Universal Artificial Intelligence Research Articles

A Universal Artificial Intelligence Platform for Collaborative Management of Cataracts

Background: The current healthcare system is unsatisfactory for the management of high-incidence diseases, such as cataract, due to inadequate medical resources and limited accessibility. Artificial intelligence (AI) holds great promise but remains to be improved to integrate it into primary healthcare services for increased patient coverage. The goals of this study were to establish and validate a universal AI platform for the collaborative management of cataracts involving multilevel clinical scenarios and to explore an AI-based medical referral pattern to improve collaborative efficiency and resource coverage. Methods: The training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence (CMAAI), covering multilevel healthcare facilities and capture modes. The datasets were labeled using a three-step strategy: (1) capture mode recognition; (2) cataract diagnosis as a normal lens, cataract or a postoperative eye; and (3) detection of referable cataracts with respect to etiology and severity. Moreover, we integrated the cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary healthcare, and specialized hospital services. Findings: The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in three-step tasks: (1) capture mode recognition (AUC 99.28%-99.71%), (2) cataract diagnosis (normal lens, cataract or postoperative eye with AUCs of 99.82%, 99.96%, and 99.93% for mydriatic-slit lamp mode and AUCs >99% for other capture modes), and (3) detection of referable cataracts (AUCs>91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30.3% of people be referred, substantially increasing the ophthalmologist-to-population service ratio by 10.2-fold compared to the traditional pattern. Interpretation The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations to provide cost-effective health care for an increasing number of patients. Trial Registration Number: This study was registered with ClinicalTrials.gov (identifier: NCT03623971). Funding Statement: This study was supported by the National Key Research and Development Program (2018YFC0116500), the Key Research Plan for the National Natural Science Foundation of China in Cultivation Project (91546101), the Science Foundation of China for Excellent Young Scientists (8182200130), the Guangdong Provincial Natural Science Foundation for Distinguished Young Scholars of China (2014A030306030), the Guangdong Province Universities and Colleges Youth Pearl River Scholar Funded Scheme (Haotian Lin), the National Natural Science Foundation of China (81800810), the Natural Science Foundation of Guangdong Province (2018A030310104), the Science and Technology Planning Projects of Guangdong Province (2017B030314025), the Clinical Research and Translational Medical Center of Pediatric Cataract in Guangzhou City (201505032017516), the Outstanding Young Teacher Cultivation Projects in Guangdong Province (YQ2015006), the Fundamental Research Funds for the Central Universities (16ykjc28) Declaration of Interests: The authors declare that they have no competing financial interests to disclose. Ethics Approval Statement: Ethical review of the study was performed by the Zhongshan Ophthalmic Center Ethics Review Committee.

Accelerating the construction of artificial intelligence diagnostic platform based on ophthalmic imaging database

With the global trend of aging populations, the prevalence of blindness is expected to increase.However, due to deficiencies and imbalances, medical resources are far from satisfactory for ophthalmic disease diagnosis and management, particularly in primary medical facilities of developing countries.Eyeball is a visualized organ with its superficial position and transparent refractory media.Therefore, many ophthalmic diseases can be screened and diagnosed from images and photographs, such as cataract, cornea diseases and retinopathy, etc.As the development of data mining technology and accumulation of large amount of ophthalmic clinical data, the conditions are ripe for establishment of artificial intelligence (AI) diagnostic platform.Currently, attention should be focused on integrating the abundant ocular imaging resources, practically applying the data mining technology and gradually developing the universal AI platform for the management of ophthalmic disease. Key words: Ophthalmic diseases; Artificial intelligence; Ocular images; Data mining

Universal Artificial Intelligence for Intelligent Agents: An Approach to Super Intelligent Agents

Human beings have the real intelligence. The intelligence triggers new thoughts in mind. Human thoughts so many things but he may take long times to solve a complex problem. If he builds such a system which work as like human intelligence, then the time taken to solve the complex problem may be very less. In this case he provides the Artificial Intelligence (AI) to the system. Artificial intelligence based system has the ability to mimic the functions of the human brain. An intelligent agent works on behalf of man. What will happen if send the intelligent agent in new environment? It can work properly or not properly in the new environment. If we provide such intelligence to the agent that it works proper in the new environment without changing their set of rules. Such type of intelligence generally known as Universal Artificial Intelligence (UAI). This paper suggests an idea to build such an intelligent agent that attempts to take the right decision in the new environment. Here we will use the neuro-fuzzy system to provide the more intelligence to agent and this agent can take right decision with learning capability in new environment. If an agent has more intelligence than other agent we can call it super intelligent agent. This paper also shows the simulation of intelligent agent to avoid obstacle in new environment. This simulated intelligent agent shows the good result as compared to existing work. Keywords- Universal Artificial Intelligence, Hidden Markov Model, Neuro-Fuzzy Systems

The Big Question

The Big Question