Artificial intelligence and thyroid disease management

Gruson, Damien; Dabla, Pradeep Kumar; Stanković, Sanja; Homšak, Evgenija; Gouget, Bernard; Bernardini, Sergio; Macq, Benoı̂t

doi:10.11613/bm.2022.020601

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…The use of integrated technologies in thyroid models has been relatively successful. For example, AI was shown to support optimized examination of thyroid function, results evaluation, and guidance for clinical decision making by collecting related multi‐center data [37].…”

Section: Construction Of a Patient‐centered Medicine System Through T...mentioning

confidence: 99%

Clinical application of intelligent technologies and integration in medical laboratories

Huang

Ding

et al. 2023

iLABMED

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With the development of scientific technology, the transition to the intelligent era of digitalization and automation is an irresistible trend for medical laboratories. Medical diagnosis systems have undergone significant changes as a result of intelligent technologies, such as machine learning, artificial intelligence, and the Internet of Things, from the collection, transmission, and detection of test samples to the review of reports and the provision of clinical feedback. In addition to significantly enhancing the efficiency, consistency, and accuracy of medical laboratory testing, these technologies also assist the improvement of individualized healthcare and medical expert systems, as well as the early detection and treatment of diseases. The future development of medical laboratories will focus on integrating big data and diverse intelligent resources, cooperating more closely with clinical departments, and realizing the effective pathway of patient‐centered care. The purpose of this review is to illustrate the current state of intelligent technology integration in medical laboratories and provide a preliminary discussion about the potential future influences of intelligent technology development on the evolution of medical laboratories.

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Section: Construction Of a Patient‐centered Medicine System Through T...mentioning

confidence: 99%

Clinical application of intelligent technologies and integration in medical laboratories

Huang

Ding

et al. 2023

iLABMED

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“…(2) Neurodegenerative diseases [55,49]: while not directly identifiable through text, subtle changes in typing speed and fine motor skills required for typing might provide early clues of Parkinson's disease [39,54,1,20]; (3) Sleep disorders [73,66]: late-night time stamps and content that indicates restlessness or consistent complaints about lack of sleep may be suggestive of insomnia; (4) Infectious diseases [71,12,72,16]: it is less likely to identify infectious diseases from text messages unless the content explicitly describes symptoms or experiences related to the infectious disease [71]; (5) Chronic diseases [41,43,45]: if someone frequently discusses feelings of tiredness, changes in weight, or other symptomatic experiences, this could indirectly hint at chronic conditions like diabetes or thyroid issues [31]. While text message analysis may provide signals indicative of a health issue [58,75], this method is far from diagnostic so this can be just a preventive supportive tool for specialists (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis

Biró,

Cuesta-Vargas,

Szilágyi

2023

Acta Universitatis Sapientiae, Informatica

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The paper examines the potential of artificial intelligence (AI) in parsing text and conducting sentiment analysis to identify early markers of mental health and neurodegenerative disorders. Through the analysis of textual data, we investigate whether AI can provide a noninvasive, continuous, and objective complement to traditional diagnostic practices. Background: the early detection of mental health (such as depression, anxiety, psychotic disorders, Alzheimer’s disease and dementia) and neurodegenerative disorders (like Parkinson’s disease) remains a critical challenge in clinical practice. Traditional diagnostic methods rely on clinical evaluations that may be subjective and episodic. Recent advancements in AI and natural language processing (NLP) have opened new avenues for precognitive health assessments, suggesting that variations in language and expressed sentiments in written text can serve as potential biomarkers for these conditions. Materials and Methods: the research used a dataset comprising various forms of textual data, including anonymized social media interactions, transcripts from patient interviews, and electronic health records. NLP algorithms were deployed to parse the text, and machine learning models were trained to identify language patterns and sentiment changes. The study also incorporated a sentiment analysis to gauge emotional expression, a key component of mental health diagnostics. Results: the AI models were able to identify language use patterns and sentiment shifts that correlated with clinically validated instances of mental health symptoms and neurodegenerative conditions. Notably, the models detected an increased use of negative a ect words, a higher frequency of first-person singular pronouns, and a decrease in future tense in individuals with depression. For neurode-generative conditions, there was a notable decline in language complexity and semantic coherence over time. Conclusions: the implemented pipeline of AI-parsed text and sentiment analysis appears to be a promising tool for the early detection and ongoing monitoring of mental health and neurodegenerative disorders. However, these methods are supplementary and cannot replace the nuanced clinical evaluation process. Future research must refine the AI algorithms to account for linguistic diversity and context, while also addressing ethical considerations regarding data use and privacy. The integration of AI tools in clinical settings necessitates a multidisciplinary approach, ensuring that technological advancements align with patient-centered care and ethical standards.

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Prediction of Thyroid Disease using Deep Learning Techniques

Pallavi

Singh

2023

2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)

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Artificial intelligence and thyroid disease management

Cited by 3 publications

References 38 publications

Clinical application of intelligent technologies and integration in medical laboratories

Clinical application of intelligent technologies and integration in medical laboratories

Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis

Prediction of Thyroid Disease using Deep Learning Techniques

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