scholarly journals Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations

2021 ◽  
Author(s):  
Sarah E. Hickman ◽  
Gabrielle C. Baxter ◽  
Fiona J. Gilbert
Keyword(s):  
Artificial Intelligence ◽  
Patient Outcomes ◽  
Breast Imaging ◽  
Breast Screening ◽  
Retrospective Studies ◽  
Healthcare Systems ◽  
Imaging Evaluation ◽  
Before And After ◽  
Workflow Efficiency ◽  
Regulatory Challenges

AbstractRetrospective studies have shown artificial intelligence (AI) algorithms can match as well as enhance radiologist’s performance in breast screening. These tools can facilitate tasks not feasible by humans such as the automatic triage of patients and prediction of treatment outcomes. Breast imaging faces growing pressure with the exponential growth in imaging requests and a predicted reduced workforce to provide reports. Solutions to alleviate these pressures are being sought with an increasing interest in the adoption of AI to improve workflow efficiency as well as patient outcomes. Vast quantities of data are needed to test and monitor AI algorithms before and after their incorporation into healthcare systems. Availability of data is currently limited, although strategies are being devised to harness the data that already exists within healthcare institutions. Challenges that underpin the realisation of AI into everyday breast imaging cannot be underestimated and the provision of guidance from national agencies to tackle these challenges, taking into account views from a societal, industrial and healthcare prospective is essential. This review provides background on the evaluation and use of AI in breast imaging in addition to exploring key ethical, technical, legal and regulatory challenges that have been identified so far.

scholarly journals Group Agency and Artificial Intelligence

2021 ◽  
Author(s):  
Christian List
Keyword(s):  
Artificial Intelligence ◽  
Moral Status ◽  
Intelligent Systems ◽  
Group Agency ◽  
High Stakes ◽  
Moral Agents ◽  
Legal Personhood ◽  
The Social ◽  
Intentional Agents ◽  
Regulatory Challenges

AbstractThe aim of this exploratory paper is to review an under-appreciated parallel between group agency and artificial intelligence. As both phenomena involve non-human goal-directed agents that can make a difference to the social world, they raise some similar moral and regulatory challenges, which require us to rethink some of our anthropocentric moral assumptions. Are humans always responsible for those entities’ actions, or could the entities bear responsibility themselves? Could the entities engage in normative reasoning? Could they even have rights and a moral status? I will tentatively defend the (increasingly widely held) view that, under certain conditions, artificial intelligent systems, like corporate entities, might qualify as responsible moral agents and as holders of limited rights and legal personhood. I will further suggest that regulators should permit the use of autonomous artificial systems in high-stakes settings only if they are engineered to function as moral (not just intentional) agents and/or there is some liability-transfer arrangement in place. I will finally raise the possibility that if artificial systems ever became phenomenally conscious, there might be a case for extending a stronger moral status to them, but argue that, as of now, this remains very hypothetical.

scholarly journals A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Color Space Conversion ◽  
Before And After ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Algorithm ◽  
Time Image ◽  
Cooking Conditions

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

A Critical Assessment and Review of Artificial Intelligence in Facial Paralysis Analysis: Uncovering the Truth

FACE ◽  
2021 ◽  
pp. 273250162110228
Author(s):  
David T. Mitchell ◽  
David Z. Allen ◽  
Matthew R. Greives ◽  
Phuong D. Nguyen
Keyword(s):  
Artificial Intelligence ◽  
Facial Paralysis ◽  
Critical Assessment ◽  
Facial Emotion ◽  
Data Systems ◽  
Computer Algorithms ◽  
Future Directions ◽  
Sample Data ◽  
Before And After ◽  
Facial Reanimation

Machine learning is a rapidly growing subset of artificial intelligence (AI) which involves computer algorithms that automatically build mathematical models based on sample data. Systems can be taught to learn from patterns in existing data in order to make similar conclusions from new data. The use of AI in facial emotion recognition (FER) has become an area of increasing interest for providers who wish to quantify facial emotion before and after interventions such as facial reanimation surgery. While FER deep learning algorithms are less subjective when compared to layperson assessments, the databases used to train them can greatly alter their outputs. There are currently many well-established modalities for assessing facial paralysis, but there is also increasing interest in a more objective and universal measurement system to allow for consistent assessments between practitioners. The purpose of this article is to review the development of AI, examine its existing uses in facial paralysis assessment, and discuss the future directions of its implications.

AIRA, an Artificial Intelligence Realistic Assistant that will save your time, a before-and-after study during Pandemic (Preprint)

2021 ◽  
Author(s):  
Kaio Bin ◽  
Adler Araújo Ribeiro Melo ◽  
José Guilherme Franco Da Rocha ◽  
Renata Pivi De Almeida ◽  
Vilson Cobello Junior ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Record ◽  
Medical Care ◽  
Health Screening ◽  
Urgent Care ◽  
Health Record ◽  
Before And After ◽  
Electronic Health ◽  
The Impact ◽  
Before And After Study

BACKGROUND AIRA is an AI designed to reduce the time that doctors dedicate filling out EHR, winner of the first edition of MIT Hacking Medicine held in Brazil in 2020. As a proof of concept, AIRA was implemented in administrative process before its application in a medical process. OBJECTIVE The aim of the study is to determinate the impact of AIRA by eliminating the Medical Care Registration (MCR) on Electronic Health Record (EHR) by Administrative Officer. METHODS This is a comparative before-and-after study following the guidance “Evaluating digital health products” from Public Health England. An Artificial Intelligence named AIRA was created and implemented at CEAC (Employee Attention Center) from HCFMUSP. A total of 25,507 attendances were evaluated along 2020 for determinate AIRA´s impact. Total of MCR, time of health screening and time between the end of the screening and the beginning of medical care, were compared in the pre and post AIRA periods. RESULTS AIRA eliminated the need for Medical Care Registration by Administrative Officer in 92% (p<0.0001). The nurse´s time of health screening increased 16% (p<0.0001) during the implementation, and 13% (p<0.0001) until three months after the implementation, but reduced in 4% three months after implementation (p<0.0001). The mean and median total time to Medical Care after the nurse’ Screening was decreased in 30% (p<0.0001) and 41% (p<0.0001) respectively. CONCLUSIONS The implementation of AIRA reduced the time to medical care in an urgent care after the nurse´ screening, by eliminating non-value-added activity the Medical Care Registration on Electronic Health Record (EHR) by Administrative Officer.

scholarly journals Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sihua Niu ◽  
Jianhua Huang ◽  
Jia Li ◽  
Xueling Liu ◽  
Dan Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Breast Imaging ◽  
Texture Features ◽  
Matrix Analysis ◽  
Width Ratio ◽  
Malignant Group ◽  
Malignant Lesions ◽  
Occurrence Matrix ◽  
Benign Group

Abstract Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

scholarly journals Lean management in health care: effects on patient outcomes, professional practice, and healthcare systems

2020 ◽  
Author(s):  
Thomas Rotter ◽  
Christopher Plishka ◽  
Adegboyega Lawal ◽  
Michelle Fiander ◽  
Elizabeth L Harrison ◽  
...  
Keyword(s):  
Health Care ◽  
Patient Outcomes ◽  
Professional Practice ◽  
Healthcare Systems ◽  
Lean Management

scholarly journals An Overview on Patient-Centered Clinical Services

2021 ◽  
Vol 4 (2) ◽  
pp. 157-170
Author(s):  
Gaurav Joshi ◽  
Atul Kabra ◽  
Nishant Goutam ◽  
Alka Sharma
Keyword(s):  
Patient Outcomes ◽  
Clinical Pharmacist ◽  
Healthcare Systems ◽  
Clinical Services ◽  
Drug Related Problems ◽  
Patient Centered ◽  
Health Quality ◽  
Health Quality Of Life ◽  
The One

Drug-related problems (DRPs) had often been a concern in the system that needed to be detected, avoided, and addressed as soon as possible. The need for a clinical pharmacist becomes even more important. He is the one who can not only share the load but also be an important part of the system by providing required advice. They fill out the patient's pharmacotherapy reporting form and notify the medical team's head off any drug-related issues. General practitioners register severe adverse drug reactions (ADRs) yearly. As a result of all of this, a clinical pharmacist working in and around the healthcare system is expected to advance the pharmacy industry. Its therapy and drugs can improve one's health quality of life by curing, preventing, or diagnosing a disease, sign, or symptom. The sideshows, on the other hand, do much harm. Because of the services they offer, clinical pharmacy has grown in popularity. To determine the overall effect and benefits of the emergency department (ED) clinical pharmacist, a systematic review of clinical practice and patient outcomes will be needed. A clinical pharmacist's anatomy, toxicology, pharmacology, and medicinal chemistry expertise significantly improves a patient's therapy enforcement. It is now important to examine the failure points of healthcare systems as well as the individuals involved.

scholarly journals The Effect of Artificial Intelligence (Microbit) in Raising the Motivation Towards Learning Programming Among the Students of Educational Technology at King Abdulaziz University in Jeddah: أثر الذكاء الاصطناعي المايكروبت (Microbit) في رفع الدافعية نحو تعلُّم البرمجة لدى الطالبات في مقرر تقنيات التعليم بجامعة الملك عبد العزيز بجدة

2021 ◽  
Vol 5 (30) ◽  
pp. 58-76
Author(s):  
Samaher Ahmed Al-Qarni, Amani Mohammed Omran Samaher Ahmed Al-Qarni, Amani Mohammed Omran
Keyword(s):  
Artificial Intelligence ◽  
Educational Technology ◽  
Educational Institutions ◽  
Artificial Intelligence Techniques ◽  
Before And After ◽  
Quasi Experimental ◽  
King Abdulaziz University ◽  
Effective Role ◽  
Level Of Significance ◽  
Learning Programming

The research seeks to know the effect of Artificial Intelligence (Microbit) in raising the motivation towards learning programming among the students of educational technology at King Abdulaziz University in Jeddah. The sample consisted of (14) students, and the research followed the quasi-experimental method for one experimental group. Also, a pre-measurement and post-measurement was done by using the motivation measure towards learning programming. The results of the research confirmed that there were statistically significant differences at the level of significance (0.001) between the results of students for their motivation towards learning programming before and after the use of the (Microbit) in favor of post-measurement. The research also recommended the importance of employing artificial intelligence techniques in curricula and academic projects for its effective role in making the education process active, improving the performance of male and female students and raising their motivation. As well as, preparing educational institutions and centers, and training teachers to work using artificial intelligence techniques, especially the Microbit device.

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