Fully Interactive Lungs Auscultation with AI Enabled Digital Stethoscope

Grzywalski, Tomasz; Belluzzo, Riccardo; Piecuch, M.; Szajek, Marcin; Bręborowicz, Anna; Pastusiak, Anna; Hafke-Dys, Honorata; Kociński, Jędrzej

doi:10.1007/978-3-030-21642-9_5

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…The study in Reference 125 proposed a Noise Masking Recurrent Neural Network (NMRNN) for respiratory sound classification, the work in Reference 126 used the Support Vector Machine (SVM) model for pediatric breath sound classification, 126 while the study in Reference 127 used a back‐propagation neural network for the detection of lung sound signals. In Reference 128, a convolutional recurrent neural network (CRNN) with a reinforcement learning (RL) agent was proposed for lungs auscultation examination, whereas a novel CNN architecture called the N‐CNN model was used in conjunction with the VGG‐16 and ResNet50 CNN architectures to measure the pain in new‐borns on based on crying sounds in Reference 129. The findings of the experiments showed that using the N‐CNN model for measuring pain in neonates has significant therapeutic value and has proven to be a good alternative to conventional evaluation methods.…”

Section: Acoustic Aimentioning

confidence: 99%

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

Shinde,

Patil,

Kotecha

et al. 2023

Trans Emerging Tel Tech

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Healthcare institutions are progressively integrating artificial intelligence (AI) into their operations. The extraordinary potential of AI is restricted by insufficient medical data for AI model training and adversarial attacks wherein attackers perturb the dataset by adding some noise to it, which leads to the malfunctioning of the AI models, and a lack of trust caused by the opaque operational approach it employs. This Systematic Literature Review (SLR) is a state‐of‐the‐art survey of the research on blockchain technology for securing AI‐integrated healthcare applications. The most relevant articles from the Scopus and Web of Science (WoS) databases were identified using the PRISMA model. Most of the existing literature is about protecting the healthcare data used by AI‐based healthcare systems using blockchain technology, but the modality of data (text, images, audio, and sound) was not specifically mentioned. Information on protecting the training phase and model deployment for AI‐based healthcare systems considering the variations in feature extraction based on the modality of data was also not clearly specified. Hence, the three subfields of AI, namely, natural language processing (NLP), computer vision, and acoustic AI are further studied to identify security loopholes in its implementation pipeline. The three phases, namely the dataset, the training phase, and the trained models need to be protected from adversaries to avoid malfunctioning of the deployed AI models. The nature of the data processed by NLP, computer vision, and acoustic AI, underlying deep neural network (DNN) architectures, the complexity of attacks, and the perceivability of attacks by humans are analyzed to identify the need for security. A blockchain solution for AI‐based healthcare systems is synthesized based on the findings that have demonstrated the distinctive technological features of blockchains. It offers a solution for the privacy and security issues encountered by NLP, computer vision, and acoustic AI to boost the widespread adoption of AI applications in healthcare.

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Section: Acoustic Aimentioning

confidence: 99%

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

Shinde,

Patil,

Kotecha

et al. 2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…We presented them with a fictional scenario in which we exposed them to information about the mobile medical application and a fictional diagnosis from that application. We decided to use the existing health care application -StethoMe, which was introduced by a Polish start-up (Grzywalski et al, 2019). This application is an AI-based system that a patient can use to continuously monitor the state of the body.…”

Section: Research Overviewmentioning

confidence: 99%

“…The promise of artificial intelligence (AI) in health care offers substantial opportunities to improve patient and clinical team outcomes, reduce costs, and influence the health of the general population. From the patient's point of view, it enables constant monitoring of the body's condition, and analysis of the results through comparison with a vast database containing information no doctor or human analyst would have time to process (Grzywalski, et al, 2019). From the doctor's point of view, it allows them to diagnose patients without having to have contact with them.…”

Section: Introductionmentioning

confidence: 99%

Is AI Augmenting or Substituting Humans?

Gaczek

Leszczyński

Zieliński

2021

International Journal of Technology and Human Interaction

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In this paper, the authors focus on Artificial Intelligence as a tangible technology that is designed to sense, comprehend, act, and learn. There are two manifestations of AI in the medical service: an algorithm that analyzes and interprets the test result and a virtual assistant that communicates the result to the patient. The aim of this paper is to consider how AI can substitute a doctor in measuring human health and how the interaction with virtual assistant impacts one’s visual attention processes. Theoretically, the article refers to the following research strands: Human-Computer Interaction, technology in services, implementation of AI in the medical sector, and behavioral economy. By conducting an eye-tracking experimental study, it is demonstrated that the perception of medical diagnosis does not differ across experimental groups (human vs. AI). However, it is observed that participants exposed to AI-based assistant focused more on button allowing to contact a real doctor.

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Fully Interactive Lungs Auscultation with AI Enabled Digital Stethoscope

Cited by 2 publications

References 6 publications

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

Is AI Augmenting or Substituting Humans?

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