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Rapid Communication - Journal of Biomedical Imaging and Bioengineering (2023) Volume 7, Issue 4
Artificial Intelligence(AI)-enabled pneumonia detection and treatment.
Selena Oliva*
Department of Radiology, University of Bristol, United Kingdom
- *Corresponding Author:
- Paige Greco
Department of Radiology,
University of Bristol,
United Kingdom
E-mail: olivaselena21@hotmail.com
Received: 26-Jul-2023, Manuscript No. AABIB-23-109268; Editor assigned: 28-Jul-2023, PreQC No. AABIB-23-109268 (PQ); Reviewed: 11-Aug-2023, QC No. AABIB-23-109268; Revised: 11-Aug-2023, Manuscript No. AABIB-23-109268; Published: 23-Aug-2023, DOI:10.35841/aabib-7.4.186
Citation: Oliva S, Artificial Intelligence(AI)-Enabled Pneumonia Detection and Treatment. J Biomed Imag Bioeng. 2023;7(4):186.
Introduction
The intersection of artificial intelligence (AI) and healthcare has ushered in a new era of medical diagnostics and treatment strategies. Pneumonia, a prevalent and potentially life-threatening respiratory infection, stands to benefit significantly from the integration of AI techniques. The traditional methods of diagnosing and managing pneumonia often rely on radiological imaging, clinical assessments, and laboratory tests, which can sometimes be time-consuming and subject to interpretation variability. AI, with its ability to analyze complex data patterns and make predictions, has emerged as a powerful tool in enhancing pneumonia detection, classification, and treatment decisions. This article delves into the transformative role of AI in enabling more accurate and efficient pneumonia detection and treatment[1].
AI-enabled pneumonia detection and treatment mark a paradigm shift in the healthcare landscape. Pneumonia, characterized by lung inflammation usually caused by infections, demands rapid and precise diagnosis to ensure timely intervention. AI brings to the table the capability to process vast amounts of medical data, including radiological images, clinical records, and even genetic information, to provide comprehensive insights. AI techniques, particularly deep learning algorithms, excel at recognizing intricate patterns in medical images such as chest X-rays and CT scans. These algorithms can swiftly identify pneumonia-related anomalies, aiding radiologists in their assessments and potentially reducing diagnostic errors. Moreover, AI facilitates the automation of image interpretation, enabling healthcare providers to allocate their expertise more efficiently[2].
The advantages of AI extend beyond diagnosis. Treatment strategies can also be enhanced through AI-driven predictive analytics. By analyzing historical patient data, AI algorithms can assist clinicians in forecasting disease progression, optimizing medication regimens, and tailoring treatment plans to individual patient profiles. This personalized approach not only improves patient outcomes but also minimizes adverse effects and unnecessary interventions. Chest radiographs (CRs) are recommended by the AWMF (Guidance Manual and Rules for Guideline Development) for individuals who are clinically suspected of having community-acquired or hospital-acquired pneumonia. As a result, patients in the emergency department who have dyspnea or other respiratory symptoms suggestive of pneumonia are frequently given a CR. In comparison to other imaging modalities, such as computed tomography (CT), CRs have lower radiation exposure, faster feasibility, and superior equipment portability. This diagnostic test can provide additional and timely information on a patient's cardiopulmonary status and any changes (acute and chronic) caused by COVID-19 infection. Furthermore, the ongoing COVID-19 epidemic has posed a challenge to healthcare systems all across the world since December 2019. As a result, the number of CRs is increasing. Early detection and isolation of patients is critical in the context of high levels of infection and a rising number of variants of concern. This is especially difficult for radiology departments. According to studies, earlier reporting of pneumonia in CRs reduces the median length of hospital stays, increases the likelihood of receiving adequate therapy, and reduces the risk of infectious transmission[3].
Despite pulmonary CT's better accuracy, CRs remain one of the most essential imaging modalities in emergency departments globally due to their ease of use. Accelerating the reporting of CRs with significant anomalies is critical since it can lead to speedier treatment and, if necessary, patient seclusion. Consolidations and opacifications are thought to be induced mostly by infectious illnesses in CRs. However, even among radiologists, the interpretation of CR, particularly in the case of pneumonia, varies greatly[4].
However, the integration of AI in pneumonia management is not without challenges. Ensuring the quality and diversity of training data, addressing biases in algorithms, and maintaining patient privacy are critical considerations. Collaborations between healthcare professionals and AI experts are essential to refine and validate these AI systems to ensure their clinical applicability and safety [5].
Conclusion
AI-enabled pneumonia detection and treatment represent a leap forward in healthcare capabilities. The amalgamation of AI's analytical prowess with medical expertise holds the potential to revolutionize pneumonia care by expediting accurate diagnosis and refining personalized treatment strategies. As AI continues to evolve, its impact on pneumonia management offers a glimpse into a future where technology works seamlessly alongside healthcare providers to improve patient outcomes and enhance medical practices.
References
- Huang S, Yang J, Fong S, et al.Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives. Int J Biol Sci. 2021;17(6):1581.
- Borkowski AA, Viswanadhan NA, Thomas LB, et al. Using artificial intelligence for COVID-19 chest X-ray diagnosis. Federal Practitioner. 2020;37(9):398.
- Dorr F, Chaves H, Serra MM, et al. COVID-19 pneumonia accurately detected on chest radiographs with artificial intelligence. Intell-Based Med. 2020;3:100014.
- Patel BN, Rosenberg L, Willcox G, et al. Human–machine partnership with artificial intelligence for chest radiograph diagnosis. NPJ Digit Med. 2019;2(1):111.
- Kwon T, Lee SP, Kim D, et al. Diagnostic performance of artificial intelligence model for pneumonia from chest radiography. Plos one. 2021;16(4):e0249399.