Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and Opportunities
The healthcare sector in India has experienced significant transformations owing to the advancement in technology and infrastructure. Despite these transformations, there are major challenges to address critical issues like insufficient healthcare infrastructure for the country’s huge population, li...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | AI |
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| author | Sarat Kumar Chettri Rup Kumar Deka Manob Jyoti Saikia |
| author_facet | Sarat Kumar Chettri Rup Kumar Deka Manob Jyoti Saikia |
| author_sort | Sarat Kumar Chettri |
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| description | The healthcare sector in India has experienced significant transformations owing to the advancement in technology and infrastructure. Despite these transformations, there are major challenges to address critical issues like insufficient healthcare infrastructure for the country’s huge population, limited accessibility, shortage of skilled professionals, and high-quality care. Artificial intelligence (AI)-driven solutions have the potential to lessen the stress on India’s healthcare system; however, integrating trustworthy AI in the sector remains challenging due to ethical and regulatory constraints. This study aims to critically review the current status of the development of AI systems in Indian healthcare and how well it satisfies the ethical and legal aspects of AI, as well as to identify the challenges and opportunities in adoption of trustworthy AI in the Indian healthcare sector. This study reviewed 15 articles selected from a total of 1136 articles gathered from two electronic databases, PubMed and Google Scholar, as well as project websites. This study makes use of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). It finds that the existing studies mostly used conventional machine learning (ML) algorithms and artificial neural networks (ANNs) for a variety of tasks, such as drug discovery, disease surveillance systems, early disease detection and diagnostic accuracy, and management of healthcare resources in India. This study identifies a gap in the adoption of trustworthy AI in Indian healthcare and various challenges associated with it. It explores opportunities for developing trustworthy AI in Indian healthcare settings, prioritizing patient safety, data privacy, and compliance with ethical and legal standards. |
| format | Article |
| id | doaj-art-617dfc54614943ea85f5503aef723723 |
| institution | Kabale University |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | AI |
| spelling | doaj-art-617dfc54614943ea85f5503aef7237232025-01-24T13:17:23ZengMDPI AGAI2673-26882025-01-01611010.3390/ai6010010Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and OpportunitiesSarat Kumar Chettri0Rup Kumar Deka1Manob Jyoti Saikia2Department of Computer Applications, Assam Don Bosco University, Guwahati 781017, IndiaFaculty of Computer Technology, Assam down town University, Guwahati 781026, IndiaBiomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USAThe healthcare sector in India has experienced significant transformations owing to the advancement in technology and infrastructure. Despite these transformations, there are major challenges to address critical issues like insufficient healthcare infrastructure for the country’s huge population, limited accessibility, shortage of skilled professionals, and high-quality care. Artificial intelligence (AI)-driven solutions have the potential to lessen the stress on India’s healthcare system; however, integrating trustworthy AI in the sector remains challenging due to ethical and regulatory constraints. This study aims to critically review the current status of the development of AI systems in Indian healthcare and how well it satisfies the ethical and legal aspects of AI, as well as to identify the challenges and opportunities in adoption of trustworthy AI in the Indian healthcare sector. This study reviewed 15 articles selected from a total of 1136 articles gathered from two electronic databases, PubMed and Google Scholar, as well as project websites. This study makes use of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). It finds that the existing studies mostly used conventional machine learning (ML) algorithms and artificial neural networks (ANNs) for a variety of tasks, such as drug discovery, disease surveillance systems, early disease detection and diagnostic accuracy, and management of healthcare resources in India. This study identifies a gap in the adoption of trustworthy AI in Indian healthcare and various challenges associated with it. It explores opportunities for developing trustworthy AI in Indian healthcare settings, prioritizing patient safety, data privacy, and compliance with ethical and legal standards.https://www.mdpi.com/2673-2688/6/1/10healthcaretrustworthy AIlegal and ethical standardsdata privacychallenges and solutionsIndian healthcare |
| spellingShingle | Sarat Kumar Chettri Rup Kumar Deka Manob Jyoti Saikia Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and Opportunities AI healthcare trustworthy AI legal and ethical standards data privacy challenges and solutions Indian healthcare |
| title | Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and Opportunities |
| title_full | Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and Opportunities |
| title_fullStr | Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and Opportunities |
| title_full_unstemmed | Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and Opportunities |
| title_short | Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and Opportunities |
| title_sort | bridging the gap in the adoption of trustworthy ai in indian healthcare challenges and opportunities |
| topic | healthcare trustworthy AI legal and ethical standards data privacy challenges and solutions Indian healthcare |
| url | https://www.mdpi.com/2673-2688/6/1/10 |
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