Face recognition, voice assistants, chatbots, automation recommendations, with the gradual entry of artificial intelligence into human life, the face of various industries is also changing. According to Statista, the world's leading online statistical database, the global AI software market was about US$22.6 billion in 2020, mainly in the media, finance, medical, retail, manufacturing, etc (see chart below). The following is an introduction to each industry as mentioned.
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1. Media Industry
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Nowadays, the media makes extensive use of computers and the Internet, which implies the significant influence of AI. With readers' attention shifting from paper to the Internet, "paid subscriptions" and "advertising" have become new revenue sources for media. For example, social media editors observe and analyze community trends and create discussion topics based on current issues; TV and radio platforms make quarterly or annual plans to ensure that the content produced is up to date. Artificial intelligence can be used as a supplement, and online media voice and social media monitoring are common applications for website managers to deliver the latest information to their audience.
2. Financial Industry
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The AI application in financial institutions can be categorised into four major areas: customer management, risk management and compliance, process improvement, and data analysis. "Customer management" emphasizes the role of AI in serving customers, including the smart recommendation of portfolio based on investor preferences, and the use of natural language understanding technology to conduct smart inquiries and one-on-one Q&A. In the area of "risk management and compliance", AI screens suspicious transactions for risk control to prevent money laundering, fraud and other criminal activities. As for "process improvement", AI uses optical image recognition to push forward the digitization of bills, and automatically generates various sheets and charts to speed up operation processes. "Data analysis" focuses on customer behavior trends analysis to recommend suitable products and achieve accurate marketing.
3. Medical Industry
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AI also has quite diverse applications in the medical industry, such as precision diagnosis, drug development, clinical decision support, surgical robot, etc. These applications enable patient-centered medical treatment, give the most customized drugs and treatment, avoid the side effects due to delayed treatment and inappropriate drugs, and provide better quality of medical care. In the time-consuming and costly new drug development process, AI can effectively cut down on the number of trials and errors required for drug candidates by identifying therapeutic targets and, as a result, significantly reduce the cost and time of drug manufacturing. It can even perform low-risk, precise and efficient surgeries, which release the patient’s pain, surgeon’s stress and mental burden of family members from long waiting time.
4. Retail Industry
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Currently, AI is being used to improve the consumer shopping experience. In the retail industry, smart e-commerce combines delivery robots, automated warehouses, and supply chain analytics to effectively reduce costs and increase revenue. AI machine learning can collect data and traffic from brand websites, apps, and social media to segment customers and provide customized services to each group. In addition, it helps stores design supply chains that are small yet diverse enough to meet customers' needs, thus, storage costs are reduced and stock is replenished automatically at any time, and the retailing process is accurately managed. Meanwhile, it reduces resource waste and environmental damage caused by fashion brands.
5. Manufacturing Industry
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The traditional manufacturing industry is following suit as well. By introducing AI technology, sensors can detect potential problems when machines are malfunctioning or parts are defective, so that it lowers the risk of accidents. In addition, with the help of AI to select the right raw materials and assist in the design and quality control process, the employees’ work performance is expected to improve. What's more, AI automates the process, which ensures the safety of factory operators and reduces the risk of occupational accidents caused by human errors. As repetitive work items are left to AI, workers can avoid fatigue and occupational injuries caused by excessively long working hours.
Artificial intelligence is a complex yet simple technology application with a single core principle——to put the customer at the center of product planning in order to solve their pain points and even create more value. Among numerous industries, the medical industry is particularly suitable for AI, as Taiwan has an excellent medical system, rich clinical energy, and a national health insurance database that can be used to plan, monitor, and evaluate medical services. In particular, the combination of AI computer vision and medical imaging is a very important non-invasive diagnostic tool; the market is estimated by Data Bridge Market Research, a world-renowned market research firm, to reach US$264.85 billion by 2026, with an annual compound growth rate of 36.9%. According to this, it is evident that the medical development, the data collection of patient symptoms, and the modern clinical care brought by AI with the support of machine learning is a growing trend.
By combining many experienced clinicians with a team of outstanding data scientists, Muen has accomplished a number of innovations that address the following medical AI research challenges:
1. Unable to upload data publicly: Use data formats and clinical standard from different places to train models
2. Data differences caused by various devices: Train models standalone without uploading data to external servers
3. Difficult to get enough data: Build pre-training models to improve model accuracy
4. Unable to be applied in different places: easy-to-use UI, built-in best model hyperparameters, do model training without programming
MAIA is the world's first automated AI platform developed specifically for the medical field, and is the only one on the market that can import DICOM format, and can combine multiple patient images as a single unit for evaluation.
The MAIA platform has competitive advantages over competitors in terms of multiple task types, convenient data storage, easy operation, and diverse payment methods, making it suitable not only for large-scale institutional projects, but also for individual physicians working on research topics. The platform's AutoDL core is one of the few platforms that supports automatic training of segmentation models to detect and label lesions, and is the only platform that can input trainable audio data, as well as support metadata types such as compound file of Image and Tabular. The platform can also train models with images of the same case (which is very common in clinical practice); only MAIA's technology is sufficient to apply such data types. In addition, the platform is available in both stand-alone, server and cloud versions.