Artificial intelligence (AI) in Medicine - Translational Brain Function & Neuromodulation Lab
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Artificial intelligence (AI) in Medicine
Date: 2004-present
Overview
Artificial intelligence (AI) has revolutionized the field of medicine, particularly through the use of Artificial Neural Networks (ANNs) and neurofuzzy systems for diagnosis and predicting treatment responses. ANNs, inspired by the human brain's structure, excel at identifying complex patterns in medical data, such as imaging, genetic information, or patient records, enabling accurate disease diagnosis. Neurofuzzy systems, which combine the learning capabilities of neural networks with the reasoning of fuzzy logic, provide interpretable and adaptive decision-making tools, particularly useful in handling uncertainties inherent in medical data. Together, these AI techniques enhance personalized medicine by predicting patient-specific treatment outcomes, optimizing therapeutic strategies, and improving overall healthcare delivery. Their integration into clinical practice promises to transform diagnostics and treatment planning, making them more precise, efficient, and patient-centric.
Selected articles
Sargolzaee Aval F, Behnaz N, Raoufy MR, Alavian SM. Predicting the outcomes of combination therapy in patients with chronic hepatitis C using artificial neural network. Hepat Mon. 2014 Jun 1;14(6):e17028. doi: 10.5812/hepatmon.17028.
Raoufy MR, Hajizadeh S, Gharibzadeh S, Mani AR, Eftekhari P, Masjedi MR. Nonlinear model for estimating respiratory volume based on thoracoabdominal breathing movements. Respirology. 2013 Jan;18(1):108-16. doi: 10.1111/j.1440-1843.2012.02251.x.
Azarkhish I, Raoufy MR, Gharibzadeh S. Artificial intelligence models for predicting iron deficiency anemia and iron serum level based on accessible laboratory data. J Med Syst. 2012 Jun;36(3):2057-61. doi: 10.1007/s10916-011-9668-3.
Raoufy MR, Vahdani P, Alavian SM, Fekri S, Eftekhari P, Gharibzadeh S. A novel method for diagnosing cirrhosis in patients with chronic hepatitis B: artificial neural network approach. J Med Syst. 2011 Feb;35(1):121-6. doi: 10.1007/s10916-009-9348-8.
Raoufy MR, Eftekhari P, Gharibzadeh S, Masjedi MR. Predicting arterial blood gas values from venous samples in patients with acute exacerbation chronic obstructive pulmonary disease using artificial neural network. J Med Syst. 2011 Aug;35(4):483-8. doi: 10.1007/s10916-009-9384-4.
Dariani S, Keshavarz M, Parviz M, Raoufy MR, Gharibzadeh S. Modeling force-velocity relation in skeletal muscle isotonic contraction using an artificial neural network. Biosystems. 2007 Sep-Oct;90(2):529-34. doi: 10.1016/j.biosystems.2006.12.004.