Generative AI Decision-Making Attributes in Complex Health Services: A Rapid Review
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View abstract on PubMed
Summary
This summary is machine-generated.Generative AI (GAI) shows promise in healthcare but poses risks due to its potential for bias and inaccuracy. Further research is needed before widespread adoption in complex health services.
Area Of Science
- Artificial Intelligence
- Health Services Research
- Bioethics
Background
- Generative Artificial Intelligence (Generative AI or GAI) presents a significant advancement in AI, incorporating both normative programming rules and descriptive elements influenced by programmer subjectivity and data discrepancies.
- GAI's capacity to generate both accurate and inaccurate information, support ethical and unethical decisions, and its inherent lack of transparency and accountability pose risks to decision-making in complex health services like policy and regulation.
Purpose Of The Study
- To conduct a rapid review identifying and mapping attributes influencing Generative AI decision-making in complex health services.
- To ensure an ethical approach to Generative AI design, engineering, and use by examining its decision-making processes from both rational and descriptive perspectives.
Main Methods
- A rapid review methodology adhering to PRISMA 2020 standards was employed.
- Inclusion and exclusion criteria were established for a systematic database search across ProQuest, Scopus, Web of Science, and Google Scholar.
- Articles published in 2023 and early 2024 were analyzed, with 21 selected from an initial 1,550 identified articles.
Main Results
- Learning, understanding, and bias were the most frequently cited attributes of Generative AI in the reviewed literature.
- While Generative AI offers advanced automation capabilities, it carries substantial risks, including potential for bias and 'hallucinations' (generating false information).
- Key detrimental factors identified include the lack of a moral compass, empathy, and consideration for privacy.
Conclusions
- Generative AI's current limitations, such as bias and lack of accountability, present significant risks for application in complex health services.
- Despite its potential for automation and pattern recognition, further development is required to address ethical concerns and ensure reliability before widespread adoption in healthcare.
- The study highlights the need for more work on Generative AI's ethical framework and decision-making processes to mitigate risks in sensitive areas like health policy and regulation.
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