Artificial intelligence use cases
Pro Research Analysisby 
Searched over 200M research papers
Artificial Intelligence Use Cases
AI in Pandemic Preparedness and Response
Artificial intelligence (AI) has proven to be a valuable tool in managing pandemics by informing clinical and public health decision-making. Key use cases identified include forecasting infectious disease dynamics, surveillance and outbreak detection, real-time monitoring of public health adherence, real-time detection of influenza-like illnesses, triage and timely diagnosis of infections, and prognosis of illness and response to treatment . These applications leverage machine learning (ML) techniques to improve the accuracy of estimations and projections, particularly during the COVID-19 pandemic .
AI in Brain Disease Diagnosis and Treatment
In the domain of brain care, AI has achieved significant results in diagnosis, surgical planning, intra-operative assistance, and postoperative assessment. Techniques such as artificial neural networks, support vector machines, and random forests are widely used. Brain imaging data is frequently utilized, enhancing clinicians' decision-making capabilities in neuroscience applications . However, challenges remain in gathering comprehensive data and developing explainable AI algorithms for practical use .
AI in Pharmaceutical and Healthcare Research
AI is revolutionizing pharmaceutical and healthcare research by enhancing disease diagnosis, digital therapy, personalized treatment, drug discovery, and epidemic forecasting. Deep learning and neural networks are prominent technologies, with Bayesian nonparametric models being potential tools for clinical trial design. AI has been applied to predict outbreaks of diseases like influenza, Zika, Ebola, Tuberculosis, and COVID-19, promising rapid and cost-effective advancements in healthcare .
AI for Decision Making in Big Data Era
The integration of AI in decision-making processes has been empowered by advancements in supercomputing and Big Data technologies. AI supports or replaces human decision-makers in various tasks, offering enhanced decision support and automation capabilities. However, challenges such as explainability, privacy, algorithmic bias, and scalability need to be addressed to fully realize AI's potential in decision-making .
AI in Business Value Creation
Organizations are increasingly adopting AI to generate business value by leveraging data and computational capacity. AI technologies enable improved operations through enhanced decision-making, process automation, and error reduction. However, businesses face challenges in understanding how AI creates value and in overcoming barriers to adoption and implementation .
AI Implementation Challenges in Healthcare
The implementation of AI in healthcare, particularly ML, faces several challenges. These include ensuring meaningful decision support, maintaining privacy and consent, addressing algorithmic bias, and achieving scalability. The future of AI in healthcare is promising but requires support from various stakeholders to overcome these challenges and achieve widespread benefits .
AI in Structural Engineering
AI offers efficient alternatives to classical modeling techniques in structural engineering, addressing uncertainties and complex problems. Techniques such as ML, pattern recognition, and deep learning are used to determine engineering design parameters, enhance decision-making, and increase computational efficiency. These methods provide significant savings in human time and effort .
AI in Stock Market Trading
AI has been extensively researched for its application in stock market trading, focusing on portfolio optimization, stock market prediction, and financial sentiment analysis. AI techniques have evolved from initial research to state-of-the-art applications, continuously gaining attention and becoming more specific and thorough .
AI in Medicine
AI in medicine encompasses both virtual and physical branches. The virtual branch includes informatics approaches like deep learning for information management and electronic health records, while the physical branch involves robotics for surgery and patient assistance. AI applications in medicine require further validation of their utility, economic value, and interdisciplinary strategies for broader adoption .
Conclusion
AI's diverse applications across various fields demonstrate its transformative potential. From pandemic response and brain disease treatment to pharmaceutical research and structural engineering, AI enhances decision-making, efficiency, and accuracy. However, challenges such as data comprehensiveness, explainability, and implementation barriers must be addressed to fully harness AI's capabilities.
Sources and full results
Most relevant research papers on this topic