التفاصيل البيبلوغرافية
| العنوان: |
The AI Diagnostician: Improving Medical Diagnosis with Artificial Intelligence |
| المؤلفون: |
Farrokhi, Mehrdad, Taheri, Fatemeh, Adibnia, Ehsan, Mehrtabar, Saba, Rassaf, Zahra, Tooyserkani, Seyed Hamed, Rajabloo, Yasamin, Tooyserkani, Ghazal Sadat, Ranjbar, Zohreh, Hashemi, Erfan, Khorsand, Mohammad-Soroush, Aminoleslami, Sima, Sabzehie, Hamed, Taherlou, Shila, Moosavi, Seyede Mahshad, Fatahi, Mansoureh, Taherlou, Azadeh, Kohansal, Erfan, Rezaeirad, Azadeh, Kardan, Mohammadhossein, Boroumandfar, Fatemeh, Garousi, Behzad, Azarshab, Mahsa, Mojarrad, Alireza, Ghasrsaz, Haniyeh, Mahmoodi, Tara, Farahani Rad, Farid, Pourkand, Donya, Zohrei, Asieh, Emtiazi, Nikoo, Askarinejad, Amir, Roozbehi, Khatere, Beheshtiparvar, Dorsa, Alireza, Daneshvar, Daneshvar, Kimia, MomeniAmjadi, Arman, Khorsand, Kamyar, Pour Bahrami, Parnian, Hosseini Hooshiar, Mohammad, Ahmadyan, Maryam, Hassanzadeh, Hournaz, Tafreshi, Seyed Mobin, Khorsandi, Michael, Motavaselian, Mohsen, Goudarzi, Mina, Riahi, Farshad, Farrokhi, Masoud |
| بيانات النشر: |
Zenodo |
| سنة النشر: |
2024 |
| المجموعة: |
Zenodo |
| الوصف: |
The integration of artificial intelligence (AI) into the field of medical diagnostics represents a revolutionary advancement in healthcare. AI diagnosticians, powered by sophisticated algorithms and vast datasets, are transforming how diseases are detected and diagnosed, offering potential improvements in accuracy, efficiency, and accessibility. One of the primary advantages of AI in medical diagnosis is its ability to analyze and interpret large volumes of data rapidly. Traditional diagnostic processes, which rely heavily on human expertise, can be time-consuming and prone to errors. In contrast, AI systems can process complex medical data, including medical imaging, laboratory results, and patient histories, in a fraction of the time. For instance, AI algorithms have demonstrated remarkable proficiency in interpreting radiological images, often matching or surpassing the diagnostic accuracy of experienced radiologists in detecting conditions such as cancer, fractures, and neurological disorders. Moreover, AI diagnosticians excel in identifying patterns and correlations that may be overlooked by human clinicians. Machine learning models can be trained on extensive datasets to recognize subtle indicators of disease, leading to earlier and more accurate diagnoses. This capability is particularly beneficial in diagnosing rare diseases and conditions with ambiguous symptoms, where traditional diagnostic methods might falter. AI also enhances the accessibility of medical diagnosis. In regions with limited access to healthcare professionals, AI-powered diagnostic tools can provide essential support. For example, AI applications in telemedicine enable remote diagnosis and consultation, bridging the gap between patients and medical expertise. This democratization of diagnostic services has the potential to improve healthcare outcomes in underserved communities globally. |
| نوع الوثيقة: |
book |
| اللغة: |
unknown |
| Relation: |
https://doi.org/10.5281/zenodo.11266850; https://doi.org/10.5281/zenodo.11266851; oai:zenodo.org:11266851 |
| DOI: |
10.5281/zenodo.11266851 |
| الاتاحة: |
https://doi.org/10.5281/zenodo.11266851 |
| Rights: |
info:eu-repo/semantics/openAccess ; Creative Commons Attribution 4.0 International ; https://creativecommons.org/licenses/by/4.0/legalcode |
| رقم الانضمام: |
edsbas.102B1AFC |
| قاعدة البيانات: |
BASE |