Introdução à análise de dados em saúde com Python / Introduction to healthcare data analysis with Python

Este eBook foi elaborado no contexto do curso de capacitação Introdução à Análise de Dados em Saúde com Python ofertado pelo Centro de Inovação em Inteligência Artificial para Saúde. O curso tem como objetivo introduzir o estudo exploratório de bases de dados de saúde, com a utilização do Python. Neste eBook, procura-se apresentar uma abordagem preliminar à Ciência de Dados, que explora e descreve um conjunto de dados com técnicas da estatística descritiva e inferencial por meio da linguagem de programação Python. O público alvo que pretende-se atingir caracteriza-se por profissionais de saúde, alunos de graduação e pós-graduação, docentes e pesquisadores da área das ciências da saúde, exatas ou demais interessados em utilizar os recursos computacionais para análise de bases de dados em saúde. A linguagem Python tem se destacado como uma ferramenta poderosa para análise de dados em saúde, possuindo uma ampla gama de bibliotecas e recursos, o Python pode ser usado para limpar, processar, analisar e visualizar dados de saúde. Além disso, a comunidade de utilizadores da linguagem Python é muito colaborativa, com muitos recursos disponíveis, incluindo documentação, tutoriais e fóruns de suporte. O conteúdo foi agrupado em conceitos iniciais sobre a utilização dos dados em saúde, introdução ao Python para utilização de dados, conceitos de limpeza e tratamento de dados, aplicação da estatística descritiva com os sumários estatísticos e gráficos, técnicas de amostragens, aplicação da estatística inferencial com os testes de hipótese, de associação, de médias, de medianas e correlações, além de explorar a estilização de gráficos.

Medición de la mortalidad de pacientes internados por enfermedad cardiovascular mediante el desarrollo de un algoritmo de inteligencia artificial / Measurement of mortality during hospitalization due cardiovascular disease through the development of an artificial intelligence algorithm

Introducción: determinar la causa de muerte de los pacientes internados con enfermedad cardiovascular es de suma importancia para poder tomar medidas y así mejorar la calidad su atención y prevenir muertes evitables. Objetivos: determinar las principales causas de muerte durante la internación por enfermedades cardiovasculares. Desarrollar y validar un algoritmo para clasificar automáticamente a los pacientes fallecidos durante la internación con enfermedades cardiovasculares Diseño del estudio: estudio exploratorio retrospectivo. Desarrollo de un algoritmo de clasificación. Resultados: del total de 6161 pacientes, el 21,3% (1316) se internaron por causas cardiovasculares; las enfermedades cerebrovasculares representan el 30,7%, la insuficiencia cardíaca el 24,9% y las enfermedades cardíacas isquémicas el 14%. El algoritmo de clasificación según motivo de internación cardiovascular vs. no cardiovascular alcanzó una precisión de 0,9546 (IC 95%: 0,9351-0,9696). El algoritmo de clasificación de causa específica de internación cardiovascular alcanzó una precisión global de 0,9407 (IC 95%: 0,8866-0,9741). Conclusiones: la enfermedad cardiovascular representa el 21,3% de los motivos de internación de pacientes que fallecen durante su desarrollo. Los algoritmos presentaron en general buena performance, particularmente el de clasificación del motivo de internación cardiovascular y no cardiovascular y el clasificador según causa específica de internación cardiovascular. (AU)

Introduction: determining the cause of death of hospitalized patients with cardiovascular disease is of the utmost importance in order to take measures and thus improve the quality of care of these patients and prevent preventable deaths. Objectives: to determine the main causes of death during hospitalization due to cardiovascular diseases.To development and validate a natural language processing algorithm to automatically classify deceased patients according to their cause for hospitalization. Design: retrospective exploratory study. Development of a natural language processing classification algorithm. Results: of the total 6161 patients in our sample who died during hospitalization, 21.3% (1316) were hospitalized due to cardiovascular causes. The stroke represent 30.7%, heart failure 24.9%, and ischemic cardiac disease 14%. The classification algorithm for detecting cardiovascular vs. Non-cardiovascular admission diagnoses yielded an accuracy of 0.9546 (95% CI 0.9351, 0.9696), the algorithm for detecting specific cardiovascular cause of admission resulted in an overall accuracy of 0.9407 (95% CI 0.8866, 0.9741). Conclusions: cardiovascular disease represents 21.3% of the reasons for hospitalization of patients who die during hospital stays. The classification algorithms generally showed good performance, particularly the classification of cardiovascular vs non-cardiovascular cause for admission and the specific cardiovascular admission cause classifier. (AU)

Computed Tomography Image Segmentation of the Proximal Colon by U-Net for the Clinical Study of Somatostatin Combined with Intestinal Obstruction Catheter.

OBJECTIVE: U-Net technology is implemented for image segmentation to diagnose cases of intestinal obstruction. To evaluate the application value of somatostatin combined with transanal intestinal obstruction decompression catheter in the treatment of distal colonic malignant intestinal obstruction and to explore the therapeutic effect of somatostatin on acute abdomen surgery in patients with intestinal obstruction. METHODS: After the segmentation technique, a retrospective analysis of 30 patients with acute and complete distal colonic malignant obstruction treated by surgery was divided into a control group and an observation group according to a random number table. The treatment efficiency, clinical symptoms, disappearance time after treatment, and the incidence of complications were compared between the two groups of patients. RESULTS: The image segmentation using U-Net can effectively assist in the medical diagnosis of the colon. Our study found that patients with combined treatment with somatostatin and anal intestinal obstruction catheter were relieved of preoperative abdominal pain and abdominal distension; compared with the abdominal circumference at the time of admission, the abdominal circumference was significantly reduced. Abdominal examination was performed 3 days after comprehensive treatment, and combined with computed tomography (CT), we observed that the measured maximum transverse diameter of the proximal colon was significantly smaller than that before treatment. Before treatment, all patients were divided into a control group and a treatment group. After treatment, the symptoms of the two groups of patients were alleviated. The treatment effective rate of the observation group was 93.3%, and the treatment effective rate of the control group was 73.3%. The effective rate was significantly higher than that of the control group, and the difference was statistically significant. CONCLUSIONS: Through the use of image segmentation technology, somatostatin treatment of early inflammatory bowel obstruction after acute abdomen surgery can effectively improve the treatment efficiency of patients, shorten the disappearance of clinical symptoms, reduce the incidence of complications, and have a significant therapeutic effect, which is worthy of clinical application. Somatostatin combined with enteral obstruction catheter treatment is safe and effective for elderly patients with acute distal large bowel malignant intestinal obstruction. It has a higher completion rate of laparoscopic surgery and a first-stage anastomosis power, which reduces the risk of perioperative period and reduces the patient's financial burden.

An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B.

BACKGROUND & AIMS: Several models have recently been developed to predict risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB). Our aims were to develop and validate an artificial intelligence-assisted prediction model of HCC risk. METHODS: Using a gradient-boosting machine (GBM) algorithm, a model was developed using 6,051 patients with CHB who received entecavir or tenofovir therapy from 4 hospitals in Korea. Two external validation cohorts were independently established: Korean (5,817 patients from 14 Korean centers) and Caucasian (1,640 from 11 Western centers) PAGE-B cohorts. The primary outcome was HCC development. RESULTS: In the derivation cohort and the 2 validation cohorts, cirrhosis was present in 26.9%-50.2% of patients at baseline. A model using 10 parameters at baseline was derived and showed good predictive performance (c-index 0.79). This model showed significantly better discrimination than previous models (PAGE-B, modified PAGE-B, REACH-B, and CU-HCC) in both the Korean (c-index 0.79 vs. 0.64-0.74; all p <0.001) and Caucasian validation cohorts (c-index 0.81 vs. 0.57-0.79; all p <0.05 except modified PAGE-B, p = 0.42). A calibration plot showed a satisfactory calibration function. When the patients were grouped into 4 risk groups, the minimal-risk group (11.2% of the Korean cohort and 8.8% of the Caucasian cohort) had a less than 0.5% risk of HCC during 8 years of follow-up. CONCLUSIONS: This GBM-based model provides the best predictive power for HCC risk in Korean and Caucasian patients with CHB treated with entecavir or tenofovir. LAY SUMMARY: Risk scores have been developed to predict the risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B. We developed and validated a new risk prediction model using machine learning algorithms in 13,508 antiviral-treated patients with chronic hepatitis B. Our new model, based on 10 common baseline characteristics, demonstrated superior performance in risk stratification compared with previous risk scores. This model also identified a group of patients at minimal risk of developing HCC, who could be indicated for less intensive HCC surveillance.

Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial.

BACKGROUND: We have developed the computer-aided detection (CADe) system using an original deep learning algorithm based on a convolutional neural network for assisting endoscopists in detecting colorectal lesions during colonoscopy. The aim of this study was to clarify whether adenoma miss rate (AMR) could be reduced with CADe assistance during screening and surveillance colonoscopy. METHODS: This study was a multicenter randomized controlled trial. Patients aged 40 to 80 years who were referred for colorectal screening or surveillance at four sites in Japan were randomly assigned at a 1:1 ratio to either the "standard colonoscopy (SC)-first group" or the "CADe-first group" to undergo a back-to-back tandem procedure. Tandem colonoscopies were performed on the same day for each participant by the same endoscopist in a preassigned order. All polyps detected in each pass were histopathologically diagnosed after biopsy or resection. RESULTS: A total of 358 patients were enrolled and 179 patients were assigned to the SC-first group or CADe-first group. The AMR of the CADe-first group was significantly lower than that of the SC-first group (13.8% vs. 36.7%, P < 0.0001). Similar results were observed for the polyp miss rate (14.2% vs. 40.6%, P < 0.0001) and sessile serrated lesion miss rate (13.0% vs. 38.5%, P = 0.03). The adenoma detection rate of CADe-assisted colonoscopy was 64.5%, which was significantly higher than that of standard colonoscopy (53.6%; P = 0.036). CONCLUSION: Our study results first showed a reduction in the AMR when assisting with CADe based on deep learning in a multicenter randomized controlled trial.

Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review.

Purpose: The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain. Method: Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted. Results: 34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models. Conclusion: Machine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain.

Artificial intelligence in dermatopathology: Diagnosis, education, and research.

Artificial intelligence (AI) utilizes computer algorithms to carry out tasks with human-like intelligence. Convolutional neural networks, a type of deep learning AI, can classify basal cell carcinoma, seborrheic keratosis, and conventional nevi, highlighting the potential for deep learning algorithms to improve diagnostic workflow in dermatopathology of highly routine diagnoses. Additionally, convolutional neural networks can support the diagnosis of melanoma and may help predict disease outcomes. Capabilities of machine learning in dermatopathology can extend beyond clinical diagnosis to education and research. Intelligent tutoring systems can teach visual diagnoses in inflammatory dermatoses, with measurable cognitive effects on learners. Natural language interfaces can instruct dermatopathology trainees to produce diagnostic reports that capture relevant detail for diagnosis in compliance with guidelines. Furthermore, deep learning can power computation- and population-based research. However, there are many limitations of deep learning that need to be addressed before broad incorporation into clinical practice. The current potential of AI in dermatopathology is to supplement diagnosis, and dermatopathologist guidance is essential for the development of useful deep learning algorithms. Herein, the recent progress of AI in dermatopathology is reviewed with emphasis on how deep learning can influence diagnosis, education, and research.

Application of artificial intelligence in screening for adverse perinatal outcomes: A protocol for systematic review.

The article presents a systematic review protocol. The aim of the study is an assessment of current studies regarding the application of artificial intelligence and neural networks in the screening for adverse perinatal outcomes. We intend to compare the reported efficacy of these methods to improve pregnancy care and outcomes. There are more and more studies that describe the role of machine learning in facilitating the diagnosis of adverse perinatal outcomes, like gestational diabetes or pregnancy hypertension. A systematic review of available literature seems to be crucial to compare the known efficacy and application. Publication of a systematic review in this category would improve the value of future studies. The studies reporting on artificial intelligence application will have a major impact on future prenatal practice.

Thyroid Incidentalomas: Practice Considerations for Radiologists in the Age of Incidental Findings.

Radiologists very frequently encounter incidental findings related to the thyroid gland. Given increases in imaging use over the past several decades, thyroid incidentalomas are increasingly encountered in clinical practice, and it is important for radiologists to be aware of recent developments with respect to workup and diagnosis of incidental thyroid abnormalities. Recent reporting and management guidelines, such as those from the American College of Radiology and American Thyroid Association, are reviewed along with applicable evidence in the literature. Trending topics, such as artificial intelligence approaches to guide thyroid incidentaloma workup, are also discussed.

What is the point of innovation in patient dose monitoring?

The Medical Futurist says that radiology is one of the fastest growing and developing areas of medicine, and therefore this might be the speciality in which we can expect to see the largest steps in development. So why do they think that, and does it apply to dose monitoring? The move from retrospective dose evaluation to a proactive dose management approach represents a serious area of research. Indeed, artificial intelligence and machine learning are consistently being integrated into best-in-class dose management software solutions. The development of clinical analytics and dashboards are already supporting operators in their decision-making, and these optimisations - if taken beyond a single machine, a single department, or a single health network - have the potential to drive real and lasting change. The question is for whom exactly are these innovations being developed? How can the patient know that their scan has been performed to the absolute best that the technology can deliver? Do they know or even care how much their lifetime risk for developing cancer has changed post examination? Do they want a personalised size-specific dose estimate or perhaps an individual organ dose assessment to share on Instagram? Let's get real about the clinical utility and regulatory application of dose monitoring, and shine a light on the shared responsibility in applying the technology and the associated innovations.

A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas.

Glioblastoma is the most common malignant brain parenchymal tumor yet remains challenging to treat. The current standard of care-resection and chemoradiation-is limited in part due to the genetic heterogeneity of glioblastoma. Previous studies have identified several tumor genetic biomarkers that are frequently present in glioblastoma and can alter clinical management. Currently, genetic biomarker status is confirmed with tissue sampling, which is costly and only available after tumor resection or biopsy. The purpose of this study was to evaluate a fully automated artificial intelligence approach for predicting the status of several common glioblastoma genetic biomarkers on preoperative MRI. We retrospectively analyzed multisequence preoperative brain MRI from 199 adult patients with glioblastoma who subsequently underwent tumor resection and genetic testing. Radiomics features extracted from fully automated deep learning-based tumor segmentations were used to predict nine common glioblastoma genetic biomarkers with random forest regression. The proposed fully automated method was useful for predicting IDH mutations (sensitivity = 0.93, specificity = 0.88), ATRX mutations (sensitivity = 0.94, specificity = 0.92), chromosome 7/10 aneuploidies (sensitivity = 0.90, specificity = 0.88), and CDKN2 family mutations (sensitivity = 0.76, specificity = 0.86).

Automated quantification and architectural pattern detection of hepatic fibrosis in NAFLD.

Accurate detection and quantification of hepatic fibrosis remain essential for assessing the severity of non-alcoholic fatty liver disease (NAFLD) and its response to therapy in clinical practice and research studies. Our aim was to develop an integrated artificial intelligence-based automated tool to detect and quantify hepatic fibrosis and assess its architectural pattern in NAFLD liver biopsies. Digital images of the trichrome-stained slides of liver biopsies from patients with NAFLD and different severity of fibrosis were used. Two expert liver pathologists semi-quantitatively assessed the severity of fibrosis in these biopsies and using a web applet provided a total of 987 annotations of different fibrosis types for developing, training and testing supervised machine learning models to detect fibrosis. The collagen proportionate area (CPA) was measured and correlated with each of the pathologists semi-quantitative fibrosis scores. Models were created and tested to detect each of six potential fibrosis patterns. There was good to excellent correlation between CPA and the pathologist score of fibrosis stage. The coefficient of determination (R2) of automated CPA with the pathologist stages ranged from 0.60 to 0.86. There was considerable overlap in the calculated CPA across different fibrosis stages. For identification of fibrosis patterns, the models areas under the receiver operator curve were 78.6% for detection of periportal fibrosis, 83.3% for pericellular fibrosis, 86.4% for portal fibrosis and >90% for detection of normal fibrosis, bridging fibrosis, and presence of nodule/cirrhosis. In conclusion, an integrated automated tool could accurately quantify hepatic fibrosis and determine its architectural patterns in NAFLD liver biopsies.

Application of artificial intelligence using convolutional neural networks in determining the invasion depth of esophageal squamous cell carcinoma.

OBJECTIVES: In Japan, endoscopic resection (ER) is often used to treat esophageal squamous cell carcinoma (ESCC) when invasion depths are diagnosed as EP-SM1, whereas ESCC cases deeper than SM2 are treated by surgical operation or chemoradiotherapy. Therefore, it is crucial to determine the invasion depth of ESCC via preoperative endoscopic examination. Recently, rapid progress in the utilization of artificial intelligence (AI) with deep learning in medical fields has been achieved. In this study, we demonstrate the diagnostic ability of AI to measure ESCC invasion depth. METHODS: We retrospectively collected 1751 training images of ESCC at the Cancer Institute Hospital, Japan. We developed an AI-diagnostic system of convolutional neural networks using deep learning techniques with these images. Subsequently, 291 test images were prepared and reviewed by the AI-diagnostic system and 13 board-certified endoscopists to evaluate the diagnostic accuracy. RESULTS: The AI-diagnostic system detected 95.5% (279/291) of the ESCC in test images in 10 s, analyzed the 279 images and correctly estimated the invasion depth of ESCC with a sensitivity of 84.1% and accuracy of 80.9% in 6 s. The accuracy score of this system exceeded those of 12 out of 13 board-certified endoscopists, and its area under the curve (AUC) was greater than the AUCs of all endoscopists. CONCLUSIONS: The AI-diagnostic system demonstrated a higher diagnostic accuracy for ESCC invasion depth than those of endoscopists and, therefore, can be potentially used in ESCC diagnostics.

Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy.

BACKGROUND/AIM: To study the impact of computer-aided detection (CADe) system on the detection rate of polyps and adenomas in colonoscopy. MATERIALS AND METHODS: A total of 1026 patients were prospectively randomly scheduled for colonoscopy with (the CADe group, CADe) or without (the control group, CON) the aid of the CADe system, together with visual notification and voice alarm, so as to compare the detection rate of polyp. RESULTS: Compared with group CON, the detection rate of adenomas increased in group CADe, the average number of adenomas increased, the number of small adenomas increased, the number of proliferative polyps increased, and the differences were statistically significant (P < 0.001), but the comparison for the number of larger adenomas showed no significant difference between the groups (P> 0.05). CONCLUSIONS: The CADe system is feasible for increasing the detection of polyps and adenomas in colonoscopy.

Ethical Considerations in the Advent of Artificial Intelligence in Otolaryngology.

Artificial intelligence (AI) is quickly expanding within the sphere of health care, offering the potential to enhance the efficiency of care delivery, diminish costs, and reduce diagnostic and therapeutic errors. As the field of otolaryngology also explores use of AI technology in patient care, a number of ethical questions warrant attention prior to widespread implementation of AI. This commentary poses many of these ethical questions for consideration by the otolaryngologist specifically, using the 4 pillars of medical ethics-autonomy, beneficence, nonmaleficence, and justice-as a framework and advocating both for the assistive role of AI in health care and for the shared decision-making, empathic approach to patient care.

Utility of a public-available artificial intelligence in diagnosis of polypoidal choroidal vasculopathy.

PURPOSE: To investigate the feasibility of training an artificial intelligence (AI) on a public-available AI platform to diagnose polypoidal choroidal vasculopathy (PCV) using indocyanine green angiography (ICGA). METHODS: Two methods using AI models were trained by a data set including 430 ICGA images of normal, neovascular age-related macular degeneration (nvAMD), and PCV eyes on a public-available AI platform. The one-step method distinguished normal, nvAMD, and PCV images simultaneously. The two-step method identifies normal and abnormal ICGA images at the first step and diagnoses PCV from the abnormal ICGA images at the second step. The method with higher performance was used to compare with retinal specialists and ophthalmologic residents on the performance of diagnosing PCV. RESULTS: The two-step method had better performance, in which the precision was 0.911 and the recall was 0.911 at the first step, and the precision was 0.783, and the recall was 0.783 at the second step. For the test data set, the two-step method distinguished normal and abnormal images with an accuracy of 1 and diagnosed PCV with an accuracy of 0.83, which was comparable to retinal specialists and superior to ophthalmologic residents. CONCLUSION: In this evaluation of ICGA images from normal, nvAMD, and PCV eyes, the models trained on a public-available AI platform had comparable performance to retinal specialists for diagnosing PCV. The utility of public-available AI platform might help everyone including ophthalmologists who had no AI-related resources, especially those in less developed areas, for future studies.

The Use of Emotional Artificial Intelligence in Plastic Surgery.

BACKGROUND: The use of social media to discuss topics related to and within plastic surgery has become widespread in recent years; however, it remains unclear how to use this abundance of largely untapped data to propagate educational research in the field of plastic surgery. In this prospective, observational study, the authors aimed to delineate which plastic surgery-related topics evoked a significant emotional response within the study population and to assess the utility of motivational artificial intelligence within the field of plastic surgery. METHODS: Over a 4-month period (January to April of 2018), Cognovi Lab's artificial intelligence technology was used to search and analyze emotional reactions to several commonly hashtagged words. This innovative software uses several key metrics to describe its findings, including awareness, engagement, and motivation. RESULTS: Of the search terms examined, "nose job" had the most awareness during the study period, and the topic that most engaged consumers emotionally was "liposuction." Interestingly, "liposuction" ranked only fifth in terms of awareness. Consumers showed the strongest positive motivation toward the subjects of "plastic surgery" and "cosmetic surgery," and the lowest motivation toward the topic of "tummy tucks." CONCLUSIONS: This analysis by Cognovi Labs is the first quantitative effort to use the plethora of data on social media to interpret patient motivations and subsequent behavior. Moving forward, artificial intelligence technology will make it possible to predict which plastic surgery products, procedures, and practices will be successful. The findings presented in this article describe the unique viewpoint and power that this technology can deliver.

Clinical Documentation and Patient Care Using Artificial Intelligence in Radiation Oncology.

Detailed clinical documentation is required in the patient-facing specialty of radiation oncology. The burden of clinical documentation has increased significantly with the introduction of electronic health records and participation in payer-mandated quality initiatives. Artificial intelligence (AI) has the potential to reduce the burden of data entry associated with clinical documentation, provide clinical decision support, improve quality and value, and integrate patient data from multiple sources. The authors discuss key elements of an AI-enhanced clinic and review some emerging technologies in the industry. Challenges regarding data privacy, regulation, and medicolegal liabilities must be addressed for such AI technologies to be successful.