Using AI to solve modern issues [Week 1]
Nicholas Ching Woei Xiang TP077555
Artificial Intelligence is rapidly transforming the healthcare landscape, offering innovative solutions for diagnosing and treating common diseases. One promising application is the use of AI-powered diagnostic tools, such as Stanford University's system that accurately diagnoses skin cancer by analyzing images of skin lesions [Ko, Swetter, Blau, & Thrun, (2017).]
AI is also making significant strides in drug discovery and development. Researchers at MIT have created an AI system that can predict the binding properties of potential drug candidates, accelerating the process of identifying effective treatments [Zhavoronkov, Ivanenkov, Aliper, (2019).]
Moreover, AI-powered virtual assistants are being explored as a means to provide personalized health coaching and medication adherence support to patients with chronic conditions such as diabetes and hypertension [Sartor, Gelarden, (2021).]
While the potential of AI in healthcare is vast, it is essential to address ethical concerns surrounding privacy, bias, and transparency to ensure its responsible and equitable implementation [Obermeyer, Powers, (2019).]
Shahzada Fazl Nashwan TP074680
Worls problems solved through AI
Artificial Intelligence (AI) has the potential to solve a wide range of problems across various domains. One area where AI can make a significant impact is in healthcare. AI-powered systems can assist in early disease detection, drug discovery, and personalized treatment plans (Davenport & Kalakota, 2019). By analyzing vast amounts of medical data, AI algorithms can identify patterns and provide insights that may be overlooked by human experts.
Another crucial problem that AI can help address is climate change. AI can be applied to optimize energy systems, predict weather patterns, and model the impact of climate change on ecosystems (Rolnick et al., 2019). This information can guide policymakers and researchers in developing effective mitigation and adaptation strategies.
In the field of education, AI can personalize learning experiences by adapting to individual students' needs and learning styles (Popenici & Kerr, 2017). AI-powered tutoring systems can provide real-time feedback and adjust the content and pace based on each student's progress, potentially improving educational outcomes.
However, it is important to note that AI systems are not without their limitations and potential risks. Ethical considerations, such as bias in data and algorithms, as well as privacy and security concerns, must be carefully addressed (Fjeld et al., 2020).
Muhamad Luthfi Hakim (TP073468)
AI Revolutionizes Scientific Discovery: Unveiling Hidden Patterns and Accelerating Progress
Science is about to enter a revolutionary period. Science fiction is no longer relevant to artificial intelligence (AI), which is quickly evolving into a potent instrument in the hands of scientists, accelerating scientific advancement at a never-before-seen rate.
"AI is advancing science in a range of ways — identifying meaningful trends in large datasets, predicting outcomes based on data, and simulating complex scenarios," said Victor Dzau, President of the National Academy of Medicine (Sara Frueh, 2023).
The capacity of AI to analyze large datasets—a process that can be overwhelming for even the most accomplished scientists—is one of its main advantages. Artificial intelligence (AI) algorithms are capable of sorting through massive amounts of data, uncovering links and patterns that the human eye could miss. This is especially helpful in domains like astronomy, where scientists are juggling petabytes of data gathered by potent telescopes.
Furthermore, AI is becoming efficient in forecasting future events. AI is being used in medicine, for example, to evaluate patient data and identify people who are more likely to contract specific diseases. Better patient outcomes and early treatment are made possible by this (Jami Klotz, 2023).
The impact of AI on scientific discovery is undeniable.expand_more However, challenges remain. As Steven Finkbeiner, a senior investigator at the Gladstone Institutes, points out, "There's a lot to consider" when using AI for independent scientific inquiry [1].
One concern is the potential for bias in AI algorithms.expand_more If the data used to train an AI is biased, the resulting predictions or discoveries will be as well.expand_more Another challenge is ensuring the transparency and explainability of AI models.expand_more Scientists need to understand how AI arrives at its conclusions in order to trust its findings.
Despite these challenges, the future of scientific discovery with AI is bright. As AI technology continues to evolve and researchers become more adept at using it, we can expect even more groundbreaking discoveries across a wide range of scientific disciplines.
https://www.nationalacademies.org/our-work/ai-for-scientific-discovery-a-workshop
Andrea Willige (2023) How AI can speed scientific discovery, from predicting virus variants to vital protein research. https://www.weforum.org/agenda/2023/10/ai-for-good-science-discovery/
In the era of rapid technological advancement, Artificial Intelligence (AI) emerges as a potent tool for addressing multifaceted societal challenges. One prominent issue ripe for AI intervention is healthcare. AI algorithms can analyze vast medical datasets, aiding in early disease detection and personalized treatment plans. For instance, a study by Esteva et al. (2017) demonstrated that AI-based deep learning models outperformed dermatologists in diagnosing skin cancer, showcasing AI's potential in enhancing diagnostic accuracy and efficiency.
Moreover, AI holds promise in mitigating environmental degradation. Climate change, exacerbated by human activities, necessitates innovative solutions. AI-driven predictive models can analyze climate data to forecast extreme weather events and inform proactive measures for disaster management (Lopez et al., 2019). Additionally, AI-powered monitoring systems facilitate precision agriculture, optimizing resource allocation and reducing environmental impact (Kamilaris et al., 2017).
Furthermore, AI offers solutions to the challenge of urbanization. With rapid urban growth, efficient resource management becomes imperative. AI-enabled smart city technologies can optimize energy consumption, alleviate traffic congestion, and enhance public safety (Albino et al., 2015). By harnessing real-time data analytics and autonomous systems, cities can become more sustainable and resilient.
In conclusion, the application of AI presents a transformative opportunity to address pressing global issues across diverse domains. Through continued research and ethical implementation, AI can contribute significantly to building a more equitable, sustainable, and technologically advanced society.
Vandro (TP075672)
AI can be a good help to cancer treatment, enhancing both therapeutic strategies and diagnosis. With AI the analyses of the patient data, genetics, medical records all of that can be processed by AI.
By understanding the content within this data, AI can individual treatment for the patient and also recommend the most effective therapy Creating conditions to avoid unforeseen situations.
AI can also has a tumor detection, classification and monitoring, and image techniques. Methods that help preventing tumor with an earlier detection and providing a treatment plan.
AI can easily find drugs by rapidly the molecular structures inside the patient body and predict potential/further drugs interactions. This detection can provide new/more ways of treatment (medicines) according with the status of the patient.
With the help of AI oncologists can make their plans on how they will cure the patient with treatment selection and therapeutic development.
That’s why I believe that AI is very important in this aspect.
References:
Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature biotechnology, 37(9), 1038-1040.
Sartor, F., Gelarden, I. A., . (2021). Conversational AI for health coaching: reflecting on the relationship between language and behavior change. Digital Biomarkers, 5(1), 41-58.
Obermeyer, Z., Powers, B., (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
Davenport, T. H., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), 94.
Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 1-13.
Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., & Srikumar, M. (2020). Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI. Berkman Klein Center Research Publication, (2020-1).
Esteva, A. et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
Lopez, J. C. et al. (2019). Review of Artificial Intelligence Techniques Employed in Climate Change Mitigation and Adaptation. Sustainability, 11(9), 2590.
Kamilaris, A. et al. (2017). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70-90.
Albino, V. et al. (2015). Smart Cities: Definitions, Dimensions, Performance, and Initiatives. Journal of Urban Technology, 22(1), 3-21.
Jallat, F. (n.d.). How AI could dramatically improve cancer patients’ prognosis.
Admin. (2023b, November 7). What problems can artificial intelligence help us solve? - Stefanini.
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