Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

Mirbabaie, Milad; Stieglitz, Stefan; Frick, Nicholas R. J.

doi:10.1007/s12553-021-00555-5

Cited by 129 publications

(61 citation statements)

References 194 publications

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“…Several functions make AI so useful, such as recognizing patterns and images, understanding all types of language, perceiving relationships and connections, following decision algorithms proposed by experts, being able to understand concepts and not just process data, acquiring reasoning through the ability to integrate new experiences, and, with that, self-improvement by solving problems or performing tasks. AI processes the stored data through algorithms, improves itself through its operation, and proposes increasingly accurate diagnostic hypotheses [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…It is clear that technological advances increasingly interfere in people's daily lives, whether facilitating existing processes or creating new methods for solving problems. Artificial intelligence (AI) is seen as a technology revolutionizing different processes in different organizational contexts [ 2 ]. The times of great technological systems are advancing faster and faster.…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Perception of the Impact of Artificial Intelligence in the Decision‐Making Processes of Public Healthcare Professionals

Ibrahim

Al‐Azzawi²,

Mohamad

et al. 2022

Journal of Environmental and Public Health

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Technologies are increasingly independent and play important roles in society. Artificial intelligence (AI) is a branch of science that can improve various environments and processes. The health sector stands out among these contexts, especially ophthalmology and dentistry. Studies evaluating the impact of using these technologies in these contexts are still developing. There are still few studies that assess how AI can impact the decision-making process of health professionals and how it can improve the quality of care provided to these professionals. In this sense, this study aims to evaluate the perception of the impact of AI on the decision-making process of health professionals and the quality of patient care from the perspective of ophthalmologists and dentists. The methodological strategy used was the application of an online questionnaire with eighteen professionals in these areas. Based on the respondents’ opinions, we sought to assess how these decision-making processes are affected by the use of technologies and how they impact the quality of patient care. As a result, it was observed that AI has become essential and a facilitator of the diagnostic processes. However, it presents some challenges related to cost, accessibility, AI x professional responsibility, and incentive of agreements.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

[Retracted] Perception of the Impact of Artificial Intelligence in the Decision‐Making Processes of Public Healthcare Professionals

Ibrahim

Al‐Azzawi²,

Mohamad

et al. 2022

Journal of Environmental and Public Health

View full text Add to dashboard Cite

show abstract

“…Some AI benefits like fast pattern detection impact medical imaging practices. Using as input imaging studies like mammography for breast cancer detection, CT scans for colon cancer diagnosis, chest radiographs for the detection of pulmonary nodules, MRI for brain tumor segmentation, and the diagnosis of neurologic disorders, such as Alzheimer's disease [6]. On another front, AI and data analytics can also offer great benefits to mitigate pressure on the health system from treats like the COVID-19 pandemic.…”

Section: Augmenting Digital Health With Aimentioning

confidence: 99%

Data Federation in Healthcare for Artificial Intelligence Solutions

Castellanos¹,

Raposo²,

Antúnez³

2022

Studies in Health Technology and Informatics

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Data federation offers a way to get data moving from multiple sources providing advantages in healthcare systems where medical data is often hard to reach because of regulations or the lack of reliable solutions that can integrate on top of protocols like FHIR, HL7, DICOM, among others. Given the increasing need for solutions that augment healthcare systems with artificial intelligence (AI), in fields like genomics, cancer treatment, and radiology, all of which will require solutions that can provide data at scale while being traceable, safe, and regulatory-compliant. This paper proposes an architectural solution that may provide the core capabilities to implement a data federation approach in a healthcare system to enable AI.

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“…The deep learning technology itself is a data-driven recognition process. By adding image data samples with corresponding feature skew, change, and noise, the model can better identify these biological features in nonstandard states to better improve user experience [ 24 ].…”

Section: Related Researchmentioning

confidence: 99%

Construction and Research of Constructive English Teaching Model Applying Multimodal Neural Network Algorithm

Zhang

Wang²

2022

Computational Intelligence and Neuroscience

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This article draws on previous research on constructive English teaching models and uses multimodal neural network algorithm theory and constructive English teaching as the theoretical basis, experimental research method, questionnaire survey method, and evaluation method. In this article, we propose a multimodal neural network consisting of a multiscale FCN module and an LSTM module. The network focuses on both the multiscale geometric spatial features and the numerical time-dependent features of the time series curves, and with the comprehensive knowledge of their characteristics, it can better distinguish the classes to which the series belong. A large-scale perceptual field is achieved by null convolution in the model to ensure that the training pressure does not increase significantly. A series of experiments on the UCR dataset verifies the effectiveness and superiority of the model. Simulation experiments were conducted to build the proposed constructive English teaching model based on a multimodal neural network algorithm, and a test environment was built for use case testing. The experimental results showed that the algorithm can be better applied to constructive English teaching and has better adaptability and accuracy in various scenarios. At the end of the experiment, a posttest of grammar level was conducted in two classes to test whether the constructive English teaching model based on the multimodal neural network model could help students improve their English grammar skills. The results of the data analysis showed that the mean score of the experimental class was significantly higher than that of the control class, and the experimental class showed a more significant improvement, indicating that this new constructive English teaching model was beneficial to improving students’ English grammar skills. The interaction strategy proposed under the constructive English teaching model can effectively improve the interaction between teachers and students. This positive feedback effect can provide front-line teachers with corresponding teaching references.

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Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

Cited by 129 publications

References 194 publications

[Retracted] Perception of the Impact of Artificial Intelligence in the Decision‐Making Processes of Public Healthcare Professionals

[Retracted] Perception of the Impact of Artificial Intelligence in the Decision‐Making Processes of Public Healthcare Professionals

Data Federation in Healthcare for Artificial Intelligence Solutions

Construction and Research of Constructive English Teaching Model Applying Multimodal Neural Network Algorithm

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