Authors
- Erfan Rahmani
- Masoud Farrokhi
- Ali Aghajan
- Golsa Gholampour
- Erfan Ghoodjani
- Ramtin Shemshadigolafzani
- Fatemeh Gheibi
- Arya Tarassodi
- Leila Rostamian
- Parinaz Taheri
- Mohsen Firoozi
- Dalia Sharifi
- Nastaran Nemati
- Pegah Babaheidarian
- Horrieh Abbasmofrad
- Saeid Ali Zadeh
- Bahareh Salekani
- Golnaz Dadpour
- Parham Rahmani
Keywords:
Artificial Intelligence, Quality of Life, InterventionsAbstract
Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering innovative solutions to enhance patient quality of life (QoL). By leveraging machine learning algorithms, natural language processing, and predictive analytics, AI enables personalized care, early disease detection, and improved treatment outcomes. One key area where AI has made significant strides is in chronic disease management. Conditions such as diabetes, cardiovascular diseases, and chronic pain can be monitored continuously through wearable devices and mobile health apps. These technologies track vital signs, alert patients and physicians to potential complications, and provide tailored recommendations for lifestyle modifications, thereby empowering patients to take an active role in their care. In the realm of mental health, AI-driven chatbots and virtual therapists offer accessible, stigma-free support to individuals experiencing anxiety, depression, or stress. These tools provide real-time guidance, track emotional patterns, and integrate seamlessly with traditional therapy, improving mental health outcomes. AI is also revolutionizing cancer care through precision oncology. Algorithms analyze vast datasets, including genomic profiles and medical records, to identify optimal treatment strategies for individual patients. This personalized approach enhances therapeutic efficacy and minimizes side effects, significantly improving the QoL for cancer patients. Moreover, AI enhances diagnostic accuracy by analyzing medical images and clinical data with unparalleled speed and precision. For example, AI tools in radiology and pathology reduce diagnostic errors, enabling earlier interventions and better prognoses. Despite its immense potential, challenges such as ethical concerns, data security, and algorithm biases must be addressed to maximize the benefits of AI. By fostering collaboration between technologists, clinicians, and policymakers, AI-driven strategies can be refined to improve the QoL for patients across diverse medical disciplines.
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