AI — Artificial Intelligence
Artificial Intelligence (AI) is a broad field of computer science dedicated to developing technologies that can perform various advanced functions typically requiring human intelligence. The goal of AI is to create systems that can learn from data and solve problems in a human-like manner. The core technologies of AI mainly include machine learning, deep learning, natural language processing, and computer vision.
The development of artificial intelligence has gone through several stages:
01
Origin Stage (1950s)
During this stage, scientists began exploring how to make machines simulate human intelligence, focusing on areas such as logical reasoning and theorem proving. For example, in 1950, Claude Shannon proposed computer game theory, laying the foundation for artificial intelligence. In 1956, the Dartmouth Summer Research Project on Artificial Intelligence was held, officially using the term “artificial intelligence,” marking the birth of the AI discipline.
Golden Age (1956-1974)
During this period, AI research entered a golden age, achieving a series of groundbreaking advancements. For instance, in 1966, the ELIZA program demonstrated the possibilities of natural language conversation. In 1972, WABOT-1 was introduced, the world’s first full-scale humanoid robot.
03
First AI Winter (1974-1980)
In 1973, the UK government stopped funding AI research projects, and the US government also significantly reduced its investments, leading to setbacks in AI research and the onset of the first AI winter.
Prosperity Period (1980-1987)
In the 1980s, the Japanese government launched the “Fifth Generation Computer Systems” project, and the US government and businesses again invested heavily in AI. During this period, expert system technology was widely applied, and AI entered a new prosperity phase.
05
Second AI Winter (1987-1993)
By the late 1980s, the limitations of expert systems gradually became apparent, leading to investor withdrawals and another downturn in AI research.
Modern AI (1993-Present)
Since 1993, AI has entered a new stage of development, with breakthroughs in machine learning and deep learning technologies. In 2012, AlexNet achieved groundbreaking progress in image recognition, marking the advancement of deep learning. In 2020, OpenAI developed GPT-3, a language model capable of performing multiple tasks. Currently, AI surpasses humans in fields such as image recognition, speech recognition, and gaming, and is widely applied in various industries, including medical diagnosis, financial forecasting, and autonomous driving.
AGI — Artificial General Intelligence
Artificial General Intelligence (AGI), also known as strong AI, refers to machines that can understand, learn, and perform any intelligent task that humans can complete. AGI possesses cross-domain capabilities and can solve a variety of complex problems in different environments. Unlike narrow AI (Artificial Narrow Intelligence, ANI), which is designed for specific tasks, AGI aims to achieve comprehensive intelligence, enabling it to function across diverse environments and tasks with human-like cognitive abilities.
The term AGI was first proposed and used by Mark Gubrud in 1997. In 2000, Marcus Hutter proposed a mathematical formalism for AGI. In 2002, Shane Legg and Ben Goertzel reintroduced and popularized the AGI concept. Currently, AGI research activities are conducted at multiple conferences worldwide, with an increasing number of researchers focusing on open-ended learning and the development of general AI. Experts have differing opinions on when AGI will be achieved; some believe it could happen in a few years or decades, while others think it may take a century or longer.
AGI can exhibit intelligent behavior across various fields, not limited to a specific task. AGI can also continuously improve its capabilities through autonomous learning and experience accumulation without human intervention or reprogramming. AGI can perform complex reasoning, solve new problems, and make decisions in the face of uncertainty and complexity. AGI can understand and generate natural language, enabling natural interaction and communication with humans. AGI can adjust and adapt to different environments and situations, demonstrating flexibility and adaptability.
Once realized, AGI will bring revolutionary changes in multiple fields. It can be used in scientific research: analyzing vast amounts of data and proposing new theories to accelerate scientific discovery. It can provide personalized diagnostic and treatment plans, improving the efficiency and effectiveness of medical services. It can offer personalized learning experiences, helping students learn at their own pace and according to their needs. It can optimize resource allocation and improve the accuracy of market predictions. It can also automate daily tasks, providing intelligent assistant services to enhance quality of life.
GMAI — General Medical Artificial Intelligence
General Medical Artificial Intelligence (GMAI) is an intelligent system capable of performing a wide range of tasks in the medical field, with cross-domain capabilities. In April 2023, a research team from Stanford University published an article titled “Foundation models for generalist medical artificial intelligence” in Nature, proposing a new paradigm of medical AI, namely generalist medical AI (GMAI). GMAI combines multimodal medical data, including text, images, laboratory results, and genomics, to produce outputs in the form of natural language explanations, verbal suggestions, or image annotations, showcasing strong medical reasoning capabilities. Unlike AI designed for specific medical tasks (such as radiology, pathology, or diagnosis), GMAI can be applied to various medical contexts, handling complex medical data and problems. The goal of GMAI is to improve diagnostic accuracy, treatment effectiveness, and overall medical efficiency through comprehensive analysis and understanding of medical data.
Generalist Medical AI (GMAI) is an emerging AI paradigm that differs from traditional specialized medical AI models in several key ways:
The GMAI model can perform diverse medical tasks without requiring a large amount of specific task-labeled data.
The GMAI model has three core capabilities: it can execute dynamically specified tasks; flexibly combine various data modalities, such as images, electronic health records, laboratory results, and genomics; and utilize formal medical knowledge for reasoning.
The GMAI model can generate various expressive outputs, such as free text explanations, verbal suggestions, and image annotations, demonstrating advanced medical reasoning capabilities.
GMAI can function in various medical fields and contexts, including internal medicine, surgery, radiology, pathology, etc. GMAI can handle and integrate various types of medical data, such as electronic health records (EHR), medical images, genomic data, and clinical notes. GMAI provides accurate diagnostic suggestions by analyzing patient symptoms, history, and diagnostic images. GMAI recommends optimal treatment plans based on the latest medical research and the specific circumstances of the patient. GMAI predicts disease progression, offers preventive suggestions, and helps patients avoid severe health issues. GMAI automates patient record management, appointment scheduling, and follow-ups, improving the efficiency of medical processes. GMAI accelerates medical research progress by analyzing vast amounts of medical data to discover new disease patterns and treatment methods.
GOAI — General Orthopedic Artificial Intelligence
General Orthopedic Artificial Intelligence (GOAI), proposed by the author in July 2024, is a general medical AI system focused on the field of orthopedics. It can handle various tasks related to orthopedics, covering aspects from diagnosis to treatment planning and postoperative management. The goal of GOAI is to improve the precision and efficiency of orthopedic diagnosis and treatment through comprehensive analysis and understanding of orthopedic data.
GOAI can be applied in different subfields of orthopedics, such as joint diseases, spine, fractures, sports injuries, and orthopedic tumors, by analyzing patient symptoms, medical history, and imaging data to provide accurate diagnostic suggestions for diseases like fractures, arthritis, and spine issues. It can handle and integrate various types of orthopedic data, including X-rays, MRIs, CT scans, electronic health records (EHR), and surgical records. GOAI can also improve the accuracy of diagnostic and treatment suggestions for orthopedic-related diseases through continuous learning and experience accumulation. Based on the specific circumstances and needs of patients, GOAI can provide personalized diagnostic and treatment plans. GOAI helps orthopedic doctors make more accurate and effective medical decisions, enhancing the quality of medical services. By combining various data sources and advanced AI technologies, GOAI can significantly improve the precision and efficiency of orthopedic diagnosis and treatment, enhancing patient treatment experience and recovery outcomes.
References:
[1] https://www.nature.com/articles/s41586-023-05881-4
[2] https://pubmed.ncbi.nlm.nih.gov/37045921/
[3] https://www.ibm.com/topics/artificial-intelligence
[4] https://sitn.hms.harvard.edu/flash/2019/artificial-intelligence-in-medicine-applications-implications-and-limitations/
[5] https://www.ibm.com/topics/artificial-intelligence-medicine
[6] https://news.stanford.edu/stories/2023/04/advances-generalizable-medical-ai
[7] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/
[8] https://www.techtarget.com/healthtechanalytics/feature/Artificial-intelligence-in-healthcare-defining-the-most-common-terms
[9] https://www.researchgate.net/publication/369991868_Foundation_models_for_generalist_medical_artificial_intelligence
[10] https://www.finra.org/rules-guidance/key-topics/fintech/report/artificial-intelligence-in-the-securities-industry/overview-of-ai-tech
[11] https://en.wikipedia.org/wiki/Artificialintelligencein_healthcare
[12] https://arxiv.org/abs/2304.14204
[13] https://www.researchgate.net/publication/359212682_Applications_of_Artificial_Intelligence_in_Healthcare
[14] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6592326/
[15] https://www.rtinsights.com/revolutionizing-healthcare-with-generalist-medical-ai/
Disclaimer: This article is for reference only for medical professionals and does not represent the views of the Bone Today platform. We hope everyone can make rational judgments and apply them appropriately.
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