International Journal of Medical Informatics

, volume 193 , pages 105689

Decoding the black box: Explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-A state-of-the art systematic review

Yusuf Abas Mohamed ^{1, 2}

Khoo Bee Ee ²

Bee Luan Khoo ³

Mohd Shahrimie Mohd Asaari ^{1, 2}

Ezane Aziz Mohd ⁴

Mohd Ezane Aziz ⁵

Fattah Rahiman Ghazali ^{4, 5}

Hide authors affiliations Show authors affiliations: 5 affiliations

School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), Malaysia. |

School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), Malaysia |

School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), Malaysia. Electronic address: beekhoo@eng.usm.my. |

⁴

Department of Radiology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia (USM), Kelantan, Malaysia |

⁵

Department of Radiology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia (USM), Kelantan, Malaysia. |

Publication type: Journal Article

Publication date: 2025-01-01

Elsevier

International Journal of Medical Informatics

scimago Q1

wos Q1

SJR: 1.189

CiteScore: 7.9

Impact factor: 4.1

ISSN: 13865056, 18728243

DOI: 10.1016/j.ijmedinf.2024.105689

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PubMed ID: 39522406

Abstract

ObjectiveExplainable Artificial Intelligence (XAI) is increasingly recognized as a crucial tool in cancer care, with significant potential to enhance diagnosis, prognosis, and treatment planning. However, the holistic integration of XAI across all stages of cancer care remains underexplored. This review addresses this gap by systematically evaluating the role of XAI in these critical areas, identifying key challenges and emerging trends.

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GOST |

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GOST Copy

Abas Mohamed Y. et al. Decoding the black box: Explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-A state-of-the art systematic review // International Journal of Medical Informatics. 2025. Vol. 193. p. 105689.

GOST all authors (up to 50) Copy

Abas Mohamed Y., Ee K. B., Khoo B. L., Shahrimie Mohd Asaari M., Mohd E. A., Aziz M. E., Rahiman Ghazali F. Decoding the black box: Explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-A state-of-the art systematic review // International Journal of Medical Informatics. 2025. Vol. 193. p. 105689.

RIS |

Cite this

RIS Copy

TY - JOUR

DO - 10.1016/j.ijmedinf.2024.105689

UR - https://linkinghub.elsevier.com/retrieve/pii/S1386505624003526

TI - Decoding the black box: Explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-A state-of-the art systematic review

T2 - International Journal of Medical Informatics

AU - Abas Mohamed, Yusuf

AU - Ee, Khoo Bee

AU - Khoo, Bee Luan

AU - Shahrimie Mohd Asaari, Mohd

AU - Mohd, Ezane Aziz

AU - Aziz, Mohd Ezane

AU - Rahiman Ghazali, Fattah

PY - 2025

DA - 2025/01/01

PB - Elsevier

SP - 105689

VL - 193

PMID - 39522406

SN - 1386-5056

SN - 1872-8243

ER -

BibTex

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BibTex (up to 50 authors) Copy

@article{2025_Abas Mohamed,

author = {Yusuf Abas Mohamed and Khoo Bee Ee and Bee Luan Khoo and Mohd Shahrimie Mohd Asaari and Ezane Aziz Mohd and Mohd Ezane Aziz and Fattah Rahiman Ghazali},

title = {Decoding the black box: Explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-A state-of-the art systematic review},

journal = {International Journal of Medical Informatics},

year = {2025},

volume = {193},

publisher = {Elsevier},

month = {jan},

url = {https://linkinghub.elsevier.com/retrieve/pii/S1386505624003526},

pages = {105689},

doi = {10.1016/j.ijmedinf.2024.105689}

}

Publisher

Elsevier

Journal

International Journal of Medical Informatics

scimago Q1

wos Q1

SJR

1.189

CiteScore

7.9

Impact factor

4.1

ISSN

13865056 (Print)

18728243 (Electronic)

Profiles

Bee Luan Khoo