical Trials

Singh, Mandeep S.

doi:10.1001/jamaophthalmol.2022.6542

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…One potential application could be clinical trials for rare conditions, such as Retinitis pigmentosa. The rarity of such conditions often makes it challenging to conduct traditional clinical trials due to the lack of a large patient sample or ethical issues [51]. However, with the generation of multiple DTs reproducing the specifics of these rare diseases, it would become possible to test new treatments within a digital environment.…”

Section: Case Study: Developing a Dt Of The Eyementioning

confidence: 99%

Perspective Chapter: Digital Twins for Health – Opportunities, Barriers and a Path Forward

Armeni,

Polat,

Maria De Rossi

et al. 2023

Industrial Engineering and Management

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The concept of precision medicine involves tailoring medical interventions to each patient’s specific needs, considering factors such as their genetic makeup, lifestyle, environment and response to therapies. The emergence of digital twin (DT) technology is anticipated to enable such customization. The healthcare field is, thus, increasingly exploring the use of digital twins (DTs), benefiting from successful proof of concept demonstrated in various industries. If their full potential is realized, DTs have the capability to revolutionize connected care and reshape the management of lifestyle, health, wellness and chronic diseases in the future. However, the realization of DTs’ full potential in healthcare is currently impeded by technical, regulatory and ethical challenges. In this chapter, we map the current applications of DTs in healthcare, with a primary focus on precision medicine. We also explore their potential applications in clinical trial design and hospital operations. We identify the key enablers of DTs in healthcare and discuss the opportunities and barriers that foster or hinder their larger and faster diffusion. By providing a comprehensive view of the current landscape, opportunities and challenges, we aim to contribute to DTs’ ongoing development and help policymakers facilitate the growth of DTs’ application in healthcare.

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Section: Case Study: Developing a Dt Of The Eyementioning

confidence: 99%

Perspective Chapter: Digital Twins for Health – Opportunities, Barriers and a Path Forward

Armeni,

Polat,

Maria De Rossi

et al. 2023

Industrial Engineering and Management

View full text Add to dashboard Cite

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Evaluating ChatGPT's efficacy and readability to common pediatric ophthalmology and strabismus-related questions

Ahmed,

Thrishulamurthy

2024

European Journal of Ophthalmology

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Introduction The rise in popularity of chatbots, particularly ChatGPT by OpenAI among the general public and its utility in the healthcare field is a topic of present controversy. The current study aimed at assessing the reliability and accuracy of ChatGPT's responses to inquiries posed by parents, specifically focusing on a range of pediatric ophthalmological and strabismus conditions. Methods Patient queries were collected via a thematic analysis and posed to ChatGPT 3.5 version across 3 unique instances each. The questions were divided into 12 domains totalling 817 unique questions. All responses were scored on the response quality by two experienced pediatric ophthalmologists in a Likert-scale format. All questions were evaluated for readability using the Flesch-Kincaid Grade Level (FKGL) and character counts. Results A total of 638 (78.09%) questions were scored to be perfectly correct, 156 (19.09%) were scored correct but incomplete and only 23 (2.81%) were scored to be partially incorrect. None of the responses were scored to be completely incorrect. Average FKGL score was 14.49 [95% CI 14.4004–14.5854] and the average character count was 1825.33 [95%CI 1791.95–1858.7] with p = 0.831 and 0.697 respectively. The minimum and maximum FKGL scores were 10.6 and 18.34 respectively. FKGL predicted character count, R²=.012, F(1,815) = 10.26, p = .001. Conclusion ChatGPT provided accurate and reliable information for a majority of the questions. The readability of the questions was much above the typically required standards for adults, which is concerning. Despite these limitations, it is evident that this technology will play a significant role in the healthcare industry.

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Eyeing a Role for Artificial Intelligence in Clinical Trials

Cited by 2 publications

References 5 publications

Perspective Chapter: Digital Twins for Health – Opportunities, Barriers and a Path Forward

Perspective Chapter: Digital Twins for Health – Opportunities, Barriers and a Path Forward

Evaluating ChatGPT's efficacy and readability to common pediatric ophthalmology and strabismus-related questions

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