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DTSTAMP:20260506T171043Z
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DESCRIPTION:[]\n\nMedical imaging\, including magnetic resonance imaging (M
 RI) and computed tomography (CT)\, is central to modern diagnosis and trea
 tment planning. Although contemporary radiological AI systems can achieve 
 fast and accurate diagnoses\, most offer limited user interaction within c
 linical workflows. This gap hinders adoption by reducing transparency\, ad
 aptability\, and trust. This talk will showcase research from our Health-X
  Lab on building radiological AI systems designed to collaborate with clin
 icians through intuitive and natural interaction channels. Specifically\, 
 I will highlight three lines of work: leveraging human visual attention du
 ring radiological reading to better align AI models with expert behavior\;
  enabling flexible\, language-based interaction with medical images\; and 
 developing interactive AI agents that support real-time\, user-driven anal
 ysis. Together\, these approaches illustrate how integrating visual percep
 tion\, language\, and interaction can transform AI from passive tools into
  effective clinical co-pilots\, enhancing usability\, interpretability\, a
 nd seamless workflow integration.\n\nRoom: 321\, Bldg: Duff Medical Buildi
 ng\, 3775 Rue University\, Montreal\, Quebec\, Canada\, Virtual: https://e
 vents.vtools.ieee.org/m/558822
LOCATION:Room: 321\, Bldg: Duff Medical Building\, 3775 Rue University\, Mo
 ntreal\, Quebec\, Canada\, Virtual: https://events.vtools.ieee.org/m/55882
 2
ORGANIZER:milad.mokhtari@mail.mcgill.ca
SEQUENCE:19
SUMMARY:Towards building AI Assistants for Doctors: Vision\, Language\, and
  Interaction
URL;VALUE=URI:https://events.vtools.ieee.org/m/558822
X-ALT-DESC:Description: <br /><p class="MsoNormal" style="margin-bottom: 6.
 0pt\; text-align: justify\; line-height: normal\; mso-layout-grid-align: n
 one\; text-autospace: none\;"><img src="https://events.vtools.ieee.org/vto
 ols_ui/media/display/55b77b51-aafe-4610-8e84-dd92526eb454" alt="" width="1
 000" height="416"></p>\n<p class="MsoNormal" style="margin-bottom: 6.0pt\;
  text-align: justify\; line-height: normal\; mso-layout-grid-align: none\;
  text-autospace: none\;"><span lang="EN-CA" style="font-size: 12pt\; font-
 family: helvetica\, arial\, sans-serif\;">Medical imaging\, including magn
 etic resonance imaging (MRI) and computed tomography (CT)\, is central to 
 modern diagnosis and treatment planning. Although contemporary radiologica
 l AI systems can achieve fast and accurate diagnoses\, most offer limited 
 user interaction within clinical workflows. This gap hinders adoption by r
 educing transparency\, adaptability\, and trust. This talk will showcase r
 esearch from our Health-X Lab on building radiological AI systems designed
  to collaborate with clinicians through intuitive and natural interaction 
 channels. Specifically\, I will highlight three lines of work: leveraging 
 human visual attention during radiological reading to better align AI mode
 ls with expert behavior\; enabling flexible\, language-based interaction w
 ith medical images\; and developing interactive AI agents that support rea
 l-time\, user-driven analysis. Together\, these approaches illustrate how 
 integrating visual perception\, language\, and interaction can transform A
 I from passive tools into effective clinical co-pilots\, enhancing usabili
 ty\, interpretability\, and seamless workflow integration.</span></p>
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