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Cited by 189Artificial Intelligence ApplicationsSoftware Engineering Techniques and PracticesEthics and Social Impacts of AI

An artificial intelligence life cycle: from conception to production

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De Silva, Daswin, Alahakoon, Damminda·2022·Patterns
DOI 10.1016/j.patter.2022.100489PMC PMC9214328
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Abstract

This paper presents the "CDAC AI life cycle," a comprehensive life cycle for the design, development, and deployment of artificial intelligence (AI) systems and solutions. It addresses the void of a practical and inclusive approach that spans beyond the technical constructs to also focus on the challenges of risk analysis of AI adoption, transferability of prebuilt models, increasing importance of ethics and governance, and the composition, skills, and knowledge of an AI team required for successful completion. The life cycle is presented as the progression of an AI solution through its distinct phases-design, develop, and deploy-and 19 constituent stages from conception to production as applicable to any AI initiative. This life cycle addresses several critical gaps in the literature where related work on approaches and methodologies are adapted and not designed specifically for AI. A technical and organizational taxonomy that synthesizes the functional value of AI is a further contribution of this article.

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  • The landscape of guidance on how to develop safe and effective AI systems for healthcare is fragmented across hundreds of documents, largely with a focus on products that would be regulated as medical devices.

    “Moreover, the landscape of guidance on how to develop safe and effective AI systems for healthcare is fragmented across hundreds of documents, largely with a focus on products that would be regulated as medical devices [21].”

    From Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review (2026) · cites this paper as [21]

    The Results section (‘The CDAC AI life cycle’; page unavailable) describes a diversity of AI ethics frameworks and vague best practices, which supports the general point that guidance is fragmented. It counts 84 ethics frameworks as of 2019, not hundreds of healthcare guidance documents. The Preliminary risk assessment section mentions regulatory requirements for software-based medical devices in healthcare but does not say that most guidance focuses on those products. (LLM, conf=0.78)

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Authors
De Silva, D., & Alahakoon, D.
Affiliations
La Trobe University (AU)
ORCID
Venue
Patterns
Publisher
Elsevier BV
Published
2022-06
Volume
3
Issue
6
Pages
100489
ISSN
2666-3899 (ISSN-L)
DOI
10.1016/j.patter.2022.100489
PMC
PMC9214328
OpenAlex
W3197940319
Cited by
189
References
9
Citations by year
2026: 37 · 2025: 70 · 2024: 50 · 2023: 28 · 2022: 4
FWCI
12.50
Type
journal article
Language
en
Open access
Licence
Locations
  • Patterns (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • La Trobe University (cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
AI system design, AI system development, model transferability, AI ethics, AI governance, AI methodology

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