An Overview and Methodical Strategy to Counteract the Medical Data Shortage for AI Applications.
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| Title: | An Overview and Methodical Strategy to Counteract the Medical Data Shortage for AI Applications. |
|---|---|
| Authors: | Menne, Firman1 firman@universitasbosowa.ac.id, Kumar, Arya2 arya.kumarfcm@kiit.ac.in, Debyani, Devi3 devi.d@srisriuniversity.edu.in |
| Source: | International Journal of Online & Biomedical Engineering. 2026, Vol. 22 Issue 6, p139-155. 17p. |
| Subjects: | Machine learning, Acquisition of data, Prognostic models, Algorithmic bias, Heterogeneity, Artificial intelligence, Medical care |
| Abstract: | Artificial intelligence (AI) has the power to improve healthcare systems. Additionally, AI has the potential to improve the accuracy and fairness of medical facilities. The amount of data we currently have is inadequate despite an increase in new data. There can be an issue in this area since some health-related diseases occur less frequently than others. The amount and complexity of health-related data limit our ability to collect this type of information since this collection is expensive and complicated. At times, there are either not enough subjects included in the study or, collectively across studies, there is a lack of subject numbers. As AI and health care evolve, the performance of an ML (machine learning) model will always be poor if it does not have sufficient/adequate amounts of data upon which to learn. As a result, the models may be biased and perform poorly in medical settings. This study looks at the problems caused by a lack of information in the healthcare sector. It talks about what this means and how we can fix these issues. The study also looks at how specialists in machine learning (ML) are addressing these issues and how these concepts can be used in medical settings and in the health sector through models of machine learning. This review aims to provide researchers looking to create trustworthy predictive models using ML for healthcare purposes with a useful resource. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Online & Biomedical Engineering is the property of International Journal of Online Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194731186 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Overview and Methodical Strategy to Counteract the Medical Data Shortage for AI Applications. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Menne%2C+Firman%22">Menne, Firman</searchLink><relatesTo>1</relatesTo><i> firman@universitasbosowa.ac.id</i><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Arya%22">Kumar, Arya</searchLink><relatesTo>2</relatesTo><i> arya.kumarfcm@kiit.ac.in</i><br /><searchLink fieldCode="AR" term="%22Debyani%2C+Devi%22">Debyani, Devi</searchLink><relatesTo>3</relatesTo><i> devi.d@srisriuniversity.edu.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Online+%26+Biomedical+Engineering%22">International Journal of Online & Biomedical Engineering</searchLink>. 2026, Vol. 22 Issue 6, p139-155. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink><br /><searchLink fieldCode="DE" term="%22Prognostic+models%22">Prognostic models</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithmic+bias%22">Algorithmic bias</searchLink><br /><searchLink fieldCode="DE" term="%22Heterogeneity%22">Heterogeneity</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+care%22">Medical care</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Artificial intelligence (AI) has the power to improve healthcare systems. Additionally, AI has the potential to improve the accuracy and fairness of medical facilities. The amount of data we currently have is inadequate despite an increase in new data. There can be an issue in this area since some health-related diseases occur less frequently than others. The amount and complexity of health-related data limit our ability to collect this type of information since this collection is expensive and complicated. At times, there are either not enough subjects included in the study or, collectively across studies, there is a lack of subject numbers. As AI and health care evolve, the performance of an ML (machine learning) model will always be poor if it does not have sufficient/adequate amounts of data upon which to learn. As a result, the models may be biased and perform poorly in medical settings. This study looks at the problems caused by a lack of information in the healthcare sector. It talks about what this means and how we can fix these issues. The study also looks at how specialists in machine learning (ML) are addressing these issues and how these concepts can be used in medical settings and in the health sector through models of machine learning. This review aims to provide researchers looking to create trustworthy predictive models using ML for healthcare purposes with a useful resource. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Online & Biomedical Engineering is the property of International Journal of Online Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194731186 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3991/ijoe.v22i06.61535 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 139 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Acquisition of data Type: general – SubjectFull: Prognostic models Type: general – SubjectFull: Algorithmic bias Type: general – SubjectFull: Heterogeneity Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Medical care Type: general Titles: – TitleFull: An Overview and Methodical Strategy to Counteract the Medical Data Shortage for AI Applications. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Menne, Firman – PersonEntity: Name: NameFull: Kumar, Arya – PersonEntity: Name: NameFull: Debyani, Devi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 26268493 Numbering: – Type: volume Value: 22 – Type: issue Value: 6 Titles: – TitleFull: International Journal of Online & Biomedical Engineering Type: main |
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