How patients can benefit from artificial intelligence
Today, advanced machines have been developed to perform tasks formerly performed by humans, such as analyzing and interpreting data to help solve problems.
While machine learning (ML) has been widely used in many industries, the use and application of artificial intelligence (AI) in healthcare is still relatively new. Only recently have we seen artificial intelligence move from academia and research laboratories into hospitals. Artificial intelligence is used to assess risk, make informed diagnoses, and perform precise surgical procedures. Today, artificial intelligence is used in all types of medical specialties and services, including health care, surgical prioritization, drug discovery, or survival analysis.
Some of the key areas where artificial intelligence is bringing significant benefits in healthcare include:
Public Health and Big Data
AI excels at analyzing big data collected by healthcare organizations and can Analyze data quickly and accurately. These data enable proactive risk assessment, close public health gaps, and explain how behavioral, genetic, and environmental factors influence population health.
By combining this information with diagnostic data, artificial intelligence provides a comprehensive approach to patient treatment planning.
One of the most significant benefits of artificial intelligence in population research is the prediction of at-risk populations based on genetic, behavioral and social factors. Its potential in public health is huge and is now being harnessed by healthcare organizations to provide more personalized, data-driven care to patients and help improve outcomes.
Clinical decision-making
In medicine, the differential diagnosis of any disease is complex. Differential Diagnosis It takes time, effort, and money to get a clear diagnosis. Artificial intelligence greatly simplifies this process. Machine learning algorithms can make final diagnoses faster and more accurately than traditional methods. At the same time, the use of artificial intelligence in clinical diagnosis also reduces human diagnostic errors and allows more rapid treatment of serious diseases.
Artificial intelligence tools can sort through large amounts of patient clinical data, which is very helpful for timely diagnosis and early treatment. In particular, the use of automated machine learning (AML) has gone a long way in helping automate the data analysis process. AML uses automated algorithm selection, results visualization and improved interpretation. Data analysis can guide decision-making more accurately to improve clinicians' decision-making process. This in turn could improve diagnosis and treatment and impact patient survival and mortality.
artificial intelligence-assisted surgery
Another area where artificial intelligence stands out is its application in robotic surgery. Advances in electronics have led to the development of robots that can now perform delicate surgeries. The surgeon still controls the robot, but the robot can perform microdissections and access delicate spaces that human hands cannot.
The robot's arms have precise movements and can perform complex operations on the brain and heart with great precision. This has been shown to reduce the risk of blood loss and complications. In addition, all data from robotic surgeries can be saved to facilitate learning and training of surgeons.
balanced medical resources
People living in remote rural areas often have difficulty finding specialists. Waiting times can be long and people will have to travel to big cities. This is not only inconvenient for patients, but also expensive.
With artificial intelligence, primary care physicians can evaluate patients for all types of illnesses, whether they live in urban or rural areas. For example, AI robots can screen for eye diseases and send images to experts, who can recommend treatments. This is very beneficial to the patient because diagnosis is rapid and treatment can begin immediately.
Using artificial intelligence in rural areas enables primary care physicians to effectively triage patients who need urgent treatment and those who can be managed effectively.
In resource-poor settings, artificial intelligence can aid diagnosis when interpreting imaging studies such as chest X-rays, CT scans, PET scans and magnetic resonance imaging (MRI). Primary care physicians do not need to wait days or weeks to get an interpretation from an expert radiologist. Artificial intelligence can interpret these images very accurately in the field. For patients, this means no more waiting for diagnostic results, saving time.
In summary, developing AI digital infrastructure in rural areas can give people in these areas access to state-of-the-art medical diagnostics and faster care.
Optimizing Efficiency
Healthcare organizations are complex entities with thousands or even tens of thousands of patients, large amounts of patient data, and extensive interconnected processes and systems. This often reduces efficiency, leading to long wait times for patients and, in some cases, delays or missed appointments.
Data shows that artificial intelligence can quickly transform large amounts of patient data in electronic medical records, ensuring that no patient is left behind or misses an appointment. Additionally, AI can prioritize services based on available resources and improve revenue cycle performance by optimizing workflows.
Artificial intelligence in the healthcare field has a huge role both inside and outside medical institutions
The potential of artificial intelligence in the healthcare field is huge whether inside or outside medical institutions. Hospitals face ongoing financial challenges. Artificial intelligence can help compensate for operational inefficiencies, rising costs and shortages of health care workers. Technologies such as artificial intelligence will help improve access and delivery of medicines while improving patient outcomes.
As artificial intelligence continues to pour into all levels of healthcare, vast amounts of medical data can be properly extracted and analyzed. Data read by AI can provide deeper insights into the causes of complex diseases. Clinicians can rely on AI to identify conditions and benefit from guidance to determine effective treatment strategies.
The above is the detailed content of How patients can benefit from artificial intelligence. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
AI Hentai Generator
Generate AI Hentai for free.
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
1378
52
Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions
Jun 28, 2024 am 03:51 AM
This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP
Context-augmented AI coding assistant using Rag and Sem-Rag
Jun 10, 2024 am 11:08 AM
Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application
Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations
Jun 11, 2024 pm 03:57 PM
Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it
Seven Cool GenAI & LLM Technical Interview Questions
Jun 07, 2024 am 10:06 AM
To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text
To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework
Jul 25, 2024 am 06:42 AM
Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A
AI startups collectively switched jobs to OpenAI, and the security team regrouped after Ilya left!
Jun 08, 2024 pm 01:00 PM
Last week, amid the internal wave of resignations and external criticism, OpenAI was plagued by internal and external troubles: - The infringement of the widow sister sparked global heated discussions - Employees signing "overlord clauses" were exposed one after another - Netizens listed Ultraman's "seven deadly sins" Rumors refuting: According to leaked information and documents obtained by Vox, OpenAI’s senior leadership, including Altman, was well aware of these equity recovery provisions and signed off on them. In addition, there is a serious and urgent issue facing OpenAI - AI safety. The recent departures of five security-related employees, including two of its most prominent employees, and the dissolution of the "Super Alignment" team have once again put OpenAI's security issues in the spotlight. Fortune magazine reported that OpenA
Five schools of machine learning you don't know about
Jun 05, 2024 pm 08:51 PM
Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing
SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time
Jul 17, 2024 pm 06:37 PM
Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S
