Abstract

Purpose of Review: Artificial intelligence (AI) in healthcare has evolved dramatically from early expert systems, which were initially considered replacements for clinical judgment, to today's collaborative frameworks that aim to augment physician decision-making. This evolution is particularly crucial in domains such as transplant surgery, where decisions carry irreversible consequences and require the integration of complex, often ambiguous data. Drawing on peer-reviewed literature from 2019 to 2025, we conducted a systematic review that analyzed key elements distinguishing successful human-AI partnerships from those that fail. Recent Findings: The ideal balance incorporates human expertise into AI systems through weighted integration approaches, rather than binary accept-or-reject paradigms. This nuanced integration requires the ability to understand and adapt to the unique context of each case, underscoring the complexity and importance of the work of medical professionals and researchers. For transplant surgeons, whose practice exemplifies complex decision-making, current AI approaches built on binary logic struggle to capture the nuanced reasoning required for effective decision-making. Evidence from randomized controlled trials and multicenter validation studies demonstrates how human-AI collaborative systems achieve superior outcomes compared to either human or AI performance alone. Summary: This systematic review traces the evolution of human-AI collaboration in medical decision-making, identifies gaps that limit true collaborative teaming, and examines how fuzzy logic systems offer a framework for supporting complex decisions while maintaining the clinical interpretability that surgeons require for confident decision-making

Department(s)

Engineering Management and Systems Engineering

Second Department

Electrical and Computer Engineering

Publication Status

Open Access

Keywords and Phrases

Clinical decision support; Fuzzy associative memory; Healthcare; Human-AI Collaboration; Organ procurement; Sociotechnical; Transplant surgery

International Standard Serial Number (ISSN)

2196-3029

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Springer, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

01 Dec 2026

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