The rapid evolution of artificial intelligence (AI) has transformed nearly every dimension of medical practice, from data management to diagnostics. Radiology, as a discipline deeply reliant on image interpretation and clinical reporting, has emerged as one of the most fertile domains for AI intervention. While traditional computer-aided detection (CAD) systems and deep learning–based classifiers have already shaped radiology workflows, the emergence of large language models (LLMs), particularly OpenAI’s ChatGPT, represents a qualitatively new phase. Unlike conventional image-focused algorithms, ChatGPT integrates natural language reasoning, multimodal analysis, and explanatory capacity, making it an attractive “second reader” in radiological practice.
Yet, the promise of ChatGPT is shadowed by profound ethical and social concerns. Its integration into radiological decision-making not only raises questions about accuracy and reliability but also challenges foundational principles of medical trust, professional accountability, and patient autonomy. As hospitals and research institutions begin to experiment with ChatGPT in radiology, the urgent task is to assess whether this technology can evolve into a trustworthy collaborator or whether it risks destabilizing the very ethos of medical care. This article critically engages with ChatGPT’s potential, ethical dilemmas, social risks, and governance pathways, offering an interdisciplinary analysis at the intersection of medicine, ethics, and computational linguistics.
One of the most compelling rationales for integrating ChatGPT into radiological workflows lies in its potential to assist diagnostic interpretation. Radiology departments are often overburdened with high imaging volumes, which contribute to fatigue, diagnostic delay, and error risks. ChatGPT, trained on large-scale medical corpora and fine-tuned with radiological guidelines, can automatically generate preliminary reports, highlight abnormal findings, and suggest possible differentials. This “first-pass” capability can reduce the repetitive burden on radiologists, enabling them to focus on complex and ambiguous cases.
Beyond clinical reporting, ChatGPT holds promise as a pedagogical instrument in radiology education. It can explain imaging signs, articulate reasoning chains, and simulate diagnostic discussions with students. For example, a trainee interpreting a chest X-ray can query ChatGPT not only for an assessment of pulmonary opacities but also for an explanation of pathophysiological underpinnings. Such capacity transforms the system into an interactive tutor, thereby complementing human mentorship in radiology residency programs.
Radiological findings often need translation into clinically actionable language for surgeons, oncologists, and primary care physicians. ChatGPT can serve as an intermediary, generating patient-friendly summaries and clinician-oriented reports simultaneously. Moreover, its ability to process both textual patient history and image-derived findings positions it as a tool for integrative decision-making in tumor boards or multidisciplinary conferences.
In resource-limited settings, where radiologists are scarce, ChatGPT-driven reporting may improve access to basic interpretative support. Rural clinics or hospitals in low- and middle-income countries (LMICs) could use such tools to preliminarily triage images before referral to tertiary centers. This raises the possibility of narrowing global health inequities, although it simultaneously introduces questions of dependency and uneven regulatory oversight.
While ChatGPT demonstrates fluency in natural language, its outputs remain probabilistic rather than evidence-grounded. In radiology, such “hallucinations” may manifest as fabricated findings, mischaracterization of lesions, or overconfident assertions. Unlike CAD algorithms constrained to pattern recognition, ChatGPT synthesizes plausible but potentially misleading narratives. The risk lies in clinicians placing undue reliance on outputs that lack verifiable grounding in image features.
Medical ethics is anchored in accountability. If ChatGPT provides a diagnostic suggestion that influences clinical decision-making, who bears responsibility in the case of misdiagnosis? The radiologist who relied on the AI? The hospital deploying the system? Or the developer who designed the model? Current legal frameworks are ill-prepared for such distributed responsibility. The absence of clarity risks both over-shielding corporations and overburdening physicians with liability, undermining professional morale and patient trust.
Radiological data is deeply personal, containing identifiable biometric markers. Integrating ChatGPT into hospital systems raises urgent questions about data governance: How are images anonymized? Who has access to prompts and outputs? Are conversations logged and reused for model improvement? Without robust safeguards, patients’ imaging data may be exposed to breaches, repurposing, or even commercial exploitation, violating ethical commitments to confidentiality.
Overreliance on ChatGPT could risk deskilling radiologists. As AI takes over the cognitive task of report generation, younger radiologists may lose opportunities to cultivate interpretive nuance. Ethical medicine values not only efficiency but also the cultivation of expertise. If ChatGPT becomes an unquestioned authority, the radiologist’s role may be reduced to mere verification, fundamentally altering professional identity and training trajectories.
Trust is a relational concept, grounded in human-to-human interaction. The patient’s confidence in diagnosis often rests not only on technical correctness but on the physician’s accountability and empathy. The introduction of ChatGPT as a “second reader” challenges this dynamic: Will patients accept diagnostic input from a non-human agent? Will the aura of machine objectivity undermine the perceived authority of physicians, or conversely, erode patient confidence in medical institutions that adopt such tools?
ChatGPT is not a neutral diagnostic assistant but a socio-technical artifact shaped by corporate, cultural, and epistemic biases. Its training data embeds inequalities in access to medical knowledge, potentially reproducing biases in diagnostic interpretations. Treating ChatGPT as a neutral “reader” risks obscuring these structural determinants.
As corporate actors increasingly control access to LLM technologies, ChatGPT’s deployment in radiology may be driven more by market incentives than public health priorities. Hospitals adopting AI may become locked into proprietary ecosystems, raising questions of dependency and fairness. Ethical evaluation thus requires examining not only clinical risks but also political economy dynamics.
ChatGPT operates as a “black box,” offering outputs without transparent justification. In radiology, where explanations are essential for both clinicians and patients, opacity undermines accountability. Ethical practice requires informed consent, but how can patients consent to diagnostic support mechanisms whose inner workings are opaque even to experts?
Effective governance must operate at multiple levels:
Technical standards: Establishing benchmarks for accuracy, interpretability, and safety through rigorous clinical validation.
Legal frameworks: Defining liability structures that balance physician responsibility with corporate accountability.
Ethical oversight: Ensuring transparency, informed consent, and respect for patient autonomy.
Hospitals should adopt internal governance structures, including AI evaluation committees, phased deployment protocols, and audit mechanisms. Continuous monitoring of error rates, bias detection, and user feedback must become integral to implementation.
Given the global nature of AI development, national regulatory silos are insufficient. International organizations, such as the World Health Organization (WHO) and International Radiology Societies, should lead in establishing cross-border standards. Collaborative governance frameworks could mitigate disparities in adoption while preventing regulatory arbitrage.
Regulation must emphasize that ChatGPT is an assistive tool rather than a replacement for radiologists. Policies should safeguard the radiologist’s role as the final arbiter, ensuring that AI complements rather than supplants human judgment. Training programs should emphasize critical AI literacy, equipping radiologists to use ChatGPT responsibly and skeptically.
ChatGPT’s integration into radiological image interpretation embodies both the promise and peril of next-generation AI in medicine. As a potential “second reader,” it offers efficiency gains, educational support, and enhanced communication. Yet its ethical challenges are profound: risks of inaccuracy, opaque accountability, privacy breaches, professional deskilling, and erosion of medical trust. From a social critique perspective, ChatGPT is not merely a diagnostic tool but a socio-technical system shaped by commercial interests, biases, and governance deficits.
The path forward lies not in wholesale rejection or blind adoption but in carefully designed regulatory frameworks. By embedding transparency, accountability, and ethical safeguards into every stage of deployment, ChatGPT can evolve into a trustworthy collaborator rather than a destabilizing force. For radiology and medicine at large, the key is to uphold human values while harnessing computational innovation. Only then can the “second reader” genuinely serve the goals of patient care, professional integrity, and social justice.
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