A Cross-Sectional Study on Whether Comprehensively Gathering Information From Medical Records Is Useful for the Collection of Operational Characteristics.

This study tests whether comprehensively gathering information from medical records is useful for developing clinical decision support systems using Bayes' theorem. Using a single-center cross-sectional study, we retrospectively extracted medical records of 270 patients aged ≥16 years who visited the emergency room at the Tokyo Metropolitan Tama Medical Center with a chief complaint of experiencing headaches. The medical records of cases were analyzed in this study. We manually extracted diagnoses, unique keywords, and annotated keywords, classifying them as either positive or negative. Cross tables were created, and the proportion of combinations for which the likelihood ratios could be calculated was evaluated. Probability functions for the appearance of new unique keywords were modeled, and theoretical values were calculated. We extracted 623 unique keywords, 26 diagnoses, and 6,904 annotated keywords. Likelihood ratios could be calculated only for 276 combinations (1.70%), of which 24 (0.15%) exhibited significant differences. The power function+constant was the best fit for new unique keywords. The increase in the number of combinations after increasing the number of cases indicated that while it is theoretically possible to comprehensively gather information from medical records in this way, doing so presents difficulties related to human costs. It also does not necessarily solve the fundamental issues with medical informatics or with developing clinical decision support systems. Therefore, we recommend using methods other than comprehensive information gathering with Bayes' theorem as the classifier to develop such systems.

Expert assessment of ChatGPT's ability to generate illness scripts: an evaluative study.

BACKGROUND: An illness script is a specific script format geared to represent patient-oriented clinical knowledge organized around enabling conditions, faults (i.e., pathophysiological process), and consequences. Generative artificial intelligence (AI) stands out as an educational aid in continuing medical education. The effortless creation of a typical illness script by generative AI could help the comprehension of key features of diseases and increase diagnostic accuracy. No systematic summary of specific examples of illness scripts has been reported since illness scripts are unique to each physician. OBJECTIVE: This study investigated whether generative AI can generate illness scripts. METHODS: We utilized ChatGPT-4, a generative AI, to create illness scripts for 184 diseases based on the diseases and conditions integral to the National Model Core Curriculum in Japan for undergraduate medical education (2022 revised edition) and primary care specialist training in Japan. Three physicians applied a three-tier grading scale: "A" denotes that the content of each disease's illness script proves sufficient for training medical students, "B" denotes that it is partially lacking but acceptable, and "C" denotes that it is deficient in multiple respects. RESULTS: By leveraging ChatGPT-4, we successfully generated each component of the illness script for 184 diseases without any omission. The illness scripts received "A," "B," and "C" ratings of 56.0% (103/184), 28.3% (52/184), and 15.8% (29/184), respectively. CONCLUSION: Useful illness scripts were seamlessly and instantaneously created using ChatGPT-4 by employing prompts appropriate for medical students. The technology-driven illness script is a valuable tool for introducing medical students to key features of diseases.

Importance of Patient History in Artificial Intelligence-Assisted Medical Diagnosis: Comparison Study.

Background: Medical history contributes approximately 80% to a diagnosis, although physical examinations and laboratory investigations increase a physician's confidence in the medical diagnosis. The concept of artificial intelligence (AI) was first proposed more than 70 years ago. Recently, its role in various fields of medicine has grown remarkably. However, no studies have evaluated the importance of patient history in AI-assisted medical diagnosis. Objective: This study explored the contribution of patient history to AI-assisted medical diagnoses and assessed the accuracy of ChatGPT in reaching a clinical diagnosis based on the medical history provided. Methods: Using clinical vignettes of 30 cases identified in The BMJ, we evaluated the accuracy of diagnoses generated by ChatGPT. We compared the diagnoses made by ChatGPT based solely on medical history with the correct diagnoses. We also compared the diagnoses made by ChatGPT after incorporating additional physical examination findings and laboratory data alongside history with the correct diagnoses. Results: ChatGPT accurately diagnosed 76.6% (23/30) of the cases with only the medical history, consistent with previous research targeting physicians. We also found that this rate was 93.3% (28/30) when additional information was included. Conclusions: Although adding additional information improves diagnostic accuracy, patient history remains a significant factor in AI-assisted medical diagnosis. Thus, when using AI in medical diagnosis, it is crucial to include pertinent and correct patient histories for an accurate diagnosis. Our findings emphasize the continued significance of patient history in clinical diagnoses in this age and highlight the need for its integration into AI-assisted medical diagnosis systems.

The differential diagnosis of medical and psychogenic disease in primary care.

Diagnosis and management of psychogenic diseases such as conversion disorder, somatic symptom disorder (SSD), illness anxiety disorder, falsehood disorder, and psychotic disorder require an elaborate biopsychosocial approach and are often challenging. Herein, we propose the following points to differentiate medical diseases from these psychogenic diseases: correspondence between symptoms and objective findings or activities of daily living (ADL) impairment; placebo effect; clear provocative or palliative factors; progressive time course; paroxysmal or intermittent symptoms; unfamiliar but not strange expressions; symptoms worsen during sleep or rest.

Atypical Kikuchi-Fujimoto Disease: FDG-PET Contribution To The Diagnosis.

Kikuchi-Fujimoto disease (KFD), also called histiocytic necrotizing lymphadenitis, is more common in young women and typically presents with small, painful, localized cervical lymphadenopathy that resolves spontaneously within a few weeks. Laboratory findings are variable. As many as 40% of KFD cases are reported to be painless, and up to 22% to be generalized lymphadenopathy. Therefore, malignant lymphoma could be a differential diagnosis of KFD. A histopathologic diagnosis is needed when it is difficult to distinguish KFD from lymphoma. KFD typically shows small, highly accumulated cervical lymph nodes on fluorodeoxyglucose positron emission tomography (FDG-PET). This contrasts with malignant lymphoma, which tends to be associated with massive lymphadenopathy. In our case, a 40-year-old Japanese male presented with painless lumps in the right neck, accompanied by fever, night sweats, and loss of appetite. His symptoms and laboratory results worsened over a month. FDG-PET revealed highly accumulated uptake in cervical, mediastinal, and axillary lymph nodes. The PET imaging showed a small, high FDG uptake and contributed to the correct diagnosis of KFD. This case report highlights the importance of FDG-PET, which is a valuable diagnostic tool for KFD as it typically differentiates large clusters of small lymph nodes typical of KFD from normal lymph nodes. LEARNING POINTS: Kikuchi-Fujimoto disease (KFD) typically presents with small, painful, localised cervical lymphadenopathy.KFD has atypical patterns showing painless and generalised lymphadenopathy.Fluorodeoxyglucose positron emission tomography (FDG-PET) could be useful for diagnosing not only malignant lymphoma but also KFD.

A Stepwise Progression of Acute Bilateral Visual Loss Due to Onodi Cell Sinusitis.

Acute visual loss in an immunocompromised patient may be caused by acute invasive fungal sinusitis (AIFS), even if symptoms include only mild headache and computed tomography (CT) shows only mild sinusitis, especially of the Onodi cell. Herein, we report a case of a 71-year-old man with a medical history of dermatomyositis and type 2 diabetes mellitus who presented with a stepwise progression of acute bilateral visual loss, mild headache, and altered consciousness. Initially, as the plain cranial CT showed only mild fluid retention in the posterior ethmoid sinus without bone destruction, the sinusitis was considered unrelated to the visual loss. Afterward, however, contrast-enhanced cranial magnetic resonance imaging (MRI) showed mucosal thickening, fluid retention in the posterior ethmoid sinus, and spread of the contrast medium over the dura around the right posterior ethmoid sinus and bilateral optic nerve tracts. Aspergillus fumigatus was identified from endoscopic drainage of the sinus. The patient was diagnosed with AIFS and treated with amphotericin B 350 mg/day. The altered sensorium and headache rapidly improved, and his left visual acuity improved to counting fingers. Although AIFS is rare, it can cause severe sequela or death due to vascular or direct intracranial invasion. Therefore, immediate drainage of the sinus and intravenous antifungal therapy are essential for AIFS. Our findings will help physicians make accurate and rapid diagnoses of AIFS in future cases.

The influence of Gamification on medical students' diagnostic decision making and awareness of medical cost: a mixed-method study.

BACKGROUND: The gamification of learning increases student enjoyment, and motivation and engagement in learning tasks. This study investigated the effects of gamification using decision-making cards (DMCs) on diagnostic decision-making and cost using case scenarios. METHOD: Thirty medical students in clinical clerkship participated and were randomly assigned to 14 small groups of 2-3 medical students each. Decision-making was gamified using DMCs with a clinical information heading and medical cost on the front, and clinical information details on the back. First, each team was provided with brief clinical information on case scenarios. Subsequently, DMCs depending on the case were distributed to each team, and team members chose cards one at a time until they reached a diagnosis of the case. The total medical cost was then scored based on the number and contents of cards drawn. Four case scenarios were conducted. The quantitative outcomes including confidence in effective clinical decision-making, motivation to learn diagnostic decision-making, and awareness of medical costs were measured before and after our gamification by self-evaluation using a 7-point Likert scale. The qualitative component consisted of a content analysis on the benefits of learning clinical reasoning using DMCs. RESULT: Confidence in effective clinical decision-making, motivation to learn diagnostic decision-making, and awareness of medical cost were significantly higher after the gamification. Furthermore, comparing the clinical case scenario tackled last with the one tackled first, the average medical cost of all cards drawn by students decreased significantly from 11,921 to 8,895 Japanese yen. In the content analysis, seven advantage categories of DMCs corresponding to clinical reasoning components were extracted (information gathering, hypothesis generation, problem representation, differential diagnosis, leading or working diagnosis, diagnostic justification, and management and treatment). CONCLUSION: Teaching medical students clinical reasoning using DMCs can improve clinical decision-making confidence and learning motivation, and reduces medical cost in clinical case scenarios. In addition, it can help students to acquire practical knowledge, deepens their understanding of clinical reasoning, and identifies several important clinical reasoning skills including diagnostic decision-making and awareness of medical costs. Gamification using DMCs can be an effective teaching method for improving medical students' diagnostic decision-making and reducing costs.

Accuracy of ChatGPT on Medical Questions in the National Medical Licensing Examination in Japan: Evaluation Study.

BACKGROUND: ChatGPT (OpenAI) has gained considerable attention because of its natural and intuitive responses. ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers, as stated by OpenAI as a limitation. However, considering that ChatGPT is an interactive AI that has been trained to reduce the output of unethical sentences, the reliability of the training data is high and the usefulness of the output content is promising. Fortunately, in March 2023, a new version of ChatGPT, GPT-4, was released, which, according to internal evaluations, was expected to increase the likelihood of producing factual responses by 40% compared with its predecessor, GPT-3.5. The usefulness of this version of ChatGPT in English is widely appreciated. It is also increasingly being evaluated as a system for obtaining medical information in languages other than English. Although it does not reach a passing score on the national medical examination in Chinese, its accuracy is expected to gradually improve. Evaluation of ChatGPT with Japanese input is limited, although there have been reports on the accuracy of ChatGPT's answers to clinical questions regarding the Japanese Society of Hypertension guidelines and on the performance of the National Nursing Examination. OBJECTIVE: The objective of this study is to evaluate whether ChatGPT can provide accurate diagnoses and medical knowledge for Japanese input. METHODS: Questions from the National Medical Licensing Examination (NMLE) in Japan, administered by the Japanese Ministry of Health, Labour and Welfare in 2022, were used. All 400 questions were included. Exclusion criteria were figures and tables that ChatGPT could not recognize; only text questions were extracted. We instructed GPT-3.5 and GPT-4 to input the Japanese questions as they were and to output the correct answers for each question. The output of ChatGPT was verified by 2 general practice physicians. In case of discrepancies, they were checked by another physician to make a final decision. The overall performance was evaluated by calculating the percentage of correct answers output by GPT-3.5 and GPT-4. RESULTS: Of the 400 questions, 292 were analyzed. Questions containing charts, which are not supported by ChatGPT, were excluded. The correct response rate for GPT-4 was 81.5% (237/292), which was significantly higher than the rate for GPT-3.5, 42.8% (125/292). Moreover, GPT-4 surpassed the passing standard (>72%) for the NMLE, indicating its potential as a diagnostic and therapeutic decision aid for physicians. CONCLUSIONS: GPT-4 reached the passing standard for the NMLE in Japan, entered in Japanese, although it is limited to written questions. As the accelerated progress in the past few months has shown, the performance of the AI will improve as the large language model continues to learn more, and it may well become a decision support system for medical professionals by providing more accurate information.

Do Japanese word-embedded representations obtained in the academic corpus retain the medical concepts of "infarction"?

OBJECTIVE: The pathophysiological concepts of diseases are encapsulated in patients' medical histories. Whether information on the pathophysiology or anatomy of "infarction" can be preserved and objectively expressed in the distributed representation obtained from a corpus of scientific Japanese medical texts in the "infarction" domain is currently unknown. Word2Vec was used to obtain distributed representations, meanings, and word analogies of word vectors, and this process was verified mathematically. MATERIALS & METHODS: The texts were abstracts that were obtained by searching for "infarction," "abstract," and "case report" in the Japan Medical Journal Association's Ichushi Data Base. The abstracted text was morphologically analyzed to produce word sequences converted into their standard form. MeCab was used for morphological analysis and mecab-ipadic-NEologd and ComeJisyo were used as dictionaries. The accuracy of the known tasks for medical terms was evaluated using a word analogy task specific to the "infarction" domain. RESULTS: Only 33 % of the word analogy tasks for medical terminology were correct. However, 52 % of the new original tasks, which were specific to the "infarction" domain, were correct, especially those regarding anatomical differences. DISCUSSION: Documents related to "infarction" were collected from a corpus of Japanese medical documents and word-embedded expressions were obtained using Word2Vec. Terminology that had similar meanings to "infarction" included words such as "cavity" and "ischemia," which suggest the pathology of an infarction. CONCLUSION: The pathophysiological and anatomical features of an "infarction" may be retained in a distributed representation.

Pharyngeal Haemorrhage Immediately After Intake of Fried Foods: Angina Bullosa Haemorrhagica.

Angina bullosa haemorrhagica (ABH) is a disease of unknown cause that occurs most frequently in middle-aged and older adults and is characterized by the destruction of blood vessels in the submucosal layer of the middle pharynx and larynx centred on the soft palate, resulting in the formation of haemorrhagic blisters. It usually resolves within a day and heals without scarring within about a week. No treatment is necessary. However, cases of airway obstruction due to haematemesis have been reported, and this potential risk should be considered when tracheal intubation or upper gastrointestinal endoscopy is being performed. In this report, we describe the case of a 50-year-old man who developed a haematoma in the pharynx following upper endoscopy, which spontaneously ruptured and healed, leading to the diagnosis of ABH. The main purpose of this case report is to remind the reader that ABH improves without treatment, thus eliminating the need for unnecessary examination, and that there is a risk of airway obstruction depending on the site of the lesion. LEARNING POINTS: The key to the diagnosis of angina bullosa haemorrhagica (ABH) is a history of acute haemorrhagic vesicles caused by an external stimulus such as food or intubation, which resolve without scarring within a week or so.ABH can occur at any oropharyngeal site, but its occurrence in the pharyngeal region raises the risk of airway obstruction due to haematemesis.

Improving decision accuracy using a clinical decision support system for medical students during history-taking: a randomized clinical trial.

BACKGROUND: A clinical diagnostic support system (CDSS) can support medical students and physicians in providing evidence-based care. In this study, we investigate diagnostic accuracy based on the history of present illness between groups of medical students using a CDSS, Google, and neither (control). Further, the degree of diagnostic accuracy of medical students using a CDSS is compared with that of residents using neither a CDSS nor Google. METHODS: This study is a randomized educational trial. The participants comprised 64 medical students and 13 residents who rotated in the Department of General Medicine at Chiba University Hospital from May to December 2020. The medical students were randomly divided into the CDSS group (n = 22), Google group (n = 22), and control group (n = 20). Participants were asked to provide the three most likely diagnoses for 20 cases, mainly a history of a present illness (10 common and 10 emergent diseases). Each correct diagnosis was awarded 1 point (maximum 20 points). The mean scores of the three medical student groups were compared using a one-way analysis of variance. Furthermore, the mean scores of the CDSS, Google, and residents' (without CDSS or Google) groups were compared. RESULTS: The mean scores of the CDSS (12.0 ± 1.3) and Google (11.9 ± 1.1) groups were significantly higher than those of the control group (9.5 ± 1.7; p = 0.02 and p = 0.03, respectively). The residents' group's mean score (14.7 ± 1.4) was higher than the mean scores of the CDSS and Google groups (p = 0.01). Regarding common disease cases, the mean scores were 7.4 ± 0.7, 7.1 ± 0.7, and 8.2 ± 0.7 for the CDSS, Google, and residents' groups, respectively. There were no significant differences in mean scores (p = 0.1). CONCLUSIONS: Medical students who used the CDSS and Google were able to list differential diagnoses more accurately than those using neither. Furthermore, they could make the same level of differential diagnoses as residents in the context of common diseases. TRIAL REGISTRATION: This study was retrospectively registered with the University Hospital Medical Information Network Clinical Trials Registry on 24/12/2020 (unique trial number: UMIN000042831).

Do clinical interview transcripts generated by speech recognition software improve clinical reasoning performance in mock patient encounters? A prospective observational study.

BACKGROUND: To investigate whether speech recognition software for generating interview transcripts can provide more specific and precise feedback for evaluating medical interviews. METHODS: The effects of the two feedback methods on student performance in medical interviews were compared using a prospective observational trial. Seventy-nine medical students in a clinical clerkship were assigned to receive either speech-recognition feedback (n = 39; SRS feedback group) or voice-recording feedback (n = 40; IC recorder feedback group). All students' medical interviewing skills during mock patient encounters were assessed twice, first using a mini-clinical evaluation exercise (mini-CEX) and then a checklist. Medical students then made the most appropriate diagnoses based on medical interviews. The diagnostic accuracy, mini-CEX, and checklist scores of the two groups were compared. RESULTS: According to the study results, the mean diagnostic accuracy rate (SRS feedback group:1st mock 51.3%, 2nd mock 89.7%; IC recorder feedback group, 57.5%-67.5%; F(1, 77) = 4.0; p = 0.049), mini-CEX scores for overall clinical competence (SRS feedback group: 1st mock 5.2 ± 1.1, 2nd mock 7.4 ± 0.9; IC recorder feedback group: 1st mock 5.6 ± 1.4, 2nd mock 6.1 ± 1.2; F(1, 77) = 35.7; p < 0.001), and checklist scores for clinical performance (SRS feedback group: 1st mock 12.2 ± 2.4, 2nd mock 16.1 ± 1.7; IC recorder feedback group: 1st mock 13.1 ± 2.5, 2nd mock 13.8 ± 2.6; F(1, 77) = 26.1; p < 0.001) were higher with speech recognition-based feedback. CONCLUSIONS: Speech-recognition-based feedback leads to higher diagnostic accuracy rates and higher mini-CEX and checklist scores. TRIAL REGISTRATION: This study was registered in the Japan Registry of Clinical Trials on June 14, 2022. Due to our misunderstanding of the trial registration requirements, we registered the trial retrospectively. This study was registered in the Japan Registry of Clinical Trials on 7/7/2022 (Clinical trial registration number: jRCT1030220188).

Pubic Osteomyelitis in a Young Athlete.

We describe a case of pubic osteomyelitis in a 17-year-old Japanese male. The patient presented with acute left groin pain and left lower quadrant pain. He was evaluated at another hospital where pelvic X-ray/computed tomography was normal, and laboratory testing revealed only high C-reactive protein. Pelvic magnetic resonance imaging (MRI) on day three showed inflammation of the pubic attachment of the rectus abdominis muscle. Furthermore, a pelvic MRI performed 10 days after onset revealed a high signal on T2 short-TI inversion recovery in the left pubic bone, which was not found in the previous MRI, leading to a diagnosis of left pubic osteomyelitis. Symptoms improved rapidly after antibiotic therapy, and treatment was completed after six weeks. When a young athlete presents with fever and acute inguinal pain, osteomyelitis of the pubic bone should be considered as a differential diagnosis. This case report emphasizes the importance of taking a sports history during the interview and performing a repeat MRI for the early diagnosis of osteomyelitis of the pubic bone.