Patient satisfaction and clinical outcomes of digital dentures fabricated in a predoctoral dental clinic: An up to 2-year retrospective analysis.

The integration of artificial intelligence (AI) into clinical medicine has accelerated in recent years, with significant potential in visually intensive specialties such as dermatology. AI‐based tools employing computer vision and deep learning have shown the ability to classify skin lesions with accuracy approaching that of trained dermatologists []. The Australasian College of Dermatologists (ACD) has acknowledged this potential, while highlighting the need for ethical, evidence‐based, and clinically robust implementation []. To date, most dermatology‐focused AI research has centred on image analysis for skin cancer detection [], yet the translation of these tools into routine clinical practice remains limited. Unfortunately, the integration of AI into routine clinical practice faces substantial challenges. The ‘last mile problem’ emphasises that the success of any technology depends not only on its technical performance but also on its effective implementation within complex clinical systems and workflows [, ]. Adoption of medical innovations is multifactorial and shaped by clinician attitudes, perceptions, and trust []. A growing body of evidence has begun to explore the attitudes towards AI among various medical specialties, including in radiology, medical education, and emergency medicine, suggesting that while clinicians recognise the potential of AI, there is also apprehension regarding reliability, safety, and its impact on professional roles [, , ]. A 2021 survey of Australian and New Zealand dermatologists, alongside other specialties, revealed broadly positive views towards AI but highlighted concerns around medicolegal responsibility and the influence of technology companies []. More recently, a survey of 105 consultant dermatologists and 17 dermatology trainees conducted between April 2024 and May 2024 found that, although many had trialled AI tools, regular use was uncommon, with a lack of trust in diagnostic accuracy identified as a major barrier to use []. Survey participants anticipated that AI would play a growing role in dermatology, citing potential benefits such as improved patient access and reduced administrative burden []. Despite these valuable insights, little is known about the perspectives of dermatology trainees. This group warrants particular attention, as their formative experiences and attitudes are likely to influence long‐term patterns of AI adoption and integration within the specialty.