Artificial Intelligence Assistance Research Articles

Trojan horse or useful helper? A relationship perspective on artificial intelligence assistants with humanlike features

Artificial intelligence assistants (AIAs) such as Alexa are prevalent in consumers’ homes. Owing to their powerful artificial intelligence, consumers may perceive that AIAs have a mind of their own, that is, they anthropomorphize them. Past marketing research points to beneficial effects of AIA anthropomorphism for consumers and companies, while potential harmful effects have not been empirically explored. In examining both beneficial and harmful effects, this paper adopts a relationship perspective. Indeed, consumers spend large amounts of time with their AIAs, potentially developing a relationship over time that builds on an exchange of benefits and (psychological) costs. A preliminary survey and user interviews, a field study and a field experiment with AIA users show that AIA anthropomorphism may threaten users’ identity, which disempowers them, creates data privacy concerns and ultimately undermines their well-being. These harmful effects particularly emerge in close, long relationships. The field experiment uncovers three empowering interventions which attenuate harmful effects of AIA anthropomorphism in relationships with consumers. With AI-powered technologies taking larger roles in our daily lives, our research highlights key future directions to investigate the permanent ongoing nature of the consumer–AI relationships.

Acceptability of artificial intelligence-based retina screening in general population.

Purpose:A deep learning system (DLS) using artificial intelligence (AI) is emerging as a very promising technology in the future of healthcare diagnostics. While the concept of telehealth is emerging in every field of medicine, AI assistance in diagnosis can become a great tool for successful screening in telemedicine and teleophthalmology. The aim of our study was to assess the acceptability of AI-based retina screening.Methods:This was a prospective non-randomized study performed in the outpatient department of a tertiary eye care hospital. Patients older than 18 years who came for a regular eye check-up or a routine retina screening were recruited in the study. Fundus images of the posterior pole were captured on fundus on a phone camera (REMIDIO™, India) with a built-in AI software (Netra.AI) that can identify normal versus abnormal retina. The patients were then given an 8-point questionnaire to assess their acceptance and willingness toward AI-based screening. We recruited 104 participants.Results:We found that 90.4% were willing for an AI-based fundus screening; 96.2% were satisfied with AI-based screening. Patients with diabetes (P = 0.03) and the male population (P = 0.029) were more satisfied with the AI-based screening. The majority (i.e., 97.1%) felt that AI-based screening gave them a better understanding of their eye condition and 37.5% felt that AI-based retina screening prior to a doctor’s visit can help in routine screeningConclusion:Considering the current COVID-19 pandemic situation across the globe, this study highlights the importance of AI-based telescreening and positive patient approach toward this technology.

An artificial intelligence system using maximum intensity projection MR images facilitates classification of non-mass enhancement breast lesions.

To build an artificial intelligence (AI) system to classify benign and malignant non-mass enhancement (NME) lesions using maximum intensity projection (MIP) of early post-contrast subtracted breast MR images. This retrospective study collected 965 pure NME lesions (539 benign and 426 malignant) confirmed by histopathology or follow-up in 903 women. The 754 NME lesions acquired by one MR scanner were randomly split into the training set, validation set, and test set A (482/121/151 lesions). The 211 NME lesions acquired by another MR scanner were used as test set B. The AI system was developed using ResNet-50 with the axial and sagittal MIP images. One senior and one junior radiologist reviewed the MIP images of each case independently and rated its Breast Imaging Reporting and Data System category. The performance of the AI system and the radiologists was evaluated using the area under the receiver operating characteristic curve (AUC). The AI system yielded AUCs of 0.859 and 0.816 in the test sets A and B, respectively. The AI system achieved comparable performance as the senior radiologist (p = 0.558, p = 0.041) and outperformed the junior radiologist (p < 0.001, p = 0.009) in both test sets A and B. After AI assistance, the AUC of the junior radiologist increased from 0.740 to 0.862 in test set A (p < 0.001) and from 0.732 to 0.843 in test set B (p < 0.001). Our MIP-based AI system yielded good applicability in classifying NME lesions in breast MRI and can assist the junior radiologist achieve better performance. • Our MIP-based AI system yielded good applicability in the dataset both from the same and a different MR scanner in predicting malignant NME lesions. • The AI system achieved comparable diagnostic performance with the senior radiologist and outperformed the junior radiologist. • This AI system can assist the junior radiologist achieve better performance in the classification of NME lesions in MRI.

Role of Artificial Intelligence Interpretation of Colposcopic Images in Cervical Cancer Screening.

The accuracy of colposcopic diagnosis depends on the skill and proficiency of physicians. This study evaluated the feasibility of interpreting colposcopic images with the assistance of artificial intelligence (AI) for the diagnosis of high-grade cervical intraepithelial lesions. This study included female patients who underwent colposcopy-guided biopsy in 2020 at two institutions in the Republic of Korea. Two experienced colposcopists reviewed all images separately. The Cerviray AI® system (AIDOT, Seoul, Korea) was used to interpret the cervical images. AI demonstrated improved sensitivity with comparable specificity and positive predictive value when compared with the colposcopic impressions of each clinician. The areas under the curve were greater with combined impressions (both AI and that of the two colposcopists) of high-grade lesions, when compared with the individual impressions of each colposcopist. This study highlights the feasibility of the application of an AI system in cervical cancer screening. AI interpretation can be utilized as an assisting tool in combination with human colposcopic evaluation of exocervix.

An artificial intelligence-based system assisted endoscopists to detect early gastric cancer: a case report

Background: The mucosal changes of early gastric cancers (EGC) are slight and difficult to be recognized, leading to a high miss rate. Artificial intelligence (AI) systems have the potential to improve the detection of EGC. Here, we report a case of early gastric cancer discovered by an endoscopist with the assistance of an AI system. Case presentation: A 67-year-old male patient came to our hospital for Esophagogastroduodenoscopy (EGD) due to a routine physical examination. He had previously been healthy but was treated for a Helicobacter pylori infection two years ago. In the process of EGD, the AI system flagged a tiny mucosal lesion that was far away and was not detected by the endoscopist, and this lesion attracted the endoscopist&amp;#039;s additional attention. After the close observation of the lesion, the AI system immediately gave a red prompt box, suggesting that the endoscopist further observe it. Under magnifying narrow-band imaging (ME-NBI), the mucosal glands and blood vessels of the lesion were irregular, and AI diagnosed it as a suspicious gastric carcinoma. Biopsy pathology showed that it was high-grade intraepithelial neoplasia, and after endoscopic mucosal dissection (ESD), post-ESD histology confirmed that the lesion was a highly differentiated adenocarcinoma confined to the mucosa, with a lesion range of 1.1cm × 1.0cm. The patient was discharged from the hospital without any postoperative complications. Conclusion: AI has been widely applied in the field of gastrointestinal endoscopy and has the potential to help improve the detection rate of early gastrointestinal cancers. We reported a case of early gastric cancer discovered by the endoscopist with the assistance of AI.

An artificial intelligence-based system assisted endoscopists to detect early gastric cancer: a case report

Background: The mucosal changes of early gastric cancers (EGC) are slight and difficult to be recognized, leading to a high miss rate. Artificial intelligence (AI) systems have the potential to improve the detection rate of EGC. Here, we reported a case of EGC discovered by an endoscopist with the assistance of an AI system. Case presentation: A 67-year-old male patient came to our hospital for Esophagogastroduodenoscopy (EGD) due to a routine physical examination. He had previously been healthy but was treated for a Helicobacter pylori infection two years ago. In the process of EGD, the AI system flagged a tiny mucosal lesion that was far away and was not detected by the endoscopist, and this lesion attracted the endoscopist's additional attention. After the close observation of the lesion, the AI system immediately gave a red prompt box, suggesting that the endoscopist further observe it. Under magnifying endoscopy with narrow-band imaging (ME-NBI), the mucosal glands and blood vessels of the lesion were found to be irregular, and this patient was diagnosed with suspicious gastric carcinoma by AI. Biopsy pathology showed that it was high-grade intraepithelial neoplasia, and after endoscopic mucosal dissection (ESD), post-ESD histology confirmed that the lesion was a highly differentiated adenocarcinoma confined to the mucosa, with a lesion range of 1.1 cm × 1.0 cm. The patient was discharged from the hospital without any postoperative complications. Conclusion: AI has been widely applied in the field of gastrointestinal endoscopy and has the potential to help improve the detection rate of early gastrointestinal cancers. We reported a case of early gastric cancer discovered by the endoscopist with the assistance of AI.

Artificial intelligence assistance in radiographic detection and classification of knee osteoarthritis and its severity: a cross-sectional diagnostic study.

Radiographic interpretation suffers from an ever-increasing workload in orthopedic and radiology departments. The present study applied and assessed the performance of a convolutional neural network designed to assist orthopedists and radiologists in the detection and classification of knee osteoarthritis from early to severe degrees in accordance with the Kellgren-Lawrence (KL) classification system. In total, 1650 knee joint radiographs (anteroposterior view) were collected from the Osteoarthritis Initiative public resource. Two models were developed: one distinguished normal (KL 0-I) from osteoarthritic knees (KL II-IV), and the other classified the severity as normal (KL 0-I), non-severe (KL II), or severe (KL III-IV). The regions of interest were labeled under the supervision of experts. Our artificial intelligence (AI) models were trained using the You Only Look Once version 3 (YOLOv3) detection algorithm. Our first AI model using YOLOv3 tiny could detect and classify normal and osteoarthritic knees on plain knee joint radiographs with 85% accuracy and 81% mean average precision. The second AI model for classifying severity achieved a total accuracy of 86.7% and mean average precision of 70.6%. Our proposed deep learning models provided high accuracy and satisfactory precision for the detection and classification of early to severe knee osteoarthritis on anteroposterior radiographs. These models may be used as diagnostic aids by interpreting knee radiographs and guiding the treatment options via each osteoarthritic stage for related physicians and specialists.

Smooth and Resilient Human–Machine Teamwork as an Industry 5.0 Design Challenge

Smart machine companions such as artificial intelligence (AI) assistants and collaborative robots are rapidly populating the factory floor. Future factory floor workers will work in teams that include both human co-workers and smart machine actors. The visions of Industry 5.0 describe sustainable, resilient, and human-centered future factories that will require smart and resilient capabilities both from next-generation manufacturing systems and human operators. What kinds of approaches can help design these kinds of resilient human–machine teams and collaborations within them? In this paper, we analyze this design challenge, and we propose basing the design on the joint cognitive systems approach. The established joint cognitive systems approach can be complemented with approaches that support human centricity in the early phases of design, as well as in the development of continuously co-evolving human–machine teams. We propose approaches to observing and analyzing the collaboration in human–machine teams, developing the concept of operations with relevant stakeholders, and including ethical aspects in the design and development. We base our work on the joint cognitive systems approach and propose complementary approaches and methods, namely: actor–network theory, the concept of operations and ethically aware design. We identify their possibilities and challenges in designing and developing smooth human–machine teams for Industry 5.0 manufacturing systems.

Artificial Intelligence-Assisted Bone Age Assessment to Improve the Accuracy and Consistency of Physicians With Different Levels of Experience.

BackgroundThe accuracy and consistency of bone age assessments (BAA) using standard methods can vary with physicians' level of experience.MethodsTo assess the impact of information from an artificial intelligence (AI) deep learning convolutional neural network (CNN) model on BAA, specialists with different levels of experience (junior, mid-level, and senior) assessed radiographs from 316 children aged 4–18 years that had been randomly divided into two equal sets-group A and group B. Bone age (BA) was assessed independently by each specialist without additional information (group A) and with information from the model (group B). With the mean assessment of four experts as the reference standard, mean absolute error (MAE), and intraclass correlation coefficient (ICC) were calculated to evaluate accuracy and consistency. Individual assessments of 13 bones (radius, ulna, and short bones) were also compared between group A and group B with the rank-sum test.ResultsThe accuracies of senior, mid-level, and junior physicians were significantly better (all P < 0.001) with AI assistance (MAEs 0.325, 0.344, and 0.370, respectively) than without AI assistance (MAEs 0.403, 0.469, and 0.755, respectively). Moreover, for senior, mid-level, and junior physicians, consistency was significantly higher (all P < 0.001) with AI assistance (ICCs 0.996, 0.996, and 0.992, respectively) than without AI assistance (ICCs 0.987, 0.989, and 0.941, respectively). For all levels of experience, accuracy with AI assistance was significantly better than accuracy without AI assistance for assessments of the first and fifth proximal phalanges.ConclusionsInformation from an AI model improves both the accuracy and the consistency of bone age assessments for physicians of all levels of experience. The first and fifth proximal phalanges are difficult to assess, and they should be paid more attention.

Radiological Analysis of COVID-19 Using Computational Intelligence: A Broad Gauge Study.

Pulmonary medical image analysis using image processing and deep learning approaches has made remarkable achievements in the diagnosis, prognosis, and severity check of lung diseases. The epidemic of COVID-19 brought out by the novel coronavirus has triggered a critical need for artificial intelligence assistance in diagnosing and controlling the disease to reduce its effects on people and global economies. This study aimed at identifying the various COVID-19 medical imaging analysis models proposed by different researchers and featured their merits and demerits. It gives a detailed discussion on the existing COVID-19 detection methodologies (diagnosis, prognosis, and severity/risk detection) and the challenges encountered for the same. It also highlights the various preprocessing and post-processing methods involved to enhance the detection mechanism. This work also tries to bring out the different unexplored research areas that are available for medical image analysis and how the vast research done for COVID-19 can advance the field. Despite deep learning methods presenting high levels of efficiency, some limitations have been briefly described in the study. Hence, this review can help understand the utilization and pros and cons of deep learning in analyzing medical images.

Decoding the agility of artificial intelligence-assisted human design teams

Although necessary for complex problem solving, such as engineering design, team agility is often difficult to achieve in practice. The evolution of Artificial Intelligence (AI) affords unique opportunities for supporting team problem solving. While integrating assistive AI agents into human teams has at times improved team performance, it is still unclear if, how, and why AI affects team agility. A large-scale human experiment answers these questions, revealing that, with appropriately interfaced AIs, AI-assisted human teams enjoy improved coordination and communications, leading to better performance and adaptations to team disruptions, while devoting more effort to information handling and exploring the solution space more broadly. In sum, working with AI enables human team members to think more and act less. • Assistive AI boosts team agility with respect to both evolving and abrupt changes. • The AI-assisted teams outperform the human-only teams. • The AI assistance improves team action effectiveness and behavioral flexibility. • The AI assistance results in more information-intensive team coordination. • The AI-assisted teams explore the drone design and operations spaces more broadly.

Teacher Artificial Intelligence-Supported Pedagogical Actions in Collaborative Learning Coregulation: A Wizard-of-Oz Study

Orchestrating collaborative learning (CL) is difficult for teachers as it involves being aware of multiple simultaneous classroom events and intervening when needed. Artificial intelligence (AI) technology might support the teachers’ pedagogical actions during CL by helping detect students in need and providing suggestions for intervention. This would be resulting in AI and teacher co-orchestrating CL; the effectiveness of which, however, is still in question. This study explores whether having an AI assistant helping the teacher in orchestrating a CL classroom is understandable for the teacher and if it affects the teachers’ pedagogical actions, understanding and strategies of coregulation. Twenty in-service teachers were interviewed using a Wizard-of-Oz protocol. Teachers were asked to identify problems during the CL of groups of students (shown as videos), proposed how they would intervene, and later received (and evaluated) the pedagogical actions suggested by an AI assistant. Our mixed-methods analysis showed that the teachers found the AI assistant useful. Moreover, in multiple cases the teachers started employing the pedagogical actions the AI assistant had introduced to them. Furthermore, an increased number of coregulation methods were employed. Our analysis also explores the extent to which teachers’ expertise is associated with their understanding of coregulation, e.g., less experienced teachers did not see coregulation as part of a teacher’s responsibility, while more experienced teachers did.

Abstract P4-05-07: Assistance with an artificial intelligence-powered tumor infiltrating lymphocytes (TIL) analyzer reduces interobserver variation in pathologic scoring of TIL in breast cancer

Abstract Background Tumor infiltrating lymphocytes (TIL) is a promising prognostic marker in breast cancer. However, TIL is manually scored by pathologists, thus laborious work is required and interobserver heterogeneity exists in the results. In this study, we aimed to evaluate the clinical utility of an artificial intelligence (AI)-powered TIL analyzer in terms of reducing the interobserver variation. Methods Lunit SCOPE IO, AI-powered TIL analyzer was trained and validated with a 2.8 x 109 micrometer2 area and 5.9 x 106 TIL from 3,166 H&amp;E Whole-Slide Images (WSI) of multiple cancer types including breast cancer, annotated by 52 board-certified pathologists. Three independent board-certified pathologists scored TIL% of H&amp;E slides of breast cancer from an external cohort (N = 199). TIL% was calculated referenced on the guideline of Immuno-Oncology Biomarker Working Group on Breast Cancer. For the cases of TIL score difference between each pathologist and AI model more than 15%, the pathologists were asked to revise TIL% in assistance with AI model which displays both stromal area and TIL. Finally, we compared the interobserver variation based on intraclass correlation coefficients (ICC) before and after AI assistance. Results The distribution of TIL score by 3 pathologists was 7% (5-20%), 15% (5-50%), and 20% (10-40%), respectively [median (25%-75% quantile)]. The ICC value of the initial TIL score evaluation was 0.716 (95% confidence interval, 0.560-0.811). Afterward, pathologists revised their initial scoring with assistance of AI model for the cases of difference more than 15% (n = 19, 72, and 73, respectively for each pathologist). After rescoring, number of slides with 15% or more difference of TIL% between raters significantly decreased from 109 slides (54.8%) to 75 slides (37.7%, p &amp;lt; 0.001). The ICC value after re-scoring TIL% was 0.831 (95% confidence interval, 0.725-0.890). Conclusions There was a notable interobserver variation to score TIL% in breast cancer. Assistance with AI-powered TIL analyzer substantially improved the pathologist’s consensus and could be regarded as one of references for the final labeling of TIL%. Citation Format: Soo Ick Cho, Wonkyung Jung, Sangjoon Choi, Seokhwi Kim, Sanghoon Song, Gahee Park, Minuk Ma, Seonwook Park, Sergio Pereira, Sangheon Ahn, Brian Jaehong Aum, Seunghwan Shin, Kyunghyun Paeng, Donggeun Yoo, Chan-Young Ock. Assistance with an artificial intelligence-powered tumor infiltrating lymphocytes (TIL) analyzer reduces interobserver variation in pathologic scoring of TIL in breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-05-07.

The effects of domain knowledge on trust in explainable AI and task performance: A case of peer-to-peer lending

Increasingly, artificial intelligence (AI) is being used to assist complex decision-making such as financial investing. However, there are concerns regarding the black-box nature of AI algorithms. The field of explainable AI (XAI) has emerged to address these concerns. XAI techniques can reveal how an AI decision is formed and can be used to understand and appropriately trust an AI system. However, XAI techniques still may not be human-centred and may not support human decision-making adequately. In this work, we explored how domain knowledge, identified by expert decision makers, can be used to achieve a more human-centred approach to AI. We measured the effect of domain knowledge on trust in AI, reliance on AI, and task performance in an AI-assisted complex decision-making environment. In a peer-to-peer lending simulator, non-expert participants made financial investments using an AI assistant. The presence or absence of domain knowledge was manipulated. The results showed that participants who had access to domain knowledge relied less on the AI assistant when the AI assistant was incorrect and indicated less trust in AI assistant. However, overall investing performance was not affected. These results suggest that providing domain knowledge can influence how non-expert users use AI and could be a powerful tool to help these users develop appropriate levels of trust and reliance.

Artificial intelligence and climate change: ethical issues

PurposeThe purpose of this paper is to pinpoint and analyse ethical issues raised by the dual role of artificial intelligence (AI) in relation to climate change, that is, AI as a contributor to climate change and AI as a contributor to fighting climate change.Design/methodology/approachThis paper consists of three main parts. The first part provides a short background on AI and climate change respectively, followed by a presentation of empirical findings on the contribution of AI to climate change. The second part presents proposals by various AI researchers and commentators on how AI companies may contribute to fighting climate change by reducing greenhouse gas emissions from training and use of AI and by providing AI assistance to various mitigation and adaptation measures. The final part investigates ethical issues raised by some of the options presented in the second part.FindingsAI applications may lead to substantial emissions but may also play an important role in mitigation and adaptation. Given this dual role of AI, ethical considerations by AI companies and governments are of vital importance.Practical implicationsThis paper pinpoints practical ethical issues that AI companies and governments should take into account.Social implicationsGiven the potential impact of AI on society, it is vital that AI companies and governments take seriously the ethical issues raised by the dual role of AI in relation to climate change.Originality/valueAI has been the subject of substantial ethical investigation, and even more so has climate change. However, the relationship between AI and climate change has received only limited attention from an ethical perspective. This paper provides such considerations.

Race- and Ethnicity-Stratified Analysis of an Artificial Intelligence–Based Tool for Skin Condition Diagnosis by Primary Care Physicians and Nurse Practitioners

Background Many dermatologic cases are first evaluated by primary care physicians or nurse practitioners. Objective This study aimed to evaluate an artificial intelligence (AI)-based tool that assists with interpreting dermatologic conditions. Methods We developed an AI-based tool and conducted a randomized multi-reader, multi-case study (20 primary care physicians, 20 nurse practitioners, and 1047 retrospective teledermatology cases) to evaluate its utility. Cases were enriched and comprised 120 skin conditions. Readers were recruited to optimize for geographical diversity; the primary care physicians practiced across 12 states (2-32 years of experience, mean 11.3 years), and the nurse practitioners practiced across 9 states (2-34 years of experience, mean 13.1 years). To avoid memory effects from incomplete washout, each case was read once by each clinician either with or without AI assistance, with the assignment randomized. The primary analyses evaluated the top-1 agreement, defined as the agreement rate of the clinicians’ primary diagnosis with the reference diagnoses provided by a panel of dermatologists (per case: 3 dermatologists from a pool of 12, practicing across 8 states, with 5-13 years of experience, mean 7.2 years of experience). We additionally conducted subgroup analyses stratified by cases’ self-reported race and ethnicity and measured the performance spread: the maximum performance subtracted by the minimum across subgroups. Results The AI’s standalone top-1 agreement was 63%, and AI assistance was significantly associated with higher agreement with reference diagnoses. For primary care physicians, the increase in diagnostic agreement was 10% (P&lt;.001), from 48% to 58%; for nurse practitioners, the increase was 12% (P&lt;.001), from 46% to 58%. When stratified by cases’ self-reported race or ethnicity, the AI’s performance was 59%-62% for Asian, Native Hawaiian, Pacific Islander, other, and Hispanic or Latinx individuals and 67% for both Black or African American and White subgroups. For the clinicians, AI assistance–associated improvements across subgroups were in the range of 8%-12% for primary care physicians and 8%-15% for nurse practitioners. The performance spread across subgroups was 5.3% unassisted vs 6.6% assisted for primary care physicians and 5.2% unassisted vs 6.0% assisted for nurse practitioners. In both unassisted and AI-assisted modalities, and for both primary care physicians and nurse practitioners, the subgroup with the highest performance on average was Black or African American individuals, though the differences with other subgroups were small and had overlapping 95% CIs. Conclusions AI assistance was associated with significantly improved diagnostic agreement with dermatologists. Across race and ethnicity subgroups, for both primary care physicians and nurse practitioners, the effect of AI assistance remained high at 8%-15%, and the performance spread was similar at 5%-7%. Acknowledgments This work was funded by Google LLC. Conflicts of Interest AJ, DW, VG, YG, GOM, JH, RS, CE, KN, KBD, GSC, LP, DRW, RCD, DC, Yun Liu, PB, and Yuan Liu are/were employees at Google and own Alphabet stocks.

Augmenting the accuracy of trainee doctors in diagnosing skin lesions suspected of skin neoplasms in a real-world setting: A prospective controlled before-and-after study.

BackgroundAlthough deep neural networks have shown promising results in the diagnosis of skin cancer, a prospective evaluation in a real-world setting could confirm these results. This study aimed to evaluate whether an algorithm (http://b2019.modelderm.com) improves the accuracy of nondermatologists in diagnosing skin neoplasms.MethodsA total of 285 cases (random series) with skin neoplasms suspected of malignancy by either physicians or patients were recruited in two tertiary care centers located in South Korea. An artificial intelligence (AI) group (144 cases, mean [SD] age, 57.0 [17.7] years; 62 [43.1%] men) was diagnosed via routine examination with photographic review and assistance by the algorithm, whereas the control group (141 cases, mean [SD] age, 61.0 [15.3] years; 52 [36.9%] men) was diagnosed only via routine examination with a photographic review. The accuracy of the nondermatologists before and after the interventions was compared.ResultsAmong the AI group, the accuracy of the first impression (Top-1 accuracy; 58.3%) after the assistance of AI was higher than that before the assistance (46.5%, P = .008). The number of differential diagnoses of the participants increased from 1.9 ± 0.5 to 2.2 ± 0.6 after the assistance (P < .001). In the control group, the difference in the Top-1 accuracy between before and after reviewing photographs was not significant (before, 46.1%; after, 51.8%; P = .19), and the number of differential diagnoses did not significantly increase (before, 2.0 ± 0.4; after, 2.1 ± 0.5; P = .57).ConclusionsIn real-world settings, AI augmented the diagnostic accuracy of trainee doctors. The limitation of this study is that the algorithm was tested only for Asians recruited from a single region. Additional international randomized controlled trials involving various ethnicities are required.

Artificial intelligence assistance improves the accuracy and efficiency of intracranial aneurysm detection with CT angiography

PurposeThe aim of this study was to evaluate whether a novel head and neck artificial intelligence (AI)-assisted diagnostic system based on a three-dimensional convolutional neural network (3D-CNN) could improve the accuracy, efficiency and working mode of intracranial aneurysm (IA) detection. MethodsA total of 212 patients who underwent computed tomography angiography (CTA) and digital subtraction angiography (DSA) were retrospectively included. We used three diagnostic modes to detect IAs with CTA: AI, physicians and AI + physicians. Taking the diagnostic results of DSA as the gold standard, the sensitivity, specificity, accuracy, mean reporting time, and interobserver consistency of the three diagnostic modes were calculated and compared at the patient and lesion levels. ResultsOf 212 patients, 179 were diagnosed with IAs by DSA, and 224 IAs were analyzed. The sensitivity, specificity and accuracy of the AI system in diagnosing aneurysms were 84.9% (95% confidence interval [CI], 78.9–89.5%), 18.2% (95% CI, 8.2–34.8%) and 74.5% (95% CI, 68.3–80.0%) at the patient-level, and 77.2% (95% CI, 71.3–82.3%), 14.0% (95% CI, 6.2–27.6%) and 67.0% (95% CI, 61.2–72.4%) at the lesion-level, respectively. With AI assistance, junior physicians had the similar diagnostic performance as senior physicians at the patient (sensitivity 95.0% vs. 95.0%, specificity 48.5% vs. 57.6%, accuracy 87.7% vs. 89.2%, p = 0.690) and lesion levels (sensitivity 88.0% vs. 89.7%, specificity 32.0% vs. 38.0%, accuracy 77.8% vs. 80.3%, p = 1.000), especially for aneurysms < 5 mm (sensitivity 83.2% vs. 87.6%, specificity 60.0% vs. 63.2%, accuracy 75.4% vs. 78.9%, p = 0.424). The reporting efficiency of junior and senior physicians improved by 20.7% (141.1 ± 52.6 s to 111.9 ± 46.3 s, p = 0.004) and 18.8% (113.2 ± 42.5 s to 91.9 ± 41.2 s, p = 0.011), respectively. ConclusionsThis 3D-CNN-based AI system significantly improved the accuracy and efficiency of physician detection of IA. The AI + physicians work mode could have a major influence on daily clinical practice and clinical research.

High strength aluminum alloys design via explainable artificial intelligence

Here, we have approached to discover new aluminum (Al) alloys with the assistance of artificial intelligence (A.I.) for the enhanced mechanical property. A high prediction rate of 7xxx series Al alloy was achieved via the Bayesian hyperparameter optimization algorithm. With the guide of A.I.-based recommendation algorithm, new Al alloys were designed that had an excellent combination of strength and ductility with a yield strength (YS) of 712 MPa and elongation (EL) of 19%, exhibiting a homogeneous distribution of nanoscale precipitates hindering dislocation movement during deformation. Adding Mg and Cu was found to be the critical factor that decides the relative ratio of strength and EL. We also demonstrate an explainable A.I. (XAI) system that reveals the relationship between input and output parameters. Our A.I. assistant system can accelerate the search for high-strength Al alloys for both experts and non-experts in the field of Al alloy design.

Deep Learning-based Artificial Intelligence Improves Accuracy of Error-prone Lung Nodules.

Introduction: Early detection of lung cancer is one way to improve outcomes. Improving the detection of nodules on chest CT scans is important. Previous artificial intelligence (AI) modules show rapid advantages, which improves the performance of detecting lung nodules in some datasets. However, they have a high false-positive (FP) rate. Its effectiveness in clinical practice has not yet been fully proven. We aimed to use AI assistance in CT scans to decrease FP.Materials and methods: CT images of 60 patients were obtained. Five senior doctors who were blinded to these cases participated in this study for the detection of lung nodules. Two doctors performed manual detection and labeling of lung nodules without AI assistance. Another three doctors used AI assistance to detect and label lung nodules before manual interpretation. The AI program is based on a deep learning framework.Results: In total, 266 nodules were identified. For doctors without AI assistance, the FP was 0.617-0.650/scan and the sensitivity was 59.2-67.0%. For doctors with AI assistance, the FP was 0.067 to 0.2/scan and the sensitivity was 59.2-77.3% This AI-assisted program significantly reduced FP. The error-prone characteristics of lung nodules were central locations, ground-glass appearances, and small sizes. The AI-assisted program improved the detection of error-prone nodules.Conclusions: Detection of lung nodules is important for lung cancer treatment. When facing a large number of CT scans, error-prone nodules are a great challenge for doctors. The AI-assisted program improved the performance of detecting lung nodules, especially for error-prone nodules.