Respond To The Discussion Question Using The Lessons And Voc ✓ Solved
Respond To The Discussion Question Using The Lessons And Vocab
Respond to the discussion question using the lessons and vocabulary found in the reading. Support your answers with examples and research, and cite your research using the correct APA format. Justify your answers using examples and reasoning. Comment on the postings of at least two classmates with regard to whether you agree or disagree with their views. Discuss how expert systems, neural networks, and genetic algorithms can help scientists meet medical challenges. Provide an example of each system. Select one of your three examples and describe the system more in-depth. Give an example of a system currently being used and describe how and where it is in use today.
Paper For Above Instructions
In recent years, the integration of artificial intelligence (AI) in medicine has revolutionized disease diagnosis, treatment planning, and research. AI technologies such as expert systems, neural networks, and genetic algorithms have greatly contributed to overcoming various medical challenges. In this paper, I will discuss how these systems work, provide examples of each, and delve deeper into one specific system that currently operates in the medical field.
Expert Systems
Expert systems are AI programs that utilize databases and apply specific rules to solve complex problems, emulating the decision-making ability of human experts. In medicine, expert systems assist clinicians by providing diagnostic suggestions based on patient data. For instance, the MYCIN expert system is one of the earliest applications designed to diagnose bacterial infections and recommend antibiotics. MYCIN identified patterns in data related to infections, leading to effective treatment plans (Meyer, 2020).
Neural Networks
Neural networks are computational models inspired by the human brain's neural architecture. They are particularly adept at recognizing patterns and making predictions based on large datasets. In medical diagnostics, neural networks analyze imaging data to detect abnormalities. For example, Google's DeepMind has developed an advanced neural network capable of interpreting retinal scans to predict diseases like diabetic retinopathy (Gulshan et al., 2016). This AI tool can identify potential health issues faster than human experts.
Genetic Algorithms
Genetic algorithms (GAs) are optimization techniques based on the principles of natural selection and genetics. In medicine, GAs are often used for drug discovery and designing treatment plans. One example of a GA in action is its application in optimizing complex treatment protocols for cancer patients, where various factors such as drug interactions, genetic information, and specific patient conditions must be considered (Bhandari et al., 2019).
In-depth Example: Neural Networks in Retinal Imaging
Among the discussed systems, neural networks offer particularly compelling applications in medical imaging. The system developed by Google's DeepMind for retinal imaging exemplifies how neural networks may be harnessed to enhance diagnostic precision. It employs deep learning algorithms to analyze thousands of retinal images, learning from diverse datasets to improve its accuracy in disease detection.
The DeepMind system not only identifies diabetic retinopathy but also more severe conditions such as age-related macular degeneration. In a clinical setting, this AI solution assists ophthalmologists in diagnosing and managing these potentially sight-threatening diseases, thus enabling timely intervention (Liu et al., 2020).
Current Usage of Neural Networks in Medicine
Currently, the integration of neural networks in healthcare is witnessing rapid growth. Hospitals across several countries, including the United States and the United Kingdom, have begun utilizing AI technologies to streamline their diagnostic processes. The retinal imaging system can analyze images with up to 99% accuracy for diabetic retinopathy, making it invaluable in screenings where human resources are limited (Reddy et al., 2021).
Additionally, this technology is not just limited to hospitals; it is also being used in telemedicine platforms, allowing remote diagnosis of patients in underserved areas, thereby increasing accessibility to quality healthcare. By reducing the burden on healthcare professionals and expediting diagnosis, neural networks exemplify the transformative impact of AI in addressing some of the most pressing healthcare challenges today.
Conclusion
The advancements in AI technologies, especially expert systems, neural networks, and genetic algorithms, demonstrate vast potential in transforming the landscape of healthcare. By leveraging these systems, medical professionals can enhance diagnostic accuracy, optimize treatment plans, and ultimately improve patient outcomes. As AI continues to evolve, its integration into everyday medical practices will likely solve many existing challenges while fostering innovative solutions for future healthcare needs.
References
- Bhandari, A., Shukla, A., & Mhaske, S. (2019). Applications of Genetic Algorithms in Healthcare. Journal of Biomedical Informatics, 92, 103155.
- Gulshan, V., Peng, L., Coram, M., et al. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 316(22), 2402-2410.
- Liu, Y., Chen, P.-C., Krause, J., et al. (2020). How to Read Articles That Use Machine Learning: Users’ Guides to the Medical Literature. JAMA, 322(18), 1824-1830.
- Meyer, J. (2020). Expert Systems in Medicine: Past, Present and Future. Artificial Intelligence in Medicine, 105, 101898.
- Reddy, S. D., Mavrakis, A. T., & Lee, C. (2021). Machine Learning and Artificial Intelligence in Ophthalmology: Current Applications and Future Directions. Clinical Ophthalmology, 15, 2519-2531.