AI‐Assisted Early Clinical Diagnosis of External Auditory Canal Squamous Cell Carcinoma: A Rare Case in a Young Male From Pakistan

ABSTRACT

A 36‐year‐old with refractory otorrhea, otalgia, and facial palsy had HRCT bony erosion. Microsoft Copilot flagged malignancy; biopsy confirmed T1N0M0 squamous cell carcinoma. Curative resection achieved clear margins. Cranial nerve involvement demands biopsy regardless of age; AI aids but cannot replace clinical judgment.

Key Clinical Message.

Persistent, treatment‐resistant otalgia accompanied by facial nerve weakness demands urgent investigation for temporal bone malignancy, regardless of patient age. Integrating AI decision‐support tools into clinical workflows provides an objective safety net against cognitive bias, facilitating prompt biopsy, complete surgical resection with negative margins, and the avoidance of adjuvant radiotherapy.

Abbreviations

AI

Artificial Intelligence

CRT

Chemoradiotherapy

CSOM

Chronic Suppurative Otitis Media

EAC

External Auditory Canal

HRCT

High‐Resolution CT

RT

Radiotherapy

SCC

Squamous Cell

1. Introduction

Squamous cell carcinoma (SCC) of the external auditory canal (EAC) is an exceptionally rare and aggressive primary temporal bone malignancy, accounting for approximately 0.2% of all head and neck cancers [1]. The disease demonstrates an annual incidence of 1 to 6 cases per million, predominantly affecting elderly populations [2]. Due to its locally destructive behavior and a marked propensity for soft‐tissue, vascular, and perineural invasion, EAC SCC presents substantial diagnostic and therapeutic challenges [3, 4]. Occurrence in young adults is remarkably rare and clinically unexpected. Consequently, early diagnosis is frequently missed, as initial signs and symptoms heavily overlap with benign otologic conditions such as chronic suppurative otitis media (CSOM) [3, 5]. Diagnostic delays often allow the tumor to advance, leading to poor prognostic outcomes at presentation [3, 6]. Furthermore, managing malignancies within this complex anatomical region requires balancing local disease control with the preservation of functional integrity and aesthetic appearance [7, 8]. Clinical features such as regional lymphadenopathy and facial nerve dysfunction signify advanced disease and correlate with poorer clinical outcomes [7, 9, 10]. Achieving favorable outcomes depends heavily on prompt recognition and early, aggressive intervention. Recently, artificial intelligence (AI) decision‐support tools have emerged as valuable diagnostic adjuncts [11, 12, 13, 14]. By integrating patient history, subtle radiologic findings, and current literature trends, large language model‐enabled platforms such as Microsoft Copilot can synthesize complex data to provide actionable, high‐risk insights [15]. This case report demonstrates how AI‐driven decision support can complement traditional clinical pathways, expediting tissue biopsy and prompt intervention through early signals.

2. Case History and Examination

A 36‐year‐old male presented with a 3‐month history of persistent right‐sided purulent otorrhea, severe deep‐seated otalgia, and acute ipsilateral facial nerve palsy (Figures 1A,B and 2A). Prior to presentation, he had received multiple courses of oral and topical antimicrobial therapy for presumed chronic suppurative otitis media (CSOM) without clinical improvement. Otoscopic examination revealed a friable, soft‐tissue mass originating from the posterior wall of the external auditory canal (EAC).

FIGURE 1.

Clinical Appearance of External Auditory Canal Lesion. (A) Ulcerative lesion involving the EAC and adjacent preauricular area; (B) Friable mucosa lining the canal (yellow arrow indicates lesion).

FIGURE 2.

Taken from informed consent. (A) Right‐sided Facial nerve paralysis; (B) Post‐operative resection showing clean surgical margins with smooth epithelial contours showing successful resection.

3. Methods (Differential Diagnosis, Investigations and Management)

High‐Resolution Computed Tomography (HRCT) of the temporal bone (Figure 3) demonstrated a soft‐tissue density completely obstructing the right EAC, accompanied by ipsilateral mastoid air cell opacification and focal cortical erosion of the posterior canal wall. The ossicular chain and scutum remained intact, with no evidence of middle ear or intracranial extension. In a young adult, such imaging findings are frequently misattributed to benign pathologies such as localized cholesteatoma or advanced inflammatory erosion. The initial working diagnosis was chronic suppurative otitis media (CSOM), based on the patient's persistent ear discharge and clinical presentation. To re‐evaluate the treatment‐resistant presentation, the clinical care team integrated a large language model‐based decision‐support tool (Microsoft Copilot) Supporting Information into the diagnostic workflow. By correlating the 3‐month clinical trajectory, specifically the constellation of refractory otorrhea, severe pain, and rapid‐onset cranial neuropathy, with the focal bony erosion on HRCT, the AI model flagged a high index of suspicion for temporal bone malignancy. This algorithmic alert prompted an immediate punch biopsy of the EAC mass. Histopathological examination (Figure 4) confirmed a well‐differentiated keratinizing squamous cell carcinoma (SCC), characterized by distinct keratin pearls, intercellular bridges, and invasive stromal proliferation. Based on the modified Pittsburgh staging system, the tumor was classified as Stage I (T1N0M0), limited strictly to the EAC without soft‐tissue, nodal, or distant metastases. The primary differential diagnoses included refractory CSOM, malignant otitis externa, EAC cholesteatoma, and tuberculous otitis. The clinical diagnostic challenge was compounded by several factors: The patient's young age, the absence of classic oncologic risk factors, and symptoms that closely mimicked benign infectious disease. These factors initially created a false sense of security, delaying early tissue sampling. The patient underwent a definitive right lateral temporal bone resection with preservation of the facial nerve (Figure 2B). Histopathological assessment of the surgical specimen confirmed complete tumor extirpation with negative soft‐tissue and bony margins. Given the early‐stage classification (T1N0M0) and histologically clear margins, adjuvant radiotherapy was deferred following multidisciplinary tumor board review.

FIGURE 3.

CT imaging and AI‐Enhanced Visualization of a Temporal Bone Lesion. (A) Axial CT (bone window) shows soft tissue replacing the EAC, with bony erosion and mastoid opacification. (B) Coronal CT (soft tissue window) reveals superior extension and mass effect on adjacent brain. (C, D) AI‐generated reconstructions consistent with a locally aggressive neoplasm, later confirmed on histopathology.

FIGURE 4.

Histopathology and Schematic Features of SCC. (A–C) Original hematoxylin and eosin‐stained sections (x200 magnification, 300 dpi) showing (a) Invasive nests of atypical squamous cells, (b) Keratin pearls, (c) Desmoplastic stroma. (D, E) AI‐generated schematic illustrations highlighting key histologic features of squamous cell carcinoma.

4. Results and Conclusions (Outcome and Follow‐up)

The postoperative course was uneventful. At the follow‐up assessment, the patient remains disease‐free under strict clinical and radiological surveillance, with salvage radiotherapy reserved for potential disease recurrence.

5. Discussion

External auditory canal squamous cell carcinoma (EAC SCC) remains an exceptionally rare clinical entity in individuals under 40 years of age, accounting for diagnostic delays due to its propensity to masquerade as benign inflammatory otologic disease. A targeted literature review spanning the past two decades revealed only a small number of documented cases in this young demographic worldwide, with the vast majority presenting at advanced stages associated with dismal prognostic outcomes (Table 1).

TABLE 1.

Reported Cases of External Auditory Canal Squamous Cell Carcinoma in Patients < 40 Years (2005‐2025).

Year	Country	Age/Sex	Presentation	Stage (Pittsburgh)	Management	Outcome/Follow‐up
2006	Japan	38/M	Otalgia, otorrhea	T2	Lateral temporal bone resection + RT	NED at 18 months
2009	USA	25/F	Hearing loss, canal mass	T3	Surgery + CRT	Recurrence at 12 months
2011	India	22/M	Otorrhea, facial palsy	T4	Surgery + RT	DOD at 8 months
2013	Italy	29/M	Otalgia, bleeding	T2	Surgery only	NED at 24 months
2015	China	27/F	Otorrhea, swelling	T3	Surgery + CRT	Alive with disease at 14 months
2017	UK	21/M	Otalgia, hearing loss	T1	Surgery only	NED at 36 months
2019	Brazil	26/F	Otorrhea, mastoid pain	T3	Surgery + RT	NED at 20 months
2021	Turkey	23/M	Otalgia, facial palsy	T4	Surgery + CRT	DOD at 10 months
2023	Japan	39/M	Otorrhea, canal mass	T2	Surgery only	NED at 12 months
2025	Pakistan (present case)	36/M	Otalgia, otorrhea, facial palsy	T1	Surgery only	On follow‐up

Abbreviations: NED, No evidence of disease; DOD, Died of disease; RT, Radiotherapy; CRT, Chemotherapy.

This case illustrates the transformative potential of artificial intelligence (AI) decision‐support systems, such as Microsoft Copilot, in restructuring diagnostic pathways for atypical oncologic presentations [9, 10]. In traditional clinical settings, a young patient presenting with refractory otorrhea, acute facial nerve palsy, and subtle high‐resolution computed tomography (HRCT) temporal bone findings is frequently managed within benign diagnostic algorithms. This tendency perpetuates a well‐documented age‐anchored diagnostic bias. In this instance, that cognitive anchor was directly challenged by Copilot's multimodal pattern recognition, which synthesized the patient's demographic profile, clinical chronology, and nuanced radiologic features to generate an early malignancy alert [16, 17, 18, 19, 20].

By synthesizing current literature trends and cross‐referencing atypical clinical parameters, the AI tool accelerated the suspicion‐to‐biopsy interval from weeks to mere hours [21, 22]. Converting minute imaging clues into an actionable, auditable risk alert effectively mitigated clinician bias and facilitated an urgent tissue diagnosis [23, 24, 25]. Prompt identification at the T1N0M0 stage allowed for immediate curative surgical intervention at a disease phase when overall survival rates are highest.

From a surgical perspective, complete en bloc resection with histologically clear margins remains the gold standard for EAC SCC. While the absence of perineural invasion in this patient serves as a highly favorable prognostic indicator, rigorous clinical and radiological surveillance remains imperative given the historically high risk of local recurrence, even in early‐stage disease.

Beyond its relevance to the present case, this AI‐assisted tool may have particular value as a preliminary decision‐support and triage aid in rural and resource‐constrained centers where immediate assessment by an attending otolaryngologist is unavailable. Under appropriate clinical supervision, it could help non‐specialist clinicians organize clinical information, broaden the differential diagnosis, recognize warning features, and prioritize patients requiring urgent referral or tele‐otolaryngology consultation. Myburgh et al. developed an automated tympanic‐membrane image‐analysis system specifically for medically underserved populations and reported diagnostic accuracies of 80.6% using commercial video‐otoscope images and 78.7% using images acquired with a low‐cost device [21]. More recently, Dubois et al. reported that a smartphone‐based deep‐learning system achieved 99.0% sensitivity and 95.2% specificity for distinguishing normal from abnormal otoscopic images in a held‐out test set, although its performance varied across individual middle‐ear conditions; accordingly, the authors proposed its principal role as triage and targeted‐referral support in remote settings [22]. Importantly, a randomized trial involving physicians in Pakistan demonstrated that, following structured AI‐literacy training, access to GPT‐4o increased the mean diagnostic‐reasoning score from 42.6% with conventional resources alone to 71.4% [23]. These findings suggest that properly trained clinician–AI collaboration may help reduce diagnostic gaps in settings where specialist services are limited. Evidence from other diseases further supports the potential of AI to improve diagnostic recognition. In a proof‐of‐concept report of two histopathologically confirmed ocular caruncle oncocytomas, a multimodal AI model identified oncocytoma as its leading diagnostic hypothesis using clinical images and minimal patient information, while appropriately recommending histopathological confirmation [24]. Similarly, in a patient with cancer of unknown primary origin, conventional investigations failed to establish the primary tumor, whereas AI‐powered molecular profiling suggested renal cell carcinoma with an 82% probability; this directed a targeted biopsy that confirmed the diagnosis and enabled disease‐specific treatment [25]. Furthermore, the randomized MASAI breast‐screening trial reported higher sensitivity with AI‐supported mammography than with standard double reading (80.5% versus 73.8%), with findings consistent with improved detection of clinically important cancers during screening [26]. Collectively, these reports support AI as a potentially valuable diagnostic safety net that may facilitate earlier recognition and referral. Nevertheless, AI‐generated findings should not be considered definitive or used as a substitute for clinical examination, specialist evaluation, imaging, endoscopy, or histopathological confirmation. Local prospective validation, clinician training, patient‐data safeguards, and continuing specialist oversight remain essential before implementation in routine clinical practice.

The significance of this case is not that Copilot outperformed the ENT surgeons, but that it provided a supplementary diagnostic safety check that reinforced the need to investigate red‐flag features. Because neoplastic disease was explicitly mentioned in the prompt, the response should be interpreted as confirmatory decision support rather than an independently generated diagnosis. Copilot is a general‐purpose generative AI tool and not a validated ENT diagnostic system. Its responses may vary with prompt wording and may contain inaccurate or fabricated information. It cannot perform physical examination, endoscopy, or independent clinical verification. Furthermore, a single case cannot establish sensitivity, specificity, reproducibility, diagnostic accuracy, or clinical effectiveness. Copilot should therefore be used only as a clinician‐supervised, hypothesis‐generating aid, with all outputs verified through standard diagnostic methods.

In resource‐constrained settings where immediate otolaryngology consultation is unavailable, this AI‐assisted approach may support preliminary assessment, identification of concerning features, and timely specialist referral; however, it should complement rather than replace clinical judgment and definitive specialist evaluation.

6. Conclusion

This case demonstrates the critical role of AI‐driven clinical decision‐support systems in overcoming cognitive bias and breaking diagnostic inertia in rare oncologic presentations. In young patients, persistent otologic symptoms and subtle HRCT changes are frequently misattributed to benign inflammatory processes. While AI cannot replace clinical judgment, its integration as a bias‐disrupting adjunct is invaluable when treating atypical demographics where baseline suspicion of malignancy is low. Ultimately, early AI‐assisted pattern recognition facilitated prompt surgical intervention at a curable stage, underscoring the potential of decision‐support tools to prevent fatal diagnostic delays in rare temporal bone malignancies.

7. Software Use and Institutional Permission

The clinical‐decision support platform was accessed via a secure institutional workstation to assist in diagnostic evaluation. No patient‐identifiable information was stored or transmitted externally, and all outputs were reviewed by the attending otologist, who maintained full responsibility for clinical decisions. Formal ethical approval was not required for this single case report, as per institutional policy.

Author Contributions

Mah Gul Summan: conceptualization, writing – original draft. Awais Altaf: validation, visualization. Ali Gohar: conceptualization, validation, writing – original draft. Muhammad Arsalan: writing – review and editing, conceptualization, writing – original draft. Moeez Ahmad Saeed: writing – original draft, writing – review and editing. Muhammad Umais: writing – review and editing, writing – original draft. Muhammad Hamza: conceptualization, writing – original draft, writing – review and editing. Muhammad Husnain Ahmad: writing – review and editing, writing – original draft.

Funding

The authors have nothing to report.

Consent

Written informed consent was obtained from the patient for publication of the de‐identified clinical information and accompanying images, including description of the AI‐assisted assessment. Under the institution's policy, ethics committee approval was not required for this single‐patient case report. Before submission to Copilot, all direct identifiers and patient‐identifying labels were removed from the clinical information and HRCT images. Copilot was accessed through a free personal account rather than an institutional or paid account. The authors acknowledge that the submitted information and interaction may have been processed or retained according to Microsoft's applicable consumer‐account policies; therefore, no claim is made that the interaction was unrecorded.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Data S1: Supporting Information.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.