Perspective of Artificial Intelligence in Disease Diagnosis: A Review of Current and Future Endeavours in the Medical Field

Abstract

Artificial intelligence (AI) has demonstrated significant promise for the present and future diagnosis of diseases. At the moment, AI-powered diagnostic technologies can help physicians decipher medical pictures like X-rays, magnetic resonance imaging, and computed tomography scans, resulting in quicker and more precise diagnoses. In order to make a prospective diagnosis, AI algorithms may also examine patient information, symptoms, and medical background. The application of AI in disease diagnosis is anticipated to grow as the field develops. In the future, AI may be used to find patterns in enormous volumes of medical data, aiding in disease prediction and prevention before symptoms appear. Additionally, by combining genetic data, lifestyle data, and environmental variables, AI may help in the diagnosis of complicated diseases. It is crucial to remember that while AI can be a powerful tool, it cannot take the place of qualified medical personnel. Instead, AI ought to support and improve diagnostic procedures, enhancing patient care and healthcare results. Future research and the use of AI for disease diagnosis must take ethical issues, data protection, and ongoing model validation into account.

A Novel Automatic Algorithm to Support Lung Ultrasound Non-Expert Physicians in Interstitial Pneumonia Evaluation: A Single-Center Study

“…For example, the process of anamnesis is difficult to fix in an automated manner; this issue becomes even more evident in patients with multiple comorbidities, where different aspects of the condition emerge progressively as more exams are performed, and results become available. Diagnostic evaluation is the main clinical application for AI, ranging from pathology to imaging [13,14,[17][18][19][20]. Several authors have studied the impact of ultrasound on clinical management and patient outcomes in recent years, in particular since the COVID-19 pandemic [21][22][23].…”

“…Lastly, the study pointed out that in patients with reduced DLCO, Lung Staging classifications were significantly higher compared to those with preserved DLCO (reduced median 1 [1][2] vs. preserved 0 [0-1], p = 0.001). The overlap in results with the LUS score in this subgroup underscores the diagnostic efficacy of Lung Staging (reduced median 18 [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] vs. preserved 5.5 [2][3][4][5][6][7][8][9], p = 0.035). This is particularly true in cases where conventional lung function tests may be inconclusive, supporting a pneumologist nonexpert in LUS to have the imaging evaluation of ILD severity (Figure 6).…”

“…Therefore, innovative AI and other automatic algorithms may help in this issue [13], as they can be trained/developed to identify and highlight abnormalities, lesions, or structures of interest in medical images, assisting operators in their interpretation [14].…”

Evaluation of ChatGPT-Generated Differential Diagnosis for Common Diseases With Atypical Presentation: Descriptive Research

“…It is particularly revealing that in the more atypical presentations of common diseases (C3 and C4), the AI struggled to provide a correct diagnosis, even within the top 5 differential diagnoses, with concordance rates of 25% and 17%, respectively. These categories highlight the current limitations of AI in medical diagnosis when faced with cases that deviate significantly from the established patterns within its training data [ 27 ].…”