Artificial Intelligence (AI) stands as a pivotal innovation deeply ingrained in both our daily routines and industrial operations. Its rapid evolution promises transformative impacts across various sectors, from cutting-edge industries to the lives of ordinary individuals. AI constantly updates human experiences, shaping interactions and augmenting capabilities. For instance, contemporary educational institutions leverage AI algorithms for attendance tracking via facial recognition technology. Looking ahead, the advent of autonomous vehicles represents a pinnacle of AI application, where vehicles rely entirely on AI systems for navigation, detecting traffic signals, and navigating roads.
{"title":"Utilizing Artificial Intelligence in Real-World Applications","authors":"José Gabriel Carrasco Ramírez, Md.mafiqul Islam","doi":"10.60087/jaigs.v2i1.p19","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p19","url":null,"abstract":"Artificial Intelligence (AI) stands as a pivotal innovation deeply ingrained in both our daily routines and industrial operations. Its rapid evolution promises transformative impacts across various sectors, from cutting-edge industries to the lives of ordinary individuals. AI constantly updates human experiences, shaping interactions and augmenting capabilities. For instance, contemporary educational institutions leverage AI algorithms for attendance tracking via facial recognition technology. Looking ahead, the advent of autonomous vehicles represents a pinnacle of AI application, where vehicles rely entirely on AI systems for navigation, detecting traffic signals, and navigating roads.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"65 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140460279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial intelligence (AI) has undergone rapid evolution in recent decades, catalysing the emergence of ground-breaking technologies that have reshaped various sectors. Among these advancements is the advent of autonomous vehicles, poised to revolutionize transportation and mobility. Moreover, AI has spurred the development of cutting-edge solutions in healthcare, exemplified by AI-powered medical imaging systems. This manuscript presents an overview of AI's evolution and explores the latest strides in autonomous vehicles and healthcare innovations. Delving into the foundational technologies like machine learning and computer vision, it elucidates the methodologies employed in crafting autonomous vehicles and healthcare solutions. The document also scrutinizes the advantages and hurdles inherent in these innovations, while offering insights into future avenues of research. Overall, it underscores AI's profound impact on transportation, healthcare, and beyond, underscoring the transformative potential of autonomous vehicles and healthcare technologies in fostering safer and more efficient mobility and healthcare systems.
{"title":"Investigating State-of-the-Art Frontiers in Artificial Intelligence: A Synopsis of Trends and Innovations","authors":"Sohana Akter","doi":"10.60087/jaigs.v2i1.p30","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p30","url":null,"abstract":"Artificial intelligence (AI) has undergone rapid evolution in recent decades, catalysing the emergence of ground-breaking technologies that have reshaped various sectors. Among these advancements is the advent of autonomous vehicles, poised to revolutionize transportation and mobility. Moreover, AI has spurred the development of cutting-edge solutions in healthcare, exemplified by AI-powered medical imaging systems. This manuscript presents an overview of AI's evolution and explores the latest strides in autonomous vehicles and healthcare innovations. Delving into the foundational technologies like machine learning and computer vision, it elucidates the methodologies employed in crafting autonomous vehicles and healthcare solutions. The document also scrutinizes the advantages and hurdles inherent in these innovations, while offering insights into future avenues of research. Overall, it underscores AI's profound impact on transportation, healthcare, and beyond, underscoring the transformative potential of autonomous vehicles and healthcare technologies in fostering safer and more efficient mobility and healthcare systems.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"145 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140460609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many disciplines, such as computer vision and natural language processing (NLP), find broad applications for artificial intelligence (AI) and machine learning (ML). We will give a brief history of edge detection in this post, which is an essential method for emphasizing important characteristics in a wide range of computer vision applications. We will also explore the transformative potential of transformer-based deep learning models in improving natural language processing applications. In addition, we will present two current research initiatives that demonstrate the creative uses of AI in business negotiation and the pharmaceutical industry. Furthermore, for this journal issue, we have carefully chosen five papers that are pertinent to these topics.
{"title":"Exploring the Applications of Artificial Intelligence across Various Industries","authors":"Md.mafiqul Islam","doi":"10.60087/jaigs.v2i1.p25","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p25","url":null,"abstract":"Many disciplines, such as computer vision and natural language processing (NLP), find broad applications for artificial intelligence (AI) and machine learning (ML). We will give a brief history of edge detection in this post, which is an essential method for emphasizing important characteristics in a wide range of computer vision applications. We will also explore the transformative potential of transformer-based deep learning models in improving natural language processing applications. In addition, we will present two current research initiatives that demonstrate the creative uses of AI in business negotiation and the pharmaceutical industry. Furthermore, for this journal issue, we have carefully chosen five papers that are pertinent to these topics. \u0000 ","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"53 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140461056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial Intelligence (AI) and Machine Learning (ML) represent burgeoning fields with the potential to transform numerous facets of society and industry. AI encompasses computer systems and algorithms capable of executing tasks typically necessitating human intelligence, such as learning, problem-solving, and decision-making. Conversely, ML entails the creation of algorithms facilitating computers to glean insights from data and refine their performance over time, sans explicit programming. This research delves into the fundamental principles and practical applications of AI and ML, encompassing domains like natural language processing, image and speech recognition, and the development of autonomous vehicles. Furthermore, we scrutinize the potential advantages and apprehensions linked with these technologies, including the prospect of job displacement and the susceptibility to misuse. Finally, we underscore the significance of ethical considerations and conscientious development practices to ensure the realization of AI and ML benefits while mitigating adverse repercussions.
人工智能（AI）和机器学习（ML）是新兴领域，有可能改变社会和工业的许多方面。人工智能包括计算机系统和算法，能够执行通常需要人类智慧才能完成的任务，如学习、解决问题和决策。反之，ML 则需要创建算法，帮助计算机从数据中获取洞察力，并在不明确编程的情况下逐步完善其性能。本研究深入探讨了人工智能和 ML 的基本原理和实际应用，涵盖自然语言处理、图像和语音识别以及自动驾驶汽车开发等领域。此外，我们还仔细研究了与这些技术相关的潜在优势和担忧，包括失业前景和易被滥用的问题。最后，我们强调了道德考量和认真开发实践的重要性，以确保实现人工智能和 ML 的优势，同时减轻负面影响。
{"title":"Exploring the Advancements and Ramifications of Artificial Intelligence","authors":"Sohel Rana","doi":"10.60087/jaigs.v2i1.p35","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p35","url":null,"abstract":"Artificial Intelligence (AI) and Machine Learning (ML) represent burgeoning fields with the potential to transform numerous facets of society and industry. AI encompasses computer systems and algorithms capable of executing tasks typically necessitating human intelligence, such as learning, problem-solving, and decision-making. Conversely, ML entails the creation of algorithms facilitating computers to glean insights from data and refine their performance over time, sans explicit programming. This research delves into the fundamental principles and practical applications of AI and ML, encompassing domains like natural language processing, image and speech recognition, and the development of autonomous vehicles. Furthermore, we scrutinize the potential advantages and apprehensions linked with these technologies, including the prospect of job displacement and the susceptibility to misuse. Finally, we underscore the significance of ethical considerations and conscientious development practices to ensure the realization of AI and ML benefits while mitigating adverse repercussions.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"60 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140461048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the world of technology, machine learning, or ML, is a well recognized word. It is concerning, therefore, when ML models are used in financial institutions. Actually, in order to provide their clients with the greatest experience possible, the Industry 4.0 has pushed them to grow their digital system. The definition and uses of machine learning as well as the current state of the finetech market in Nigeria will be covered in this publication. As a result, we will forecast how financial institutions will develop in the future and whether or not to employ machine learning.
在技术领域，机器学习（ML）是一个广为人知的词汇。因此，当 ML 模型被用于金融机构时，就会引起人们的关注。事实上，为了给客户提供尽可能好的体验，工业 4.0 推动着金融机构发展其数字化系统。本出版物将介绍机器学习的定义和用途以及尼日利亚金融科技市场的现状。因此，我们将预测金融机构未来将如何发展，以及是否采用机器学习。
{"title":"Applications of MachineLearning(ML): The real situation of the Nigeria Fintech Market","authors":"Md.mafiqul Islam","doi":"10.60087/jaigs.v1i1.34","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.34","url":null,"abstract":"In the world of technology, machine learning, or ML, is a well recognized word. It is concerning, therefore, when ML models are used in financial institutions. Actually, in order to provide their clients with the greatest experience possible, the Industry 4.0 has pushed them to grow their digital system. The definition and uses of machine learning as well as the current state of the finetech market in Nigeria will be covered in this publication. As a result, we will forecast how financial institutions will develop in the future and whether or not to employ machine learning.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"27 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139897127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dr. José Gabriel Carrasco Ramírez., Md.mafiqul Islam, Asm Ibnul Hasan Even
The integration of machine learning (ML) in healthcare has witnessed remarkable advancements, transforming the landscape of medical diagnosis, treatment, and overall patient care. This article provides a comprehensive review of the current trends and future prospects of machine learning applications in the healthcare domain.The current landscape is characterized by the utilization of ML algorithms for disease diagnosis and risk prediction, personalized treatment plans, and efficient healthcare resource management. Notable applications include image recognition for radiology and pathology, predictive analytics for disease prognosis, and the development of precision medicine tailored to individual patient profiles.This review explores the evolving role of ML in improving patient outcomes, enhancing clinical decision-making, and optimizing healthcare workflows. It delves into the challenges faced in integrating ML into existing healthcare systems, such as data privacy concerns, interpretability of complex models, and the need for robust validation processes.Additionally, the article discusses future prospects and emerging trends in ML healthcare applications, including the potential for predictive analytics to preemptively identify health issues, the integration of wearable devices and remote monitoring for continuous patient care, and the intersection of ML with genomics for personalized medicine.The overarching goal of this article is to provide healthcare professionals, researchers, and policymakers with insights into the current state of ML applications in healthcare, along with an outlook on the transformative potential that machine learning holds for the future of healthcare delivery and patient outcomes.
机器学习（ML）与医疗保健的结合取得了显著进展，改变了医疗诊断、治疗和整体患者护理的格局。本文全面回顾了机器学习在医疗保健领域应用的当前趋势和未来前景。当前的特点是将 ML 算法用于疾病诊断和风险预测、个性化治疗计划和高效医疗资源管理。值得注意的应用包括放射学和病理学的图像识别、疾病预后的预测分析以及根据患者个人情况开发精准医疗。此外，文章还讨论了 ML 医疗应用的未来前景和新兴趋势，包括预测分析在预先识别健康问题方面的潜力、整合可穿戴设备和远程监控以实现持续的患者护理，以及 ML 与基因组学在个性化医疗方面的交叉应用。本文的总体目标是让医疗保健专业人士、研究人员和决策者深入了解 ML 在医疗保健领域的应用现状，并展望机器学习为未来医疗保健服务和患者治疗效果带来的变革潜力。
{"title":"Machine Learning Applications in Healthcare: Current Trends and Future Prospects","authors":"Dr. José Gabriel Carrasco Ramírez., Md.mafiqul Islam, Asm Ibnul Hasan Even","doi":"10.60087/jaigs.v1i1.33","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.33","url":null,"abstract":"The integration of machine learning (ML) in healthcare has witnessed remarkable advancements, transforming the landscape of medical diagnosis, treatment, and overall patient care. This article provides a comprehensive review of the current trends and future prospects of machine learning applications in the healthcare domain.The current landscape is characterized by the utilization of ML algorithms for disease diagnosis and risk prediction, personalized treatment plans, and efficient healthcare resource management. Notable applications include image recognition for radiology and pathology, predictive analytics for disease prognosis, and the development of precision medicine tailored to individual patient profiles.This review explores the evolving role of ML in improving patient outcomes, enhancing clinical decision-making, and optimizing healthcare workflows. It delves into the challenges faced in integrating ML into existing healthcare systems, such as data privacy concerns, interpretability of complex models, and the need for robust validation processes.Additionally, the article discusses future prospects and emerging trends in ML healthcare applications, including the potential for predictive analytics to preemptively identify health issues, the integration of wearable devices and remote monitoring for continuous patient care, and the intersection of ML with genomics for personalized medicine.The overarching goal of this article is to provide healthcare professionals, researchers, and policymakers with insights into the current state of ML applications in healthcare, along with an outlook on the transformative potential that machine learning holds for the future of healthcare delivery and patient outcomes.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"55 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep Reinforcement Learning (DRL) has emerged as a transformative paradigm in the field of artificial intelligence (AI), offering unprecedented capabilities in decision-making across diverse domains. This article explores the profound impact of DRL on enhancing the decision-making capabilities of AI systems, elucidating its underlying principles, applications, and implications.DRL represents a fusion of deep learning and reinforcement learning, enabling machines to learn complex behaviors and make decisions by interacting with their environment. The utilization of neural networks allows DRL algorithms to handle high-dimensional input spaces, making it well-suited for tasks that involve intricate decision-making processes.One of the key strengths of DRL lies in its ability to address problems with sparse and delayed rewards, common challenges in traditional reinforcement learning. Through a process of trial and error, DRL algorithms can learn optimal decision strategies by navigating through a vast decision space, adapting to dynamic environments, and maximizing cumulative rewards over time.The applications of DRL span various domains, including robotics, finance, healthcare, gaming, and autonomous systems. In robotics, DRL facilitates the development of intelligent agents capable of autonomously navigating complex environments, performing intricate tasks, and adapting to unforeseen circumstances. In finance, DRL is leveraged for portfolio optimization, algorithmic trading, and risk management, demonstrating its potential to revolutionize traditional financial strategies.
{"title":"Deep Reinforcement Learning Unleashing the Power of AI in Decision-Making","authors":"Jeff Shuford","doi":"10.60087/jaigs.v1i1.36","DOIUrl":"https://doi.org/10.60087/jaigs.v1i1.36","url":null,"abstract":"Deep Reinforcement Learning (DRL) has emerged as a transformative paradigm in the field of artificial intelligence (AI), offering unprecedented capabilities in decision-making across diverse domains. This article explores the profound impact of DRL on enhancing the decision-making capabilities of AI systems, elucidating its underlying principles, applications, and implications.DRL represents a fusion of deep learning and reinforcement learning, enabling machines to learn complex behaviors and make decisions by interacting with their environment. The utilization of neural networks allows DRL algorithms to handle high-dimensional input spaces, making it well-suited for tasks that involve intricate decision-making processes.One of the key strengths of DRL lies in its ability to address problems with sparse and delayed rewards, common challenges in traditional reinforcement learning. Through a process of trial and error, DRL algorithms can learn optimal decision strategies by navigating through a vast decision space, adapting to dynamic environments, and maximizing cumulative rewards over time.The applications of DRL span various domains, including robotics, finance, healthcare, gaming, and autonomous systems. In robotics, DRL facilitates the development of intelligent agents capable of autonomously navigating complex environments, performing intricate tasks, and adapting to unforeseen circumstances. In finance, DRL is leveraged for portfolio optimization, algorithmic trading, and risk management, demonstrating its potential to revolutionize traditional financial strategies.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"88 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the explosive rise of quantum computing, there has been intense competition in business and academics in the field of quantum optics in recent decades. The current invention's overall scalability in quantum computing has surpassed many orders of magnitude, whereas ubiquitous quantum computers can support up to hundreds of quantum bits, or thousands of qubits. Strong machines continue to be developed. As a result, ethnicity has served as the inspiration for a huge number of studies and reports. This essay offers an introduction for everyone who would truly like to understand more about the ideas of quant communication and computing from a machine learning standpoint. It starts with such an educational approach and goes on to cover important turning points and the latest advancements in quantum computing. In this research, these fundamental characteristics of such a virtual network are divided into four major challenges, each of which has been thoroughly examined. correspondingly, A, B, C, and D stand for quantum physics, networking, security, and algorithms. The main issues, important areas of research, and most recent advancements are discussed as the article comes to a close.
由于量子计算的爆炸性崛起，近几十年来，量子光学领域的商业和学术竞争十分激烈。目前发明的量子计算的整体可扩展性已经超过了许多数量级，而无处不在的量子计算机可以支持多达数百个量子比特或数千个量子比特。强机器仍在继续研发。因此，民族性成为大量研究和报告的灵感来源。这篇文章从机器学习的角度，为每一个真正想了解更多量子通信和计算思想的人提供了一个介绍。文章从这种教育方法入手，接着介绍了量子计算的重要转折点和最新进展。在这项研究中，虚拟网络的这些基本特征被分为四大挑战，每项挑战都经过了深入研究。A、B、C 和 D 分别代表量子物理、网络、安全和算法。文章最后讨论了主要问题、重要研究领域和最新进展。
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Due to the explosive rise of quantum computing, there has been intense competition in business and academics in the field of quantum optics in recent decades. The current invention's overall scalability in quantum computing has surpassed many orders of magnitude, whereas ubiquitous quantum computers can support up to hundreds of quantum bits, or thousands of qubits. Strong machines continue to be developed. As a result, ethnicity has served as the inspiration for a huge number of studies and reports. This essay offers an introduction for everyone who would truly like to understand more about the ideas of quant communication and computing from a machine learning standpoint. It starts with such an educational approach and goes on to cover important turning points and the latest advancements in quantum computing. In this research, these fundamental characteristics of such a virtual network are divided into four major challenges, each of which has been thoroughly examined. correspondingly, A, B, C, and D stand for quantum physics, networking, security, and algorithms. The main issues, important areas of research, and most recent advancements are discussed as the article comes to a close.
由于量子计算的爆炸性崛起，近几十年来，量子光学领域的商业和学术竞争十分激烈。目前发明的量子计算的整体可扩展性已经超过了许多数量级，而无处不在的量子计算机可以支持多达数百个量子比特或数千个量子比特。强机器仍在继续研发。因此，民族性成为大量研究和报告的灵感来源。这篇文章从机器学习的角度，为每一个真正想了解更多量子通信和计算思想的人提供了一个介绍。文章从这种教育方法入手，接着介绍了量子计算的重要转折点和最新进展。在这项研究中，虚拟网络的这些基本特征被分为四大挑战，每项挑战都经过了深入研究。A、B、C 和 D 分别代表量子物理、网络、安全和算法。文章最后讨论了主要问题、重要研究领域和最新进展。
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Dr. José Gabriel Carrasco Ramírez., Md.mafiqul Islam, Asm Ibnul Hasan Even
The integration of machine learning (ML) in healthcare has witnessed remarkable advancements, transforming the landscape of medical diagnosis, treatment, and overall patient care. This article provides a comprehensive review of the current trends and future prospects of machine learning applications in the healthcare domain.The current landscape is characterized by the utilization of ML algorithms for disease diagnosis and risk prediction, personalized treatment plans, and efficient healthcare resource management. Notable applications include image recognition for radiology and pathology, predictive analytics for disease prognosis, and the development of precision medicine tailored to individual patient profiles.This review explores the evolving role of ML in improving patient outcomes, enhancing clinical decision-making, and optimizing healthcare workflows. It delves into the challenges faced in integrating ML into existing healthcare systems, such as data privacy concerns, interpretability of complex models, and the need for robust validation processes.Additionally, the article discusses future prospects and emerging trends in ML healthcare applications, including the potential for predictive analytics to preemptively identify health issues, the integration of wearable devices and remote monitoring for continuous patient care, and the intersection of ML with genomics for personalized medicine.The overarching goal of this article is to provide healthcare professionals, researchers, and policymakers with insights into the current state of ML applications in healthcare, along with an outlook on the transformative potential that machine learning holds for the future of healthcare delivery and patient outcomes.
机器学习（ML）与医疗保健的结合取得了显著进展，改变了医疗诊断、治疗和整体患者护理的格局。本文全面回顾了机器学习在医疗保健领域应用的当前趋势和未来前景。当前的特点是将 ML 算法用于疾病诊断和风险预测、个性化治疗计划和高效医疗资源管理。值得注意的应用包括放射学和病理学的图像识别、疾病预后的预测分析以及根据患者个人情况开发精准医疗。此外，文章还讨论了 ML 医疗应用的未来前景和新兴趋势，包括预测分析在预先识别健康问题方面的潜力、整合可穿戴设备和远程监控以实现持续的患者护理，以及 ML 与基因组学在个性化医疗方面的交叉应用。本文的总体目标是让医疗保健专业人士、研究人员和决策者深入了解 ML 在医疗保健领域的应用现状，并展望机器学习为未来医疗保健服务和患者治疗效果带来的变革潜力。
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