Artificial intelligence; Its history, applications and novelty in medicine

Artificial intelligence (AI) has the potential to revolutionize the field of medicine by enablinghealthcare providers to make faster, more accurate diagnoses and treatments. Here are a fewexamples of the latest AI innovations in medicine: Medical image analysis, Precision medicine,Virtual nursing assistants, Drug discovery, Robotic surgery, Predictive Analytics, and Naturallanguage processing. These are just a few examples of the many ways AI is being used to improvehealthcare. As AI technology continues to advance, it is likely that we will see even more innovativeapplications in the field of medicine.Method for research in AI: There are several methods for conducting research in artificial intelligence(AI) in medicine. Here are a few examples: Supervised learning, Unsupervised learning,Reinforcement learning, Transfer learning, Deep learning, and Development and validation ofartificial intelligence models.Development and validation of artificial intelligence models is one of the important ways to conductresearch in the field of artificial intelligence (AI) in medicine. Here are some key steps in thismethod: Data collection and preparation, Feature extraction, Model selection and training, Modelevaluation, and validation, Clinical validation and deployment, and, Ethical considerations. Byfollowing these steps, researchers can develop and validate AI models that can assist healthcareproviders in making faster, more accurate diagnoses and treatments.Background and aims: Artificial intelligence has the potential to revolutionize medicine in numerousways, from improving the accuracy of diagnoses and treatments to enabling more personalizedcare. Here are some of the key results and advances in the field of AI in medicine such asMedical image analysis, Drug discovery, Personalized medicine, Medical chatbots, and Electronichealth records.Results: AI-powered tools are being developed to aid in disease diagnosis, drug development,personalized treatment, and patient monitoring. These tools can potentially improve the accuracyand speed of diagnoses, reduce errors and variability in treatment, and ultimately improve patientoutcomes. However, challenges such as data quality, regulatory barriers, and ethical considerationsmust be addressed to ensure that AI is integrated safely and effectively into clinical practice.Conclusion: Overall, AI has the potential to transform many aspects of medicine, from diagnosisand treatment to drug discovery and patient care. However, there are still challenges to overcome,including data privacy concerns, algorithm bias, and regulatory issues. The field of AI in medicineis rapidly advancing and holds promise for transforming the future of healthcare.

کلیدواژه ها:

artificial intelligence ، medicine ، Machine learning ، Deep learning