Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future Diagnostics
<h3>Background</h3><p dir="ltr">Artificial intelligence (AI) is transforming dermatopathology by enhancing diagnostic accuracy, efficiency, and precision medicine. Despite its promise, challenges such as dataset biases, underrepresentation of diverse populations, and limi...
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2025
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| _version_ | 1864513538060451840 |
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| author | Diala Ra'Ed Kamal Kakish (22330627) |
| author2 | Jehad Feras AlSamhori (17746809) Andy Noel Ramirez Fajardo (22330630) Lana N. Qaqish (22330633) Layan Ahmed Jaber (22330636) Rawan Abujudeh (22330639) Mohammad Hathal Mahmoud Al‐Zuriqat (22330642) Amina Yahya Mohammed (22330645) Abdulqadir J. Nashwan (11659453) |
| author2_role | author author author author author author author author |
| author_facet | Diala Ra'Ed Kamal Kakish (22330627) Jehad Feras AlSamhori (17746809) Andy Noel Ramirez Fajardo (22330630) Lana N. Qaqish (22330633) Layan Ahmed Jaber (22330636) Rawan Abujudeh (22330639) Mohammad Hathal Mahmoud Al‐Zuriqat (22330642) Amina Yahya Mohammed (22330645) Abdulqadir J. Nashwan (11659453) |
| author_role | author |
| dc.creator.none.fl_str_mv | Diala Ra'Ed Kamal Kakish (22330627) Jehad Feras AlSamhori (17746809) Andy Noel Ramirez Fajardo (22330630) Lana N. Qaqish (22330633) Layan Ahmed Jaber (22330636) Rawan Abujudeh (22330639) Mohammad Hathal Mahmoud Al‐Zuriqat (22330642) Amina Yahya Mohammed (22330645) Abdulqadir J. Nashwan (11659453) |
| dc.date.none.fl_str_mv | 2025-01-17T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1002/der2.70018 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Transforming_Dermatopathology_With_AI_Addressing_Bias_Enhancing_Interpretability_and_Shaping_Future_Diagnostics/30234127 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems Philosophy and religious studies Applied ethics artificial Intelligence in dermatopathology diagnostic bias explainable AI (XAI) multimodal diagnostics precision medicine |
| dc.title.none.fl_str_mv | Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future Diagnostics |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">Artificial intelligence (AI) is transforming dermatopathology by enhancing diagnostic accuracy, efficiency, and precision medicine. Despite its promise, challenges such as dataset biases, underrepresentation of diverse populations, and limited transparency hinder its widespread adoption. Addressing these gaps can set a new standard for equitable and patient‐centered care. To evaluate how AI mitigates biases, improves interpretability, and promotes inclusivity in dermatopathology while highlighting novel technologies like multimodal models and explainable AI (XAI).</p><h3>Results</h3><p dir="ltr">AI‐driven tools demonstrate significant improvements in diagnostic precision, particularly through multimodal models that integrate histological, genetic, and clinical data. Inclusive frameworks, such as the Monk scale, and advanced segmentation methods effectively address dataset biases. However, challenges such as the “black box” nature of AI, ethical concerns about data privacy, and limited access to advanced technologies in low‐resource settings remain.</p><h3>Conclusion</h3><p dir="ltr">AI offers transformative potential in dermatopathology, enabling equitable, and innovative diagnostics. Overcoming persistent challenges will require collaboration among dermatopathologists, AI developers, and policymakers. By prioritizing inclusivity, transparency, and interdisciplinary efforts, AI can redefine global standards in dermatopathology and foster patient‐centered care.</p><h2>Other Information</h2><p dir="ltr">Published in: Dermatological Reviews<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1002/der2.70018" target="_blank">https://dx.doi.org/10.1002/der2.70018</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_000705a62041d212a13f82ae4f9eb496 |
| identifier_str_mv | 10.1002/der2.70018 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30234127 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future DiagnosticsDiala Ra'Ed Kamal Kakish (22330627)Jehad Feras AlSamhori (17746809)Andy Noel Ramirez Fajardo (22330630)Lana N. Qaqish (22330633)Layan Ahmed Jaber (22330636)Rawan Abujudeh (22330639)Mohammad Hathal Mahmoud Al‐Zuriqat (22330642)Amina Yahya Mohammed (22330645)Abdulqadir J. Nashwan (11659453)Health sciencesHealth services and systemsPhilosophy and religious studiesApplied ethicsartificial Intelligence in dermatopathologydiagnostic biasexplainable AI (XAI)multimodal diagnosticsprecision medicine<h3>Background</h3><p dir="ltr">Artificial intelligence (AI) is transforming dermatopathology by enhancing diagnostic accuracy, efficiency, and precision medicine. Despite its promise, challenges such as dataset biases, underrepresentation of diverse populations, and limited transparency hinder its widespread adoption. Addressing these gaps can set a new standard for equitable and patient‐centered care. To evaluate how AI mitigates biases, improves interpretability, and promotes inclusivity in dermatopathology while highlighting novel technologies like multimodal models and explainable AI (XAI).</p><h3>Results</h3><p dir="ltr">AI‐driven tools demonstrate significant improvements in diagnostic precision, particularly through multimodal models that integrate histological, genetic, and clinical data. Inclusive frameworks, such as the Monk scale, and advanced segmentation methods effectively address dataset biases. However, challenges such as the “black box” nature of AI, ethical concerns about data privacy, and limited access to advanced technologies in low‐resource settings remain.</p><h3>Conclusion</h3><p dir="ltr">AI offers transformative potential in dermatopathology, enabling equitable, and innovative diagnostics. Overcoming persistent challenges will require collaboration among dermatopathologists, AI developers, and policymakers. By prioritizing inclusivity, transparency, and interdisciplinary efforts, AI can redefine global standards in dermatopathology and foster patient‐centered care.</p><h2>Other Information</h2><p dir="ltr">Published in: Dermatological Reviews<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1002/der2.70018" target="_blank">https://dx.doi.org/10.1002/der2.70018</a></p>2025-01-17T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1002/der2.70018https://figshare.com/articles/journal_contribution/Transforming_Dermatopathology_With_AI_Addressing_Bias_Enhancing_Interpretability_and_Shaping_Future_Diagnostics/30234127CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/302341272025-01-17T09:00:00Z |
| spellingShingle | Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future Diagnostics Diala Ra'Ed Kamal Kakish (22330627) Health sciences Health services and systems Philosophy and religious studies Applied ethics artificial Intelligence in dermatopathology diagnostic bias explainable AI (XAI) multimodal diagnostics precision medicine |
| status_str | publishedVersion |
| title | Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future Diagnostics |
| title_full | Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future Diagnostics |
| title_fullStr | Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future Diagnostics |
| title_full_unstemmed | Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future Diagnostics |
| title_short | Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future Diagnostics |
| title_sort | Transforming Dermatopathology With AI: Addressing Bias, Enhancing Interpretability, and Shaping Future Diagnostics |
| topic | Health sciences Health services and systems Philosophy and religious studies Applied ethics artificial Intelligence in dermatopathology diagnostic bias explainable AI (XAI) multimodal diagnostics precision medicine |