From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare

Chakraborty, Chiranjib; Bhattacharya, Manojit; Pal, Soumen; Lee, Sang-Soo

doi:10.1016/j.crbiot.2023.100164

Cited by 9 publications

(2 citation statements)

References 188 publications

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“… 6 However, it is important to consider the positive and negative effects of mobile health applications, especially regarding privacy and trust. 7 By developing adaptable AI solutions that consider the challenges and needs of diverse healthcare systems, as discussed recently in-depth by Wang and Preininger 8 and Chakraborty et al 9 these applications can have a transformative impact on healthcare access and outcomes globally. Understanding and addressing these varied global needs is crucial for the responsible and effective implementation of AI in healthcare worldwide.…”

Section: Introductionmentioning

confidence: 99%

Transforming Healthcare with AI: Promises, Pitfalls, and Pathways Forward

Shuaib

2024

IJGM

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This perspective paper provides a comprehensive examination of artificial intelligence (AI) in healthcare, focusing on its transformative impact on clinical practices, decision-making, and physician-patient relationships. By integrating insights from evidence, research, and real-world examples, it offers a balanced analysis of AI’s capabilities and limitations, emphasizing its role in streamlining administrative processes, enhancing patient care, and reducing physician burnout while maintaining a human-centric approach in medicine. The research underscores AI’s capacity to augment clinical decision-making and improve patient interactions, but it also highlights the variable impact of AI in different healthcare settings. The need for context-specific adaptations and careful integration of AI technologies into existing healthcare workflows is emphasized to maximize benefits and minimize unintended consequences. Significant attention is given to the implications of AI on the roles and competencies of healthcare professionals. The emergence of AI necessitates new skills in data literacy and technology use, prompting a shift in educational curricula towards digital health and AI training. Ethical considerations are a pivotal aspect of the discussion. The paper explores the challenges posed by data privacy concerns, algorithmic biases, and ensuring equitable access to AI-driven healthcare. It advocates for the development of comprehensive ethical frameworks and ongoing research to guide the responsible use of AI in healthcare. Conclusively, the paper advocates for a balanced approach to AI adoption in healthcare, highlighting the importance of ongoing research, strategic implementation, and the synergistic combination of human expertise with AI technologies for optimal patient care.

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Section: Introductionmentioning

confidence: 99%

Transforming Healthcare with AI: Promises, Pitfalls, and Pathways Forward

Shuaib

2024

IJGM

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“…Utilizing deep learning for the diagnosis and monitoring of otitis in children has the potential to revolutionize data collection and analysis in epidemiological studies. Firstly, deep learning enables the efficient processing of large volumes of unstructured data, such as clinical notes and medical reports, which is particularly advantageous in areas with extensive and complex datasets [9]. Secondly, automatic classification enhances accuracy and consistency in case categorization.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Epidemiology of Otitis Using Free-Text Pediatric Medical Notes: A Deep Learning Approach

Lanera,

Lorenzoni,

Barbieri

et al. 2023

JPM

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Free-text information represents a valuable resource for epidemiological surveillance. Its unstructured nature, however, presents significant challenges in the extraction of meaningful information. This study presents a deep learning model for classifying otitis using pediatric medical records. We analyzed the Pedianet database, which includes data from January 2004 to August 2017. The model categorizes narratives from clinical record diagnoses into six types: no otitis, non-media otitis, non-acute otitis media (OM), acute OM (AOM), AOM with perforation, and recurrent AOM. Utilizing deep learning architectures, including an ensemble model, this study addressed the challenges associated with the manual classification of extensive narrative data. The performance of the model was evaluated according to a gold standard classification made by three expert clinicians. The ensemble model achieved values of 97.03, 93.97, 96.59, and 95.48 for balanced precision, balanced recall, accuracy, and balanced F1 measure, respectively. These results underscore the efficacy of using automated systems for medical diagnoses, especially in pediatric care. Our findings demonstrate the potential of deep learning in interpreting complex medical records, enhancing epidemiological surveillance and research. This approach offers significant improvements in handling large-scale medical data, ensuring accuracy and minimizing human error. The methodology is adaptable to other medical contexts, promising a new horizon in healthcare analytics.

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2MGAS-Net: multi-level multi-scale gated attentional squeezed network for polyp segmentation

Bakkouri,

Bakkouri

2024

SIViP

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From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare

Cited by 9 publications

References 188 publications

Transforming Healthcare with AI: Promises, Pitfalls, and Pathways Forward

Transforming Healthcare with AI: Promises, Pitfalls, and Pathways Forward

Monitoring the Epidemiology of Otitis Using Free-Text Pediatric Medical Notes: A Deep Learning Approach

2MGAS-Net: multi-level multi-scale gated attentional squeezed network for polyp segmentation

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