{
  "abstract": "Objectives Current multimodal approaches to autoimmune disease diagnoses often rely solely on omics data or electronic health records, overlooking other valuable patient-centered data sources. This study aimed to gather patient perspectives on the development and use of artificial intelligence (AI) and machine learning technologies to support earlier treatment decisions in lupus care.Methods Three meetings with the University of North Carolina Thurston Arthritis Research Center Lupus Patient Community Advisory Board (CAB) were held between June and September 2025 over Zoom. Discussions followed a semi-structured format and were recorded and transcribed for thematic analysis.Results Eight lupus CAB members participated across the three meetings. Patient participants expressed nuanced perspectives on the integration of AI in lupus care. While many were open to AI as a supplemental tool, concerns were raised about limitations in diagnosing complex conditions like lupus. Participants acknowledged the potential of AI to enhance diagnostic accuracy and treatment personalization, particularly through synthesis of health records and identification of emerging therapies. However, participants emphasized the importance of clinician oversight and cautioned against overreliance on AI. Trust in AI was framed as contingent on long-term validation, transparency in data sources, and congruency with physician diagnoses. Privacy concerns were prevalent but often framed as part of broader societal data security issues. Participants highlighted the importance of maintaining face-to-face interactions, noting that AI could support, but not replace, clinician-patient relationships and face-to-face discussions.Conclusions Findings underscore the importance of patient engagement in AI development and highlight key considerations for ethical, representative, and patient-centered implementation of AI in lupus care.",
  "authors": [
    {
      "affiliations": [
        "University of North Carolina at Chapel Hill, Chapel Hill, USA"
      ],
      "name": "Tessa Englund"
    },
    {
      "affiliations": [
        "University of North Carolina at Chapel Hill, Chapel Hill, USA"
      ],
      "name": "Claire Timon"
    },
    {
      "affiliations": [
        "University of North Carolina at Chapel Hill, Chapel Hill, USA"
      ],
      "name": "Yueh Lee"
    },
    {
      "affiliations": [
        "University of North Carolina at Chapel Hill, Chapel Hill, USA"
      ],
      "name": "Becki Cleveland"
    },
    {
      "affiliations": [
        "University of California, San Diego, San Diego, USA"
      ],
      "name": "Marc Niethammer"
    },
    {
      "affiliations": [
        "University of North Carolina at Chapel Hill, Chapel Hill, USA"
      ],
      "name": "Saira Sheikh"
    }
  ],
  "title": "S2:05 Potential applications of artificial intelligence to inform lupus diagnosis & care: patient perspectives & insights",
  "uid": "bb1231e5-edd1-56e7-b7c3-ec12ce270af9"
}