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				<full_title>Journal of Artificial Intelligence for Healthcare Systems</full_title>
				<abbrev_title>J. Artif. Intell. Healthc. Syst.</abbrev_title>
				<issn>3149-8981</issn>
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					<year>2026</year>
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					<volume>5</volume>
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				<issue>2</issue>
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					<title>Multimodal Vision-Language Model for Joint Interpretation of Chest X-Ray Images and Free-Text Radiology Requests to Generate Structured Preliminary Reports</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Hassan</given_name>
            <surname>Rahman</surname>
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            <given_name>Tariq</given_name>
            <surname>Mahmood</surname>
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          					<person_name sequence="additional" contributor_role="author">
            <given_name>Ali</given_name>
            <surname>Raza</surname>
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								<publication_date>
					<year>2026</year>
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					  <unstructured_citation>Wang X, Peng Y, Lu L, Lu Z, Summers RM. Tienet: text-image embedding network for common thorax disease classification and reporting in chest X-rays. In: Proc IEEE Conf Comput Vis Pattern Recognit; 2018. p. 9049-58.</unstructured_citation>
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          					<citation key="rk-10.68159/u145014357-80375bfd-08c4-47d2-b601-618f101d415e">
					  <unstructured_citation>Li Y, Liang X, Hu Z, Xing EP. Hybrid retrieval-generation reinforced agent for medical image report generation. Adv Neural Inf Process Syst. 2018;31.</unstructured_citation>
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          					<citation key="rk-10.68159/u145014357-758ef122-90a4-4120-a442-e3a85a04507b">
					  <unstructured_citation>Liu F, Ge S, Wu X. Competence-based multimodal curriculum learning for medical report generation. In: Proc 59th Annu Meet Assoc Comput Linguist Int Joint Conf Nat Lang Process; 2021 Aug. p. 3001-12.</unstructured_citation>
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          					<citation key="rk-10.68159/u145014357-f510a11c-56c0-4f77-860a-587ce4bce426">
					  <unstructured_citation>Chen Z, Song Y, Chang TH, Wan X. Generating radiology reports via memory-driven transformer. In: Proc Conf Empir Methods Nat Lang Process; 2020 Nov. p. 1439-49.</unstructured_citation>
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					  <unstructured_citation>Miura Y, Zhang Y, Tsai E, Langlotz C, Jurafsky D. Improving factual completeness and consistency of image-to-text radiology report generation. In: Proc Conf North Am Chapter Assoc Comput Linguist Hum Lang Technol; 2021 Jun. p. 5288-304.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/u145014357-1caf7239-7edf-43ba-9f4b-9c821517f416">
					  <unstructured_citation>Zhang Y, Jiang H, Miura Y, Manning CD, Langlotz CP. Contrastive learning of medical visual representations from paired images and text. In: Mach Learn Healthc Conf. PMLR; 2022 Dec 31. p. 2-25.</unstructured_citation>
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          					<citation key="rk-10.68159/u145014357-750a7d6e-82c5-494d-a801-92f16566dd86">
					  <unstructured_citation>Huang SC, Shen L, Lungren MP, Yeung S. Gloria: a multimodal global-local representation learning framework for label-efficient medical image recognition. In: Proc IEEE/CVF Int Conf Comput Vis; 2021. p. 3942-51.</unstructured_citation>
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          					<citation key="rk-10.68159/u145014357-be4db73c-ecc2-4b11-b00b-73c748dfa44f">
					  <unstructured_citation>Tiu E, Talius E, Patel P, Langlotz CP, Ng AY, Rajpurkar P. Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning. Nat Biomed Eng. 2022;6(12):1399-406.</unstructured_citation>
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          					<citation key="rk-10.68159/u145014357-02c4c1d4-f64b-4b08-994b-45aa6aa0b6f4">
					  <unstructured_citation>Liu F, Wu X, Ge S, Fan W, Zou Y. Exploring and distilling posterior and prior knowledge for radiology report generation. In: Proc IEEE/CVF Conf Comput Vis Pattern Recognit; 2021. p. 13753-62.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/u145014357-65e38fcc-8144-44e8-b15d-5e8ba43e6057">
					  <unstructured_citation>Chen Z, Shen Y, Song Y, Wan X. Cross-modal memory networks for radiology report generation. In: Proc 59th Annu Meet Assoc Comput Linguist Int Joint Conf Nat Lang Process; 2021 Aug. p. 5904-14.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/u145014357-78f92dd5-bf0c-41ab-b16e-5ecdf5d5757c">
					  <unstructured_citation>Wang Z, Liu L, Wang L, Zhou L. Metransformer: radiology report generation by transformer with multiple learnable expert tokens. In: Proc IEEE/CVF Conf Comput Vis Pattern Recognit; 2023. p. 11558-67.</unstructured_citation>
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          					<citation key="rk-10.68159/u145014357-0e2115dd-daf0-4b7b-a0e5-86440bc1e06b">
					  <unstructured_citation>Thawakar OC, Shaker AM, Mullappilly SS, Cholakkal H, Anwer RM, Khan S, et al. Xraygpt: chest radiographs summarization using large medical vision-language models. In: Proc 23rd Workshop Biomed Nat Lang Process; 2024 Aug. p. 440-48.</unstructured_citation>
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          					<citation key="rk-10.68159/u145014357-b25eb4c4-7ad0-469c-97b7-142dc605e114">
					  <unstructured_citation>Pellegrini C, Özsoy E, Busam B, Navab N, Keicher M. Radialog: a large vision-language model for radiology report generation and conversational assistance. arXiv. 2023;arXiv:2311.18681.</unstructured_citation>
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          					<citation key="rk-10.68159/u145014357-b481c986-f20f-49f9-aea0-580c88aec231">
					  <unstructured_citation>Chen Z, Varma M, Xu J, Paschali M, Van Veen D, Johnston A, et al. A vision-language foundation model to enhance efficiency of chest X-ray interpretation. arXiv. 2024;arXiv:2401.12208.</unstructured_citation>
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          					<citation key="rk-10.68159/u145014357-962c3b64-ed11-4ca9-bf61-cdb8306b2bf4">
					  <unstructured_citation>Bannur S, Bouzid K, Castro DC, Schwaighofer A, Thieme A, Bond-Taylor S, et al. Maira-2: grounded radiology report generation. arXiv. 2024;arXiv:2406.04449.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/u145014357-d074df31-08c8-4c7c-a69e-d69bd29b3dcc">
					  <unstructured_citation>Wang Z, Wu Z, Agarwal D, Sun J. Medclip: contrastive learning from unpaired medical images and text. In: Proc Conf Empir Methods Nat Lang Process; 2022 Dec. p. 3876-87.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/u145014357-fce5f3a7-ee8e-41b6-81df-489199df39ba">
					  <unstructured_citation>Boecking B, Usuyama N, Bannur S, Castro DC, Schwaighofer A, Hyland S, et al. Making the most of text semantics to improve biomedical vision-language processing. In: Eur Conf Comput Vis. Cham: Springer; 2022. p. 1-21.</unstructured_citation>
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          					<citation key="rk-10.68159/u145014357-8dee03f2-0d80-4a8a-9a66-1915cf1bab43">
					  <unstructured_citation>Bannur S, Hyland S, Liu Q, Perez-Garcia F, Ilse M, Castro DC, et al. Learning to exploit temporal structure for biomedical vision-language processing. In: Proc IEEE/CVF Conf Comput Vis Pattern Recognit; 2023. p. 15016-27.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/u145014357-5974b033-8262-4863-bb43-6f012dfb928f">
					  <unstructured_citation>Tanida T, Müller P, Kaissis G, Rueckert D. Interactive and explainable region-guided radiology report generation. In: Proc IEEE/CVF Conf Comput Vis Pattern Recognit; 2023. p. 7433-42.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/u145014357-01cde5ef-e837-45f4-8b1d-1e2561d0f445">
					  <unstructured_citation>Lee S, Youn J, Kim H, Kim M, Yoon SH. CXR-LLAVA: a multimodal large language model for interpreting chest X-ray images. Eur Radiol. 2025;35(7):4374-86.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/u145014357-1fb6e63d-0c6a-4465-94a5-cca343890b6c">
					  <unstructured_citation>Song S, Subramanyam A, Madejski I, Grossman RL. Lab-rag: label boosted retrieval augmented generation for radiology report generation. arXiv. 2024;arXiv:2411.16523.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/u145014357-5962b364-6a94-475f-8ee1-220400f523d2">
					  <unstructured_citation>Jing B, Xie P, Xing E. On the automatic generation of medical imaging reports. In: Proc 56th Annu Meet Assoc Comput Linguist; 2018. p. 2577-86.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/u145014357-13e2c314-4621-4980-8cfd-cff408421ce6">
					  <unstructured_citation>Yuan J, Liao H, Luo R, Luo J. Automatic radiology report generation based on multi-view image fusion and medical concept enrichment. In: Int Conf Med Image Comput Comput Assist Interv. Cham: Springer; 2019. p. 721-29.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/u145014357-4fe45d98-84d8-48b1-949a-55f05b5d3c99">
					  <unstructured_citation>Li M, Lin B, Chen Z, Lin H, Liang X, Chang X. Dynamic graph enhanced contrastive learning for chest X-ray report generation. In: Proc IEEE/CVF Conf Comput Vis Pattern Recognit; 2023. p. 3334-43.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/u145014357-7f998b98-7d52-496a-8ffa-1b763812592c">
					  <unstructured_citation>Smit A, Jain S, Rajpurkar P, Pareek A, Ng AY, Lungren M. Combining automatic labelers and expert annotations for accurate radiology report labeling using BERT. In: Proc Conf Empir Methods Nat Lang Process; 2020. p. 1500-19.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/u145014357-9e50f167-b502-4da1-aaa3-80a3930133f0">
					  <unstructured_citation>Delbrouck JB, Chambon P, Bluethgen C, Tsai E, Almusa O, Langlotz C. Improving the factual correctness of radiology report generation with semantic rewards. In: Findings Assoc Comput Linguist EMNLP; 2022. p. 4348-60.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/u145014357-5e872732-2dbd-4d0d-adc2-1f76a7460d53">
					  <unstructured_citation>Yu F, Endo M, Krishnan R, Pan I, Tsai A, Reis EP, et al. Evaluating progress in automatic chest X-ray radiology report generation. Patterns. 2023;4(9).</unstructured_citation>
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          					<citation key="rk-10.68159/u145014357-15d7f889-0454-48af-8290-60c41225a126">
					  <unstructured_citation>Tanno R, Worrall DE, Ghosh A, Kaden E, Sotiropoulos SN, Criminisi A, et al. Bayesian image quality transfer with CNNs: exploring uncertainty in dMRI super-resolution. In: Int Conf Med Image Comput Comput Assist Interv. Cham: Springer; 2017. p. 611-19.</unstructured_citation>
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