Evaluating Artificial Intelligence's Role in Teaching the Reporting and Interpretation of Computed Tomographic Angiography for Preoperative Planning of the Deep Inferior Epigastric Artery Perforator Flap

Background

Artificial intelligence (AI) has the potential to transform preoperative planning for breast reconstruction by enhancing the efficiency, accuracy, and reliability of radiology reporting through automatic interpretation and perforator identification. Large language models (LLMs) have recently advanced significantly in medicine. This study aimed to evaluate the proficiency of contemporary LLMs in interpreting computed tomography angiography (CTA) scans for deep inferior epigastric perforator (DIEP) flap preoperative planning.

Methods

Four prominent LLMs, ChatGPT-4, BARD, Perplexity, and BingAI, answered six questions on CTA scan reporting. A panel of expert plastic surgeons with extensive experience in breast reconstruction assessed the responses using a Likert scale. In contrast, the responses' readability was evaluated using the Flesch Reading Ease score, the Flesch-Kincaid Grade level, and the Coleman-Liau Index. The DISCERN score was utilized to determine the responses' suitability. Statistical significance was identified through a t-test, and P-values < 0.05 were considered significant.

Results

BingAI provided the most accurate and useful responses to prompts, followed by Perplexity, ChatGPT, and then BARD. BingAI had the greatest Flesh Reading Ease (34.7±5.5) and DISCERN (60.5±3.9) scores. Perplexity had higher Flesch-Kincaid Grade level (20.5±2.7) and Coleman-Liau Index (17.8±1.6) scores than other LLMs.

Conclusion

LLMs exhibit limitations in their capabilities of reporting CTA for preoperative planning of breast reconstruction, yet the rapid advancements in technology hint at a promising future. AI stands poised to enhance the education of CTA reporting and aid preoperative planning. In the future, AI technology could provide automatic CTA interpretation, enhancing the efficiency, accuracy, and reliability of CTA reports.

Comparison of large language models in management advice for melanoma: Google's AI BARD, BingAI and ChatGPT

Large language models (LLMs) are emerging artificial intelligence (AI) technology refining research and healthcare. Their use in medicine has seen numerous recent applications. One area where LLMs have shown particular promise is in the provision of medical information and guidance to practitioners. This study aims to assess three prominent LLMs-Google's AI BARD, BingAI and ChatGPT-4 in providing management advice for melanoma by comparing their responses to current clinical guidelines and existing literature. Five questions on melanoma pathology were prompted to three LLMs. A panel of three experienced Board-certified plastic surgeons evaluated the responses for reliability using reliability matrix (Flesch Reading Ease Score, the Flesch-Kincaid Grade Level and the Coleman-Liau Index), suitability (modified DISCERN score) and comparing them to existing guidelines. t-Test was performed to calculate differences in mean readability and reliability scores between LLMs and p value <0.05 was considered statistically significant. The mean readability scores across three LLMs were same. ChatGPT exhibited superiority with a Flesch Reading Ease Score of 35.42 (±21.02), Flesch-Kincaid Grade Level of 11.98 (±4.49) and Coleman-Liau Index of 12.00 (±5.10), however all of these were insignificant (p > 0.05). Suitability-wise using DISCERN score, ChatGPT 58 (±6.44) significantly (p = 0.04) outperformed BARD 36.2 (±34.06) and was insignificant to BingAI's 49.8 (±22.28). This study demonstrates that ChatGPT marginally outperforms BARD and BingAI in providing reliable, evidence-based clinical advice, but they still face limitations in depth and specificity. Future research should improve LLM performance by integrating specialized databases and expert knowledge to support patient-centred care.

Using Generative Artificial Intelligence Tools in Cosmetic Surgery: A Study on Rhinoplasty, Facelifts, and Blepharoplasty Procedures

Artificial intelligence (AI), notably Generative Adversarial Networks, has the potential to transform medical and patient education. Leveraging GANs in medical fields, especially cosmetic surgery, provides a plethora of benefits, including upholding patient confidentiality, ensuring broad exposure to diverse patient scenarios, and democratizing medical education. This study investigated the capacity of AI models, DALL-E 2, Midjourney, and Blue Willow, to generate realistic images pertinent to cosmetic surgery. We combined the generative powers of ChatGPT-4 and Google's BARD with these GANs to produce images of various noses, faces, and eyelids. Four board-certified plastic surgeons evaluated the generated images, eliminating the need for real patient photographs. Notably, generated images predominantly showcased female faces with lighter skin tones, lacking representation of males, older women, and those with a body mass index above 20. The integration of AI in cosmetic surgery offers enhanced patient education and training but demands careful and ethical incorporation to ensure comprehensive representation and uphold medical standards.

Refinements and restoring contour in head and neck reconstruction

BACKGROUND

To date head and neck reconstructions of oncological defects have concentrated on primarily filling the defect to achieve primary wound healing, secondly restore function and lastly cosmesis. This paper describes a refinement of existing free tissue transfer techniques for improvement of contour, function and aesthetics.

METHODS

A retrospective review of 38 patients operated on by one surgeon at St Vincent's, Royal Melbourne and Western Hospitals over a 3-year period was conducted. Data were collected on patient demographics, tumour details, nature of the defect, type of reconstructive procedure, nature of additional tissue used, radiotherapy, complications and outcome.

RESULTS

We present refinements in using de-epithelialized skin paddles, flexor hallucis longus, and rectus and vastus lateralis muscle in order to achieve optimal reconstruction. Free tissue transfer refinements are discussed in anterolateral thigh, fibula, rectus and anteromedial thigh free flaps. The average defect size and volume of neck dissection prior to reconstruction is presented. A variation of radical, modified radical and selective neck dissections were required for oncological staging and clearance. Rare and minor associated complications are discussed. Post-operative radiotherapy treatment was used in the majority of patients with preoperative adjuvant therapy required in some salvage cases. All patients achieved primary wound healing post-operatively with no salivary leaks, flap failures or exposure of neck vessels.

CONCLUSIONS

Supplementary microsurgical tissue transfer of de-epithelialized skin, vastus lateralis, flexor hallucis longus and rectus muscles is a valuable option for restoring contour, aesthetics and vessel protection post-radiotherapy.

Early soft tissue coverage and negative pressure wound therapy optimises patient outcomes in lower limb trauma

BACKGROUND

The timing of soft tissue reconstruction for severe open lower limb trauma is critical to its successful outcome, particularly in the setting of exposed metalware and pre-existing wound infection. The use of negative pressure wound therapy (NPWT) may allow a delay in soft tissue coverage without adverse effects. This study evaluated the impact of delayed free-flap reconstruction, prolonged metalware exposure, pre-flap wound infection, and the efficacy of NPWT on the success of soft tissue coverage after open lower limb injury.

METHODS

Retrospective review of all free-flap reconstructions for lower limb trauma undertaken at a tertiary trauma centre between June 2002 and July 2009.

RESULTS

103 patients underwent 105 free-flap reconstructions. Compared with patients who were reconstructed within 3 days of injury, the cohort with delayed reconstruction beyond 7 days had significantly increased rates of pre-flap wound infection, flap re-operation, deep metal infection and osteomyelitis. Pre-flap wound infection independently predicted adverse surgical outcomes. In the setting of exposed metalware, free-flap transfer beyond one day significantly increased the flap failure rate. These patients required more surgical procedures and a longer hospital stay. The use of NPWT significantly lowered the rate of flap re-operations and venous thrombosis, but did not allow a delay in reconstruction beyond 7 days from injury without a concomitant rise in skeletal and flap complications.

CONCLUSIONS

Following open lower limb trauma, soft tissue coverage within 3 days of injury and immediately following fracture fixation with exposed metalware minimises pre-flap wound infection and optimises surgical outcomes. NPWT provides effective temporary wound coverage, but does not allow a delay in definitive free-flap reconstruction.