The future of medicine or a threat? Artificial intelligence representation in Chicago Med

The paper examines how Season 8 (2022–23) of the popular American medical drama Chicago Med represents artificial intelligence in clinical settings and how those portrayals may influence viewers’ perceptions. Medical dramas, long noted for striving for accuracy and for educational intent, often introduce cutting-edge technologies and can shape public understanding through entertainment education and cultivation effects. Chicago Med reaches a large, global audience and in Season 8 integrated a prominent AI-enabled surgical suite (OR 2.0) and several ED-focused AI tools, providing a rich corpus to analyze representational patterns and ethical framings. Guided by prior research on medical dramas’ influence and on AI ethics, the study explores: (1) reasons for and outcomes of AI use in the series; (2) depiction of seven AI-related ethical issues salient in medicine (transparency, selective adherence, automation bias, responsibility gap, hallucination, unequal access, political dimensions); and (3) characters’ attitudes toward AI. The goal is to assess whether the show frames AI as the future of medicine, a threat, or something more nuanced, and how such framing might inform or sway audience opinions.

The literature review situates medical dramas as influential cultural products that aim for realism and involve experts to ensure credible depictions, thereby enabling entertainment education and cultivation effects. Studies show audiences often treat medical dramas as health information sources and that exposure can shape expectations and trust in healthcare and professionals. Genre-specific cultivation research indicates perceived realism mediates effects, with implications for attitudes toward medical practice. Prior analyses of Chicago Med and other series have examined topics such as COVID-19 representation, bioethics education, genetic screening, organ donation, research funding, and depictions of medical errors and their potential to influence public attitudes and trust. The review also synthesizes key AI ethics literature, highlighting seven issues especially relevant to medicine: transparency (and explainability), selective adherence to algorithmic advice, automation bias, responsibility gaps, hallucination in AI outputs, unequal access due to socioeconomic and data biases, and political dimensions including regulation, labor impacts, and governance over powerful technologies.

The study employs qualitative content analysis of Chicago Med Season 8 (22 episodes). Researchers identified all AI-related storylines across the season and organized analysis into two main domains: (1) the OR 2.0 surgical suite story arc and (2) emergency department (ED) AI applications. Coding focused on: (a) reasons for AI use and outcomes; (b) depiction of seven ethical issues (transparency, selective adherence, automation bias, responsibility gap, hallucination, unequal access, political dimensions); and (c) characters’ attitudes (optimism/trust, growing trust, growing cautiousness, antagonism). Within the OR 2.0 arc, the team cataloged surgeries, success/complication profiles, and ethical tensions (e.g., configuration opacity, data dependence, attribution of merit and blame). For the ED, the team reviewed administrative, diagnostic, and automation tools (e.g., EMR with speech-to-text, AI diagnostic supports, triage/prognostic aids, logistics/noise controls), noting successes, failures, and ethical dilemmas. The unit of analysis was the storyline within episodes, with cross-episode synthesis to capture evolving attitudes and consequences. Data source was the publicly available series; no human subjects were involved.

• Season 8 integrates AI extensively via the OR 2.0 surgical suite and multiple ED tools, enabling a multifaceted representation of medical AI.
• OR 2.0 use cases cluster into four indications: (1) solving otherwise inoperable cases (e.g., complex Crohn’s reconstruction; ankylosing spondylitis deformity); (2) maximizing outcomes (e.g., limb salvage, traumatic injury management, shorter recovery times); (3) addressing visually challenging cases (e.g., intraoperative MRI-guided procedures); and (4) promotional demonstrations to stakeholders.
• OR 2.0 surgical outcomes: 13 depicted operations with 11 successes; nevertheless, serious complications occur, including a fatal stroke linked to an AI “hallucinated” lesion that misled the surgeon.
• Ethical issues depicted around OR 2.0: • Transparency: Surgeons are unaware of certain system configurations; company representatives act as gatekeepers and can alter or delete data, compromising auditability and trust. • Selective adherence: Clinicians’ prior attitudes shape when they accept or reject AI advice; patients also display selective acceptance when recommendations align with their hopes. • Automation bias: In routine cases, OR 2.0 can guide novices successfully, but in novel/low-data cases, over-reliance risks skill erosion and near-failures; surgeons must reassert clinical judgment. • Responsibility gap: Ambiguity over credit for success and blame for errors (surgeon vs system vs developer) fuels interpersonal conflict and legal/moral uncertainty. • Hallucination: OR 2.0 identifies a non-existent lesion, contributing to a catastrophic outcome. • Unequal access: After initial success, the system is restricted to paying patients only, highlighting distributive injustice. • Political dimensions: Investor control shapes access and use policies, linking technological power to governance and profit motives.
• Attitudes toward OR 2.0 span: (1) optimism/trust (Dayton, Dupre, Marcel), (2) growing trust (Abrams), (3) growing cautiousness (Halstead, Song, nurse Maggie), and (4) antagonism (Archer). Patients expressing views range from enthusiasm to refusal, with gratitude common among beneficiaries.
• ED AI reforms produce mixed outcomes: • Positive/low-controversy: Speech-to-text EMR reduces administrative burden; a neural network–based diagnostic support helps identify rare pediatric illness when clinicians are out of options. • Problematic systems: An opioid-risk flagger (OpioHealth) issues false positives with opaque data practices (transparency concern) and fosters bias/selective adherence, leading to its discontinuation. • Resource triage tool predicts survival to allocate scarce blood; privileging utilitarian outputs clashes with clinicians’ compassionate exceptions, exposing tensions between algorithmic rationality and bedside ethics. • A diagnostic search engine suggests a potential plague, prompting unnecessary crisis responses until clinician observation corrects it; another case shows patient anxiety and self-endangerment when exposed to an exhaustive list of improbable diagnoses. • Automation/monitoring (noise sensors; logistics optimization) disrupt workflows and can harm vulnerable patients (e.g., triggering panic in a schizophrenic patient), fueling staff resistance and job-security fears.
• ED attitudes mirror the OR 2.0 arc: initial caution or resistance (Halstead, Archer, Lockwood) with some movement toward conditional acceptance for demonstrably helpful tools, alongside persistent concerns over dehumanization and labor displacement.

Findings indicate that Chicago Med delivers a nuanced, educationally potent portrayal of medical AI that neither demonizes nor uncritically celebrates the technology. By embedding AI into realistic surgical and ED contexts, the series foregrounds both clinical benefits (expanded operability, precision, efficiency) and ethically salient risks (opacity, over-reliance, accountability ambiguity, inequity, politicization). The OR 2.0 arc demonstrates how AI can augment human capability yet introduces new failure modes (e.g., hallucinated findings) and governance challenges (data control by vendors, paywalled access). The ED storylines underscore that not all AI is equal: highly targeted supports (speech-to-text EMR, well-curated diagnostic tools) can add value, while poorly transparent or context-insensitive systems can mislead clinicians, stigmatize patients, or erode patient-centered care. Characters’ diverse and evolving attitudes model reflective practice, showing that professional skepticism, selective adherence, and reaffirmation of clinical judgment remain vital safeguards. Collectively, the portrayals address the research questions by illustrating why AI is adopted (clinical advantage, efficiency, promotion), the outcomes (often beneficial but occasionally harmful), and the spectrum of ethical issues implicated, thereby highlighting the importance of careful implementation, oversight, and equitable governance in real-world medical AI.

The study shows that Chicago Med’s Season 8 provides a comprehensive, balanced depiction of medical AI that can inform public understanding. OR 2.0 showcases AI-enabled surgical innovation alongside transparency failures, automation bias, responsibility gaps, hallucination risks, and inequitable access driven by investor interests. ED reforms illustrate that administrative and well-curated diagnostic aids can be beneficial, whereas opaque flagging systems, blunt triage algorithms, and workflow-automation tools may undermine care or trust. The series’ refusal to adopt a simplistic “miracle or menace” stance invites critical thinking and may facilitate entertainment education and cultivation effects among its large audience. Future research should broaden comparative analyses across medical dramas and seasons, examine audience reception and learning outcomes empirically, and explore how specific ethical framings in popular media influence acceptance and expectations of medical AI in real clinical encounters.