{
  "abstract": "Unlabelled\n\nAI is increasingly being applied across the perioperative continuum. AI should be viewed as a technology that can reduce cognitive burden, collate fragmented data into clinically meaningful information, support decision-making, and extend precision education to clinicians and patients. This editorial accompanies the JMIR Perioperative Medicine webinar held in May 2026 and presents our perspectives-informed by our clinical experience-on where AI can assist perioperative clinicians and surgical patients, the evidence available today, what is still needed, and how clinicians can prepare to practice alongside AI tools. We consider opportunities in preoperative risk assessment and optimization; intraoperative intelligent decision support; emerging technologies, such as surgical digital twins; and postoperative recovery, monitoring, and prediction of clinical deterioration. Most AI applications remain early and require large-scale, prospective validation across diverse populations and resource settings. Realizing their potential also requires confronting substantial challenges, including limited and fragmented training data, the specialized models needed to interpret the large volumes of data captured in operative videos and during physiological monitoring, biases embedded in data and algorithms or biases inherent in human-AI interactions, and the determination of where human oversight is essential and where it is not feasible. When used responsibly, with human judgment remaining paramount, AI offers an unprecedented opportunity to advance perioperative care.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Medicine, Stanford University, 300 Pasteur Drive, Palo Alto, CA, 94304, United States, 1 6507234000.",
    "Department of Anesthesiology, Cleveland Clinic, Cleveland Clinic Lerner College of Medicine, Cleveland, OH, United States."
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Nidhi Rohatgi",
      "orcid": "0000-0003-4574-0283"
    },
    {
      "affs": [
        1
      ],
      "name": "Piyush Mathur",
      "orcid": "0000-0003-3777-8767"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/aj43w262hrn8n.bib",
  "cited_by": 0,
  "doi": "10.2196/107155",
  "fulltext_url": "https://europepmc.org/articles/PMC13722096",
  "id": "aj43w262hrn8n",
  "issns": [
    "2561-9128"
  ],
  "landing_url": "https://europepmc.org/article/MED/42961855",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7224736538",
  "pdf_url": "https://europepmc.org/articles/PMC13722096?pdf=render",
  "pmcid": "PMC13722096",
  "pmid": "42961855",
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/aj43w262hrn8n.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T03:21:21Z",
      "native_id": "MED:42961855",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42961855"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Advancing Perioperative Medicine With AI: Current Landscape and Research Priorities.",
  "title_text": "Advancing Perioperative Medicine With AI: Current Landscape and Research Priorities.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T03:21:21Z",
  "url": "https://research.draconitic.com/paper/aj43w262hrn8n",
  "venue": "JMIR perioperative medicine",
  "versions": []
}