Pros and cons of artificial intelligence implementation in diagnostic pathology

van Diest, Paul J; Flach, Rachel N; van Dooijeweert, Carmen; Makineli, Seher; Breimer, Gerben E; Stathonikos, Nikolas; Pham, Paul; Nguyen, Tri Q; Veta, Mitko

doi:10.1111/his.15153

Cited by 4 publications

References 70 publications

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Enhancing Health Literacy: Evaluating the Readability of Patient Handouts Revised by ChatGPT's Large Language Model

Swisher,

Wu,

Liu

et al. 2024

Otolaryngol.--head neck surg.

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ObjectiveTo use an artificial intelligence (AI)‐powered large language model (LLM) to improve readability of patient handouts.Study DesignReview of online material modified by AI.SettingAcademic center.MethodsFive handout materials obtained from the American Rhinologic Society (ARS) and the American Academy of Facial Plastic and Reconstructive Surgery websites were assessed using validated readability metrics. The handouts were inputted into OpenAI's ChatGPT‐4 after prompting: “Rewrite the following at a 6th‐grade reading level.” The understandability and actionability of both native and LLM‐revised versions were evaluated using the Patient Education Materials Assessment Tool (PEMAT). Results were compared using Wilcoxon rank‐sum tests.ResultsThe mean readability scores of the standard (ARS, American Academy of Facial Plastic and Reconstructive Surgery) materials corresponded to “difficult,” with reading categories ranging between high school and university grade levels. Conversely, the LLM‐revised handouts had an average seventh‐grade reading level. LLM‐revised handouts had better readability in nearly all metrics tested: Flesch‐Kincaid Reading Ease (70.8 vs 43.9; P < .05), Gunning Fog Score (10.2 vs 14.42; P < .05), Simple Measure of Gobbledygook (9.9 vs 13.1; P < .05), Coleman‐Liau (8.8 vs 12.6; P < .05), and Automated Readability Index (8.2 vs 10.7; P = .06). PEMAT scores were significantly higher in the LLM‐revised handouts for understandability (91 vs 74%; P < .05) with similar actionability (42 vs 34%; P = .15) when compared to the standard materials.ConclusionPatient‐facing handouts can be augmented by ChatGPT with simple prompting to tailor information with improved readability. This study demonstrates the utility of LLMs to aid in rewriting patient handouts and may serve as a tool to help optimize education materials.Level of EvidenceLevel VI.

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Enhancing Health Literacy: Evaluating the Readability of Patient Handouts Revised by ChatGPT's Large Language Model

Swisher,

Wu,

Liu

et al. 2024

Otolaryngol.--head neck surg.

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Human–machine interaction in computational cancer pathology

Syrnioti,

Polónia,

Pinto

et al. 2024

ESMO Real World Data and Digital Oncology

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Artificial Intelligence for Diagnosis in Otologic Patients: Is It Ready to Be Your Doctor?

Marshall,

Forbes,

Seidman

et al. 2024

Otol Neurotol

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Objective Investigate the precision of language-model artificial intelligence (AI) in diagnosing conditions by contrasting its predictions with diagnoses made by board-certified otologic/neurotologic surgeons using patient-described symptoms. Study Design Prospective cohort study. Setting Tertiary care center. Patients One hundred adults participated in the study. These included new patients or established patients returning with new symptoms. Individuals were excluded if they could not provide a written description of their symptoms. Interventions Summaries of the patient's symptoms were supplied to three publicly available AI platforms: Chat GPT 4.0, Google Bard, and WebMD “Symptom Checker.” Main Outcome Measures This study evaluates the accuracy of three distinct AI platforms in diagnosing otologic conditions by comparing AI results with the diagnosis determined by a neurotologist with the same information provided to the AI platforms and again after a complete history and physical examination. Results The study includes 100 patients (52 men and 48 women; average age of 59.2 yr). Fleiss' kappa between AI and the physician is −0.103 (p < 0.01). The chi-squared test between AI and the physician is χ 2 = 12.95 (df = 2; p < 0.001). Fleiss' kappa between AI models is 0.409. Diagnostic accuracies are 22.45, 12.24, and 5.10% for ChatGPT 4.0, Google Bard, and WebMD, respectively. Conclusions Contemporary language-model AI platforms can generate extensive differential diagnoses with limited data input. However, doctors can refine these diagnoses through focused history-taking, physical examinations, and clinical experience—skills that current AI platforms lack.

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Pros and cons of artificial intelligence implementation in diagnostic pathology

Cited by 4 publications

References 70 publications

Enhancing Health Literacy: Evaluating the Readability of Patient Handouts Revised by ChatGPT's Large Language Model

Enhancing Health Literacy: Evaluating the Readability of Patient Handouts Revised by ChatGPT's Large Language Model

Human–machine interaction in computational cancer pathology

Artificial Intelligence for Diagnosis in Otologic Patients: Is It Ready to Be Your Doctor?

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