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Doctor Details

2024/01/25
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Doctor Detail

Asst Prof James Hallinan

Photo of Asst Prof James Hallinan

Designations:

  • Senior Consultant, Department of Diagnostic Imaging, National University Hospital
  • Senior Consultant, Department of Diagnostic Imaging, Alexandra Hospital
  • Senior Consultant, Division of Musculoskeletal Oncology, Department of Diagnostic Imaging, National University Cancer Institute, Singapore
  • Assistant Professor, Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore

Qualifications:

MBChB (Hons) (Bristol), FRCR (UK)

Specialties/Sub-specialties:

Diagnostic Radiology

Clinical Disciplines/Programmes:

Diagnostic Imaging/Radiology, Musculoskeletal Imaging

Special Interests:

AI in spine imaging, augmented reporting, spinal metastasis imaging, musculoskeletal MRI, peripheral nerve neuroradiology

Biosketch

Dr James Hallinan is a Senior Consultant at NUH and an Assistant Professor at NUS. A Bristol‑trained physician (MBChB Hons) and Fellow of the Royal College of Radiologists (UK), he completed subspecialty training in musculoskeletal MRI under Prof Donald Resnick at UC San Diego.

His research focuses on AI‑driven spine imaging. As Principal Investigator he secured the NMRC Clinician‑Scientist IRG‑NIG (2021) for “Deep Learning Pathway for Spine Metastases” and the NMRC Clinician Innovator Award (2023) for “Deep Learning Pipeline for Augmented MRI Spine Reporting,” totalling > S$640 k. He also leads a Practice Changing Innovation award deploying the “Spine AI” model clinically and maintains a technology‑development agreement with Siemens Healthineers.

Dr Hallinan has authored more than 90 peer‑reviewed papers; his recent first‑/senior‑author publications in Radiology (IF 29.1) showcase deep‑learning tools that halve lumbar‑MRI reporting time. His team’s 2025 Spine Journal paper further demonstrated AI‑driven improvements in cervical‑MRI accuracy and productivity.

Regionally recognised, he received the 2024 NCIS Best Surgical Oncology Poster and the 2023 ExxonMobil–NUS Research Fellowship, and was part of teams honoured with the 2022 AO Spine Best Basic Science Award and the 2021 APSS‑ASJ Best Clinical Research Award. He serves as an Affiliate Member of NCIS and reviews for Radiology, European Journal of Radiology, Spine Journal, and Cancers.

An advocate for clinically integrated AI, Dr Hallinan mentors radiology residents and speaks internationally on musculoskeletal radiology.

Research and Conferences

  • 15th ASEAN Association of Radiology Congress, 2011, Singapore.
  • Oral presentation: Hallinan J, Venkatesh SK, Peters L, So J (2011). Correlation of CT Volumetry with Tumour Staging of Gastric Carcinomas.
  • First runner-up: Young Radiologist Investigator’s Award.
  • Poster presentation: Hallinan J, Lim W, Hegde A (2011). Dilemmas and Diagnostic Difficulties in Meningioma. First runner-up: Best Poster Presentation (Educational).
  • European congress of Radiology, Vienna, Austria, 2011/2012 and 2013.
  • Invest in the Youth Scholarship awards for 2012 and 2013.
  • American Roentgen Ray Society, Chicago, USA, 2011.
  • Oral presentation: Hallinan J, Venkatesh SK, So J (2011). CT volumetry for assessment of response of advanced gastric carcinoma to neoadjuvant chemotherapy.
  • Radiological society of North America, annual conference, Chicago, USA.
  • Oral Presentation: Hallinan J, Tan CH, Pua U (2012). Dual-phase CT for the Detection of Active Haemorrhage in Abdominopelvic Trauma: Direct Comparison with Angiography.
  • Poster Presentation: Hallinan J, Tan CH, Pua U (2012). Emergent Computed Tomography for Acute Pelvic Trauma: Where is the Bleeder?

Awards

  • 2024 – Best Surgical Oncology Poster, NCIS Annual Research Meeting
  • 2023 – ExxonMobil–NUS Research Fellowship for Clinicians
  • 2022 – Best Basic Science Award, AO Spine Asia Pacific East Conference
  • 2021 – APSS‑ASJ Best Clinical Research Award, Asia Pacific Spine Society
  • 2018 – NUHS Academic Medicine Development Award (UC San Diego)

Journals & Publications

  1. Lee A, Wu J, Liu C, Makmur A, Ting YH, Lee S, Chan MDZ, Lim DSW, KhooVMH, Sng J, Ong HY, Tan A, Ge S, Muhamat Nor FE, Lim YT, Beh JCY, Yap QV, TanJH, Kumar N, Ooi BC, Hallinan JTPD. Using Deep Learning to Enhance ReportingEfficiency and Accuracy in Degenerative Cervical Spine MRI. Spine J. 2025 Mar26:S1529-9430(25)00157-3. doi: 10.1016/j.spinee.2025.03.009. Epub ahead ofprint. PMID: 40154625. (IF 4.9)
  2. Hallinan JTPD, Leow NW, Ong W, Lee A, Low YX, Chan MDZ, Devi GK, LohDDL, He SS, Nor FEM, Lim DSW, Teo EC, Low XZ, Furqan SM, Tham WWY, Tan JH,Kumar N, Makmur A, Ting Y. MRI spine request form enhancement and autoprotocoling using a secure institutional large language model. Spine J. 2024Nov 12:S1529-9430(24)01111-2. doi:10.1016/j.spinee.2024.10.021. Epub ahead ofprint. PMID: 39536908. (IF 4.9)
  3. Lee A, Wu J, Liu C, Makmur A, Ting YH, Nor FEM, Tan LY, Ong W, Tan WC,Lee YJ, Huang J, Beh JCY, Lim DSW, Low XZ, Teo EC, Chan YH, Lim JI, Lin S, TanJH, Kumar N, Ooi BC, Quek ST, Hallinan JTPD (last author). Deep learning modelfor automated diagnosis of degenerative cervical spondylosis and altered spinalcord signal on MRI. Spine J. 2024 Sep 30:S1529-9430(24)01038-6. doi:10.1016/j.spinee.2024.09.015. Epub ahead of print. PMID: 39357744. (IF 4.9)
  4. Low XZ, Furqan MS, Makmur A, Lim DSW, Liu RW, Lim X, Chan YH, Tan JH,Lau LL, Hallinan JTPD (last author). Automated Cobb angle measurement inscoliosis radiographs: A deep learning approach for screening. Ann Acad MedSingap. 2024 Oct 7;53(10):635-637. doi: 10.47102/annals-acadmedsg.2023300.PMID: 39508697. (IF 8.713)
  5. Lee A, Ong W, Makmur A, Ting YH, Tan WC, Lim SWD, Low XZ, Tan JJH,Kumar N, Hallinan JTPD (last author). Applications of Artificial Intelligenceand Machine Learning in Spine MRI. Bioengineering (Basel). 2024 Sep5;11(9):894. doi: 10.3390/bioengineering11090894. PMID: 39329636; PMCID:PMC11428307. (IF 5.046)
  6. Ong W, Lee A, Tan WC, Fong KTD, Lai DD, Tan YL, Low XZ, Ge S, MakmurA, Ong SJ, Ting YH, Tan JH, Kumar N, Hallinan JTPD (last author). OncologicApplications of Artificial Intelligence and Deep Learning Methods in CT SpineImaging- A Systematic Review. Cancers (Basel). 2024 Aug 28;16(17):2988. doi:10.3390/cancers16172988. PMID: 39272846; PMCID: PMC11394591. (IF 6.575)
  7. Hallinan JTPD, Zhu L, Zhang W, Ge S, Muhamat Nor FE, Ong HY, Eide SE, Cheng AJL, Kuah T, Lim DSW, Low XZ, Yeong KY, AlMuhaish MI, Alsooreti A M, Kumarakulasinghe NB, Teo EC, Yap QV, Chan YH, Lin S, Tan JH, Kumar N, Vellayappan BA, Ooi BC, Quek ST and Makmur A. Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT. Front. Oncol. 2023 May 4;13:1151073. doi:10.3389/fonc.2023.1151073. (IF 6.244)
  8. Hallinan JTPD. Deep Learning for Spine MRI: Reducing Time Not Quality. Radiology. 2023 Mar;306(3):e222410. doi: 10.1148/radiol.222410. Epub 2022 Nov 1. PMID: 36318033. (IF 29.146, TOP 10% JOURNAL)
  9. Lim DSW, Makmur A, Zhu L, Zhang W, Cheng AJL, Sia DSY, Eide SE, OngHY, Jagmohan P, Tan WC, Khoo VM, Wong YM, Thian YL, Baskar S, Teo EC, AlgazwiDAR, Yap QV, Chan YH, Tan JH, Kumar N, Ooi BC, Yoshioka H, Quek ST, HallinanJTPD (last author). Improved Productivity Using Deep Learning-assistedReporting for Lumbar Spine MRI. Radiology. 2022 Jun 14:220076. doi:10.1148/radiol.220076. Epub ahead of print. PMID: 35699577. (IF 29.146, TOP 10%JOURNAL)
  10. Hallinan JTPD, Zhu L, Yang K, Makmur A, Algazwi DAR, Thian YL, Lau S, Choo YS, Eide SE, Yap QV, Chan YH, Tan JH, Kumar N, Ooi BC, Yoshioka H, Quek ST. Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI. Radiology. 2021 Jul;300(1):130-138. doi: 10.1148/radiol.2021204289. PMID: 33973835. (IF 29.146, TOP 10% JOURNAL)

Professional Memberships

  • Royal College of Radiologists (UK)
  • Singapore Radiological Society
  • Asian Musculoskeletal Radiology Society

2025/06/25
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