Daniel G. E. Thiem

Purpose: Medical documentation is essential for clinical communication but is often intended for professional audiences, limiting patient understanding. This linguistic complexity can reduce health literacy and hinder shared decision-making. Large language models offer new opportunities to simplify medical texts while maintaining factual accuracy, thereby improving accessibility and comprehension. This study aimed to evaluate whether ChatGPT can simplify medical reports while preserving clinical content and to assess whether these simplifications improve patient comprehension, readability, and perceived communication quality. Methods: Five document types were analysed, including MRI, CT, surgical, pathology reports, and discharge summaries. Each original physician-written document was simplified using a standardized ChatGPT prompt instructing full content preservation and patient-oriented phrasing. Ten simplifications per text were reviewed for completeness. Readability was measured using Flesch Reading Ease (FRE) and LIX indices. A total of 576 participants without medical knowledge evaluated either a simplified or original report version and completed a standardized questionnaire assessing clarity, structure, and applicability, followed by comprehension testing. Results: Simplified reports achieved significantly higher readability (FRE 48.4 ± 5.0 vs. 22.9 ± 5.2; p < 0.0001; LIX 48.3 ± 3.2 vs. 59.2 ± 2.5; p = 0.004). Across all document types, patients rated simplified texts significantly higher in clarity, structure, and usefulness ( p < 0.001), with significantly improved comprehension accuracy (e.g., MRI 80.3% vs. 53.7%; p < 0.001). No loss of medical information was observed. Conclusions: ChatGPT appears to be capable of simplifying complex medical documents while preserving clinically relevant information, leading to improvements in both perceived readability and objective understanding under the conditions of this study. AI-driven text simplification thus represents a promising tool to enhance patient communication and health literacy.

Background: Multidisciplinary tumor boards (MDTs) constitute the foundation of modern tumor therapy. Large language models (LLMs) are widely discussed for optimizing their recommendations. Objective: This is the first prospective feasibility study evaluating the implementation of locally run LLMs on real-world cases within a regular head and neck MDT. Methods: Seventeen patients participated in the study. The MDT cases were processed by 2 different local LLMs (gemma-3-12b and gpt-oss-20b) to obtain treatment recommendations. The MDT conferred as usual. After the decision was made, the MDT was presented with the LLMs’ recommendations. If deemed to be beneficial, the MDT’s recommendation was adjusted. The MDT members rated the LLMs’ responses inter alia, for medical adequacy on a 6-point Likert scale. In addition, a tabular comparison of the MDT’s and LLMs’ recommendations was carried out. Results: In one case, 6% (1/17, 95% CI 0%‐29%), the LLM was able to substantially improve the MDT recommendation by underscoring a follow-up examination that had not yet been performed. Concordance regarding the curative or palliative therapy regimen reached 94% (16/17, 95% CI 71%‐100%); for gemma-3-12b and 59% (10/17, 95% CI 33%‐82%) for gpt-oss-20b. Gemma-3-12b stated the same first-line therapy regimen as the MDT as first-line in 35% (6/17, 95% CI 14%‐62%) of cases, and gpt-oss-20b in 41% (7/17, 95% CI 18%‐67%) of cases. In 59% (10/17, 95% CI 33%‐82%) of patients, gemma-3-12b stated the MDT’s first-line therapy regimen, albeit with a different priority, while for gpt-oss-20b, it was 41% (7/17, 95% CI 18%‐67%) of patients. Medical adequacy, as rated by the MDT members, revealed a median of 5 (IQR 2‐5) for gemma-3-12b and 4 (IQR 3‐5) for gpt-oss-20b. MDT members stated potentially hazardous information in 27% (25/93, 95% CI 18%‐37%) of ratings for gemma-3-12b and 17% (14/83, 95% CI 9%‐26%) of ratings for gpt-oss-20b. Conclusions: Locally run LLMs improved the MDT recommendation in 1 case and were primarily useful for identifying potentially relevant missing information in other cases, underscoring that they cannot replace MDTs. However, their observed benefit suggests that more advanced local models may offer safe, rapid, and cost-effective support for MDT decision-making. The study should be seen as an exploratory setting focusing on practical insights rather than benchmarking its clinical impact. Accordingly, the data demonstrate that the integration of LLMs in today’s MDT workflow is feasible and may benefit the quality of decision-making in specific cases.

Microvascular free flap transfer is an essential procedure in head and neck reconstructive surgery, with total flap loss representing the primary complication. Sepsis has been identified as a critical risk factor contributing to flap failure. Here we aimed to evaluate the impact of septic shock on free flap vitality using a large animal model and further investigated the flap-specific effects of different fluid resuscitation strategies on flap viability and metabolic response. A prospective, randomized trial was conducted on 31 juvenile male pigs to assess the effects of 3 fluid resuscitation strategies following septic shock induced by lipopolysaccharide infusion. The strategies included a balanced crystalloid solution, a colloid, and a combination of crystalloid and resveratrol. Free flap vitality was continuously monitored using hyperspectral imaging and blood gas analysis, comparing intraflap venous blood with central venous blood. The crystalloid group most effectively maintained higher oxygen saturation and lower lactate levels. Conversely, the colloid group exhibited lower tissue oxygen saturation and higher lactate levels. The combination of crystalloid and resveratrol stabilized the hemoglobin and tissue water indices. The study demonstrated a significant influence of different fluid resuscitation strategies on metabolic and perfusion outcomes of free flaps under septic conditions. Administration of crystalloid suggests optimized oxygen delivery and utilization, whereas the combination with resveratrol provided an improved balance of blood volume and hydration, hence preventing fluid overload.

Background: Diseases of the oral cavity, including oral squamous cell carcinoma, pose major challenges to health care worldwide due to their late diagnosis and complicated differentiation of oral tissues. The combination of endoscopic hyperspectral imaging (HSI) and deep learning (DL) models offers a promising approach to the demand for modern, noninvasive tissue diagnostics. This study presents a large-scale in vivo dataset designed to support DL-based segmentation and classification of healthy oral tissues. Objective: This study aimed to develop a comprehensive, annotated endoscopic HSI dataset of the oral cavity and to demonstrate automated, reliable differentiation of intraoral tissue structures by integrating endoscopic HSI with advanced machine learning methods. Methods: A total of 226 participants (166 women [73.5%], 60 men [26.5%], aged 24‐87 years) were examined using an endoscopic HSI system, capturing spectral data in the range of 500 to 1000 nm. Oral structures in red, green, and blue and HSI scans were annotated using RectLabel Pro (by Ryo Kawamura). DeepLabv3 (Google Research) with a ResNet-50 backbone was adapted for endoscopic HSI segmentation. The model was trained for 50 epochs on 70% of the dataset, with 30% for evaluation. Performance metrics (precision, recall, and F₁-score) confirmed its efficacy in distinguishing oral tissue types. Results: DeepLabv3 (ResNet-101) and U-Net (EfficientNet-B0/ResNet-50) achieved the highest overall F₁-scores of 0.857 and 0.84, respectively, particularly excelling in segmenting the mucosa (0.915), retractor (0.94), tooth (0.90), and palate (0.90). Variability analysis confirmed high spectral diversity across tissue classes, supporting the dataset’s complexity and authenticity for realistic clinical conditions. Conclusions: The presented dataset addresses a key gap in oral health imaging by developing and validating robust DL algorithms for endoscopic HSI data. It enables accurate classification of oral tissue and paves the way for future applications in individualized noninvasive pathological tissue analysis, early cancer detection, and intraoperative diagnostics of oral diseases.

Background: Medical reports, particularly radiology findings, are often written for professional communication, making them difficult for patients to understand. This communication barrier can reduce patient engagement and lead to misinterpretation. Artificial intelligence (AI), especially large language models such as ChatGPT, offers new opportunities for simplifying medical documentation to improve patient comprehension. Objective: We aimed to evaluate whether AI-generated radiology reports simplified by ChatGPT improve patient understanding, readability, and communication quality compared to original AI-generated reports. Methods: In total, 3 versions of radiology reports were created using ChatGPT: an original AI-generated version (text 1), a patient-friendly, simplified version (text 2), and a further simplified and accessibility-optimized version (text 3). A total of 300 patients (n=100, 33.3% per group), excluding patients with medical education, were randomly assigned to review one text version and complete a standardized questionnaire. Readability was assessed using the Flesch Reading Ease (FRE) score and LIX indices. Results: Both simplified texts showed significantly higher readability scores (text 1: FRE score=51.1; text 2: FRE score=55.0; and text 3: FRE score=56.4; P<.001) and lower LIX scores, indicating enhanced clarity. Text 3 had the shortest sentences, had the fewest long words, and scored best on all patient-rated dimensions. Questionnaire results revealed significantly higher ratings for texts 2 and 3 across clarity (P<.001), tone (P<.001), structure, and patient engagement. For example, patients rated the ability to understand findings without help highest for text 3 (mean 1.5, SD 0.7) and lowest for text 1 (mean 3.1, SD 1.4). Both simplified texts significantly improved patients’ ability to prepare for clinical conversations and promoted shared decision-making. Conclusions: AI-generated simplification of radiology reports significantly enhances patient comprehension and engagement. These findings highlight the potential of ChatGPT as a tool to improve patient-centered communication. While promising, future research should focus on ensuring clinical accuracy and exploring applications across diverse patient populations to support equitable and effective integration of AI in health care communication.

BACKGROUND Diseases of the oral cavity, including oral squamous cell carcinoma (OSCC) pose major challenges to healthcare worldwide due to its late diagnosis and complicated differentiation of oral tissues. Endoscopic hyperspectral imaging (eHSI) represents a promising approach to the demand for modern, non-invasive tissue diagnostics. This dataset is designed to enhance the performance of deep learning models by providing comprehensive spectral data essential for distinguishing between healthy and pathological oral tissue conditions. OBJECTIVE To develop and validate a clinical dataset of endoscopic hyperspectral imaging (eHSI) of the oral cavity and to evaluate the performance of deep learning-based semantic segmentation models for automated tissue classification. METHODS This clinical study included 226 participants (166 women, 60 men, aged 24-87). eHSI data were collected using an endoscopic hyperspectral sensor system, capturing spectral data in the range of 500-1000 nm. Each participant underwent five standardized intraoral hyperspectral scans of the cheek, palate, tongue, and teeth. RGB and eHSI images were archived in NPY format for Python analysis. Oral structures were annotated using RectLabel Pro©. DeepLabv3 with a ResNet-50 backbone was adapted for eHSI segmentation by modifying the first convolutional layer. The model was trained for 50 epochs on 70% of the dataset, with 30% for evaluation. Performance metrics (Precision, Recall, F1-score) confirmed its efficacy in distinguishing oral tissue types. RESULTS Preliminary analysis revealed that the Coefficient of Variation exceeded 15% for most spectral bands, indicating high variability in spectral signatures. DeepLabv3 (ResNet-101) achieved strong segmentation performance (F1-score: 0.856, Precision: 0.8506, Recall: 0.8634), excelling in key structures—mucosa (F1: 0.915), retractor (F1: 0.940), tooth (F1: 0.902), and palate (F1: 0.900). Moderate results for gingiva (F1: 0.760) and lip (F1: 0.706) suggest potential for further refinement. To assess dataset robustness, additional models were tested, including DeepLabv3 (ResNet-50), FCN (ResNet-50/101), PSPNet (ResNet-50/VGG16), and U-Net (EfficientNet-B0/ResNet-50). DeepLabv3 (ResNet-101) and U-Net (EfficientNet-B0) emerged as top performers, particularly in retractor, mucosa, and tooth segmentation. CONCLUSIONS Conducting an in-depth hyperspectral analysis of oral tissue has facilitated the development of robust deep learning algorithms, thereby enhancing diagnostic accuracy and clinical applicability. This study integrates advanced imaging technologies with deep learning, offering significant potential to enhance non-invasive detection, classification, and therefore individualized treatment of oral diseases. CLINICALTRIAL Deutsche Forschungsgemeinschaft (DFG) - Projectnumber 516210826 https://gepris.dfg.de/gepris/projekt/516210826

BACKGROUND Medical reports, particularly radiology findings, are often written for professional communication, making them difficult for patients to understand. This communication barrier can reduce patient engagement and lead to misinterpretation. Artificial intelligence (AI), especially large language models such as ChatGPT, offers new opportunities for simplifying medical documentation to improve patient comprehension. OBJECTIVE We aimed to evaluate whether AI-generated radiology reports simplified by ChatGPT improve patient understanding, readability, and communication quality compared to original AI-generated reports. METHODS In total, 3 versions of radiology reports were created using ChatGPT: an original AI-generated version (text 1), a patient-friendly, simplified version (text 2), and a further simplified and accessibility-optimized version (text 3). A total of 300 patients (n=100, 33.3% per group), excluding patients with medical education, were randomly assigned to review one text version and complete a standardized questionnaire. Readability was assessed using the Flesch Reading Ease (FRE) score and LIX indices. RESULTS Both simplified texts showed significantly higher readability scores (text 1: FRE score=51.1; text 2: FRE score=55.0; and text 3: FRE score=56.4; P<.001) and lower LIX scores, indicating enhanced clarity. Text 3 had the shortest sentences, had the fewest long words, and scored best on all patient-rated dimensions. Questionnaire results revealed significantly higher ratings for texts 2 and 3 across clarity (P<.001), tone (P<.001), structure, and patient engagement. For example, patients rated the ability to understand findings without help highest for text 3 (mean 1.5, SD 0.7) and lowest for text 1 (mean 3.1, SD 1.4). Both simplified texts significantly improved patients’ ability to prepare for clinical conversations and promoted shared decision-making. CONCLUSIONS AI-generated simplification of radiology reports significantly enhances patient comprehension and engagement. These findings highlight the potential of ChatGPT as a tool to improve patient-centered communication. While promising, future research should focus on ensuring clinical accuracy and exploring applications across diverse patient populations to support equitable and effective integration of AI in health care communication.

Objectives: This study aimed to establish a method to detect and quantify mucosal malperfusion intraorally using state-of-the-art Endoscopic Hyperspectral Imaging (EHSI). For this purpose, mucosal ischemia was selectively induced by intraligamentary anesthesia (ILA) with and without + epinephrine using a standardized protocol. Materials and methods: EHSI was performed using a novel endoscopic hyperspectral imaging system. Parameters assessed were Tissue Oxygen Saturation (StO₂ [%]), Tissue Hemoglobin Index (THI), Near Infrared Perfusion Index (NPI) and Tissue Water Index (TWI). Fifty-seven healthy subjects received ILA using Articaine 4% with (ILA+) and without (ILA-) epinephrine at a dosage of 1:200,000 administered mesially and distally to the target tooth 42 (Universal No. 26). Mucosal perfusion was assessed using EHSI for 45 min post-injection. Results: After ILA+, a distinct ischemia of the mucosa was already clinically apparent after 30 s with significant reduction of THI and StO₂ by an average of 57% (p < 0.001) and 7% (p < 0.040) compared to baseline values. Persistent hypoperfusion of the oral mucosa was observed throughout the monitoring period, exhibiting a gradual resolution at the 30-minute mark, and nearing baseline perfusion approximately 45 min post-injection. There was no papillary necrosis after ILA + injection. Conclusion: EHSI is suitable to adequately detect and visualize actual perfusion of the intraoral mucosa. The study revealed that LA with epinephrine (1:200,000) induce temporary hypoxia in the dental papilla but without causing severe ischemia. Clinical relevance: EHSI will enable promising applications in the future, i.a. success monitoring of periodontal therapies, intraoral free flap monitoring and the assessment of cancer margins.