The Epidemiology of Infectious Diseases Meets AI: A Match Made in Heaven

Bothra, Ankur; Cao, Yongguo; Černý, Jiří; Arora, Gunjan

doi:10.3390/pathogens12020317

Cited by 4 publications

(3 citation statements)

References 18 publications

(19 reference statements)

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“…На этом фоне возможности ТИИ в организации здравоохранения и изучении общественного здоровья практически не разработаны. Опубликованы отдельные статьи концептуального и общетеоретического характера, есть единичные сообщения о применении ТИИ для анализа больших данных (в том числе поисковых запросов в интернет), связанных со здоровьем [35][36][37][38][39][40][41][42]. На этом фоне наше популяционное эпидемиологическое исследование с применением технологий искусственного интеллекта отличается новизной.…”

Section: Discussionunclassified

A population study of paracardial fat as a risk factor for cardiovascular diseases (based on the data of the Moscow experiment on the use of computer vision in radiodiagnosis)

Vasilev,

Goncharova,

Vladzymyrskii

et al. 2023

Science and Innovations in Medicine

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Aim to study the prevalence of paracardial fat as a risk factor for cardiovascular diseases in Moscow population using an automated analysis of the results of radiological examinations. Material and methods. The research was designed as descriptive, retrospective epidemiological study. The results of chest computed tomography of 113,408 patients served as the study data. The data was analyzed by AI services in an automated mode for the presence of paracardial fat and calculation of its volume. Results. The paracardial fat was detected in 66.5% of the examined persons. The proportion of men was 45.7%, women 54.3% (p0.001). The volume of paracardial fat fluctuated in the range from 1.0 to 1517.0 ml; the average value was 282.1 ml. The average volume of paracardial fat in men (326.0 ml) was significantly larger than in women (244.7 ml) in each age group. The clinically significant volume of paracardial fat (200 ml) was detected in 33,081 individuals (in 64.0% of people having this risk factor). The risk factor was clinically significant in 71.1% of men and in 57.9% of women (p0.001). Conclusion. The prevalence of paracardial fat in Moscow population was 5.97 per 1000 individuals. A clinically significant volume of paracardial fat was most often found in both sexes in the elderly (78.7%) and senile age groups (78.2%). Each 5 years of age increased the probability of this risk factor incidence by 1.282 times in general; and the risk of developing its clinical form by 2.981 times in particular.

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Section: Discussionunclassified

A population study of paracardial fat as a risk factor for cardiovascular diseases (based on the data of the Moscow experiment on the use of computer vision in radiodiagnosis)

Vasilev,

Goncharova,

Vladzymyrskii

et al. 2023

Science and Innovations in Medicine

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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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“…• Analyzing data and images [35,44,66,90,108,117,124,126,151,163]. • Interpreting data and images [13,14,21,42].…”

Section: Introduction: Exponential Growth In the Use Of Artificial In...mentioning

confidence: 99%

The Ethics of Using Artificial Intelligence in Scientific Research: New Guidance Needed for a New Tool

Resnik,

Hosseini

2023

Preprint

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The use of artificial intelligence (AI) in research offers many important benefits for science and society but also creates some novel and complex ethical issues. While the issues raised by AI use will not necessitate a radical change in the established ethical norms of science, they will require the scientific community to develop new guidance for the appropriate use of AI. In this article, we provide a brief introduction to AI and how it can be used in research, examine some of the ethical issues raised by using AI in research, and offer recommendations for appropriate use of this technology. We recommend that: 1) researchers and software developers are responsible for identifying, describing, reducing, and controlling AI-related biases and random errors; 2) researchers should disclose and explain their use of AI in language that can be understood by non-experts; 3) if appropriate, researchers should engage with impacted communities, populations, and other stakeholders concerning the use of AI in research to obtain their advice and assistance and address their interests and concerns; 4) researchers may be liable for misconduct if they intentionally, knowingly, or recklessly use AI to fabricate or falsify data or commit plagiarism; 5) AI systems should not be named authors, inventors, or copyright holders but their contributions to research should be disclosed and described; 6) AI systems should not be used in situations that may involve unauthorized disclosure of confidential information related to human research subjects, unpublished research, potential intellectual property claims, or proprietary or classified research; and 7) education and mentoring in responsible conduct of research should include discussion of ethical use of AI.

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The Epidemiology of Infectious Diseases Meets AI: A Match Made in Heaven

Abstract: Infectious diseases remain a major threat to public health [...]

Cited by 4 publications

References 18 publications

A population study of paracardial fat as a risk factor for cardiovascular diseases (based on the data of the Moscow experiment on the use of computer vision in radiodiagnosis)

A population study of paracardial fat as a risk factor for cardiovascular diseases (based on the data of the Moscow experiment on the use of computer vision in radiodiagnosis)

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

The Ethics of Using Artificial Intelligence in Scientific Research: New Guidance Needed for a New Tool

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