From wearables to THINKables: artificial intelligence-enabled sensors for health monitoring

Mirmomeni, Mahtab; Fazio, Timothy; Cavallar, Stefan von; Harrer, Stefan

doi:10.1016/b978-0-12-819246-7.00012-7

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…An example is disparities in the distribution and access to COVID-19 vaccines in the United States due to disharmonized priority settings in the algorithm [ 70 ]. Data privacy and security, transparency, fairness of the analytical AI models, and limited computing power of sensing processors are a few other technical and regulatory challenges with wearable sensors with AI-at-the-edge functionality [ 71 ]. Phenotypic personalized medicine (PPM) aims to provide a quantitative representation of an individual patient’s overall health, while directly translating this knowledge into clinical care helps to find a suitable drug combination quadratic phenotypic optimization platform (QPOP) and dosing (CURATE.AI) based on data from previously treated patients.…”

Section: Reviewmentioning

confidence: 99%

Section: Artificial Intelligence-enabled Patient Monitoring and Disea...mentioning

confidence: 99%

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Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine

Iqbal,

Cortés Jaimes,

Makineni

et al. 2023

Cureus

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Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI’s precision and analysis regarding patients’ genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI’s contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI’s capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI’s impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.

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Section: Reviewmentioning

confidence: 99%

Section: Artificial Intelligence-enabled Patient Monitoring and Disea...mentioning

confidence: 99%

Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine

Iqbal,

Cortés Jaimes,

Makineni

et al. 2023

Cureus

View full text Add to dashboard Cite

show abstract

“…These devices use AI-supported algorithms to gather and analyze health data, including heart rate, sleep patterns, activity levels, and calorie consumption. Based on this information, personalized recommendations can be made to help individuals improve their well-being [ 11 ]. Although the use of medical AI systems remains in its early stages, ongoing research and development efforts are being undertaken worldwide.…”

Section: Introductionmentioning

confidence: 99%

Assessing AI Awareness and Identifying Essential Competencies: Insights From Key Stakeholders in Integrating AI Into Medical Education

Moldt,

Festl-Wietek,

Fuhl

et al. 2024

JMIR Med Educ

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Background The increasing importance of artificial intelligence (AI) in health care has generated a growing need for health care professionals to possess a comprehensive understanding of AI technologies, requiring an adaptation in medical education. Objective This paper explores stakeholder perceptions and expectations regarding AI in medicine and examines their potential impact on the medical curriculum. This study project aims to assess the AI experiences and awareness of different stakeholders and identify essential AI-related topics in medical education to define necessary competencies for students. Methods The empirical data were collected as part of the TüKITZMed project between August 2022 and March 2023, using a semistructured qualitative interview. These interviews were administered to a diverse group of stakeholders to explore their experiences and perspectives of AI in medicine. A qualitative content analysis of the collected data was conducted using MAXQDA software. Results Semistructured interviews were conducted with 38 participants (6 lecturers, 9 clinicians, 10 students, 6 AI experts, and 7 institutional stakeholders). The qualitative content analysis revealed 6 primary categories with a total of 24 subcategories to answer the research questions. The evaluation of the stakeholders’ statements revealed several commonalities and differences regarding their understanding of AI. Crucial identified AI themes based on the main categories were as follows: possible curriculum contents, skills, and competencies; programming skills; curriculum scope; and curriculum structure. Conclusions The analysis emphasizes integrating AI into medical curricula to ensure students’ proficiency in clinical applications. Standardized AI comprehension is crucial for defining and teaching relevant content. Considering diverse perspectives in implementation is essential to comprehensively define AI in the medical context, addressing gaps and facilitating effective solutions for future AI use in medical studies. The results provide insights into potential curriculum content and structure, including aspects of AI in medicine.

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“…Progressive reductions in costs, size, and energy requirements of wearable sensors, along with improved activity recognition algorithms, suggest that wearable systems may become ubiquitous tools. For instance, wearables have evolved to being capable of running artificial intelligence algorithms in real-time at the point of sensing, which allows researchers to gain analytical insights directly from measurement data [21]. User-friendliness to maintain long-term user engagement may be boosted by tattoo sensors and implanted sensors transmitting data wirelessly [22].…”

mentioning

confidence: 99%

Wearable Motion Sensors in Older Adults: On the Cutting Edge of Health and Mobility Research

Jansen

Gordt-Oesterwind

Schwenk

2022

Sensors

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From wearables to THINKables: artificial intelligence-enabled sensors for health monitoring

Cited by 7 publications

References 17 publications

Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine

Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine

Assessing AI Awareness and Identifying Essential Competencies: Insights From Key Stakeholders in Integrating AI Into Medical Education

Wearable Motion Sensors in Older Adults: On the Cutting Edge of Health and Mobility Research

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