Lewis Hains, Joshua G. Kovoor, Brandon Stretton, Aashray K. Gupta, Ammar Zaka, Gavin Carmichael, John M. Kefalianos, Win Le Shwe Sin Ei, Alasdair Leslie, Andrew Booth, Shrirajh Satheakeerthy, Alexander Beath, Yasser Arafat, Mathew O. Jacob, Martin Bruening, Weng Onn Chan, Stephen Bacchi
{"title":"当一种方式不适合所有人-人工智能在澳大利亚农村健康","authors":"Lewis Hains, Joshua G. Kovoor, Brandon Stretton, Aashray K. Gupta, Ammar Zaka, Gavin Carmichael, John M. Kefalianos, Win Le Shwe Sin Ei, Alasdair Leslie, Andrew Booth, Shrirajh Satheakeerthy, Alexander Beath, Yasser Arafat, Mathew O. Jacob, Martin Bruening, Weng Onn Chan, Stephen Bacchi","doi":"10.1111/ajr.70037","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Aims</h3>\n \n <p>Artificial intelligence (AI) is having an increasing impact on many aspects of our day-to-day lives. This change is also true in healthcare, with various tools being developed to hasten burdensome administrative tasks and increase overall healthcare efficiency, particularly in metropolitan centres.</p>\n </section>\n \n <section>\n \n <h3> Context</h3>\n \n <p>AI has remained comparatively clear of rural, regional and remote Australian hospitals, where it has the potential to provide significant benefits. Like previous health technology implementations, rural workforce requirements for AI maintenance and support may hinder AI deployment in these areas. While AI has been implemented successfully in metropolitan areas, these models may have limited translatability to rural health settings with significantly different administrative and healthcare systems.</p>\n </section>\n \n <section>\n \n <h3> Approach</h3>\n \n <p>AI may assist with key issues in rural centres such as resource allocation and timely patient transfer for higher level care. While the potential benefits of AI in rural centres are clear, one must consider key factors in rural centres that may limit the success of AI in these hospitals. Smaller rural populations may limit the ability to train location-specific models, and connectivity issues may impede their effective use.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Specific efforts are required to realise potential benefits of medical AI for rural Australia; addressing connectivity and workforce issues in rural areas is vital to allow for AI and large language models to help benefit rural centres.</p>\n </section>\n </div>","PeriodicalId":55421,"journal":{"name":"Australian Journal of Rural Health","volume":"33 3","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ajr.70037","citationCount":"0","resultStr":"{\"title\":\"When One Size Does not Fit All—Artificial Intelligence in Australian Rural Health\",\"authors\":\"Lewis Hains, Joshua G. Kovoor, Brandon Stretton, Aashray K. Gupta, Ammar Zaka, Gavin Carmichael, John M. Kefalianos, Win Le Shwe Sin Ei, Alasdair Leslie, Andrew Booth, Shrirajh Satheakeerthy, Alexander Beath, Yasser Arafat, Mathew O. Jacob, Martin Bruening, Weng Onn Chan, Stephen Bacchi\",\"doi\":\"10.1111/ajr.70037\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Aims</h3>\\n \\n <p>Artificial intelligence (AI) is having an increasing impact on many aspects of our day-to-day lives. This change is also true in healthcare, with various tools being developed to hasten burdensome administrative tasks and increase overall healthcare efficiency, particularly in metropolitan centres.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Context</h3>\\n \\n <p>AI has remained comparatively clear of rural, regional and remote Australian hospitals, where it has the potential to provide significant benefits. Like previous health technology implementations, rural workforce requirements for AI maintenance and support may hinder AI deployment in these areas. While AI has been implemented successfully in metropolitan areas, these models may have limited translatability to rural health settings with significantly different administrative and healthcare systems.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Approach</h3>\\n \\n <p>AI may assist with key issues in rural centres such as resource allocation and timely patient transfer for higher level care. While the potential benefits of AI in rural centres are clear, one must consider key factors in rural centres that may limit the success of AI in these hospitals. Smaller rural populations may limit the ability to train location-specific models, and connectivity issues may impede their effective use.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>Specific efforts are required to realise potential benefits of medical AI for rural Australia; addressing connectivity and workforce issues in rural areas is vital to allow for AI and large language models to help benefit rural centres.</p>\\n </section>\\n </div>\",\"PeriodicalId\":55421,\"journal\":{\"name\":\"Australian Journal of Rural Health\",\"volume\":\"33 3\",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-05-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ajr.70037\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Australian Journal of Rural Health\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/ajr.70037\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"NURSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Australian Journal of Rural Health","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/ajr.70037","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NURSING","Score":null,"Total":0}
When One Size Does not Fit All—Artificial Intelligence in Australian Rural Health
Aims
Artificial intelligence (AI) is having an increasing impact on many aspects of our day-to-day lives. This change is also true in healthcare, with various tools being developed to hasten burdensome administrative tasks and increase overall healthcare efficiency, particularly in metropolitan centres.
Context
AI has remained comparatively clear of rural, regional and remote Australian hospitals, where it has the potential to provide significant benefits. Like previous health technology implementations, rural workforce requirements for AI maintenance and support may hinder AI deployment in these areas. While AI has been implemented successfully in metropolitan areas, these models may have limited translatability to rural health settings with significantly different administrative and healthcare systems.
Approach
AI may assist with key issues in rural centres such as resource allocation and timely patient transfer for higher level care. While the potential benefits of AI in rural centres are clear, one must consider key factors in rural centres that may limit the success of AI in these hospitals. Smaller rural populations may limit the ability to train location-specific models, and connectivity issues may impede their effective use.
Conclusion
Specific efforts are required to realise potential benefits of medical AI for rural Australia; addressing connectivity and workforce issues in rural areas is vital to allow for AI and large language models to help benefit rural centres.
期刊介绍:
The Australian Journal of Rural Health publishes articles in the field of rural health. It facilitates the formation of interdisciplinary networks, so that rural health professionals can form a cohesive group and work together for the advancement of rural practice, in all health disciplines. The Journal aims to establish a national and international reputation for the quality of its scholarly discourse and its value to rural health professionals. All articles, unless otherwise identified, are peer reviewed by at least two researchers expert in the field of the submitted paper.