Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic.

A survey on deep learning in medicine: Why, how and when?

Artificial intelligence and computational pathology.

Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird's-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.

A bird's-eye view of deep learning in bioimage analysis.

Background: Artificial intelligence (AI) has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these findings into the clinic is the logical next step. This translation can only be successful if patients' concerns and questions are addressed suitably. We therefore conducted a survey to evaluate the patients' view of artificial intelligence in melanoma diagnostics in Germany, with a particular focus on patients with a history of melanoma. Participants and Methods: A web-based questionnaire was designed using LimeSurvey, sent by e-mail to university hospitals and melanoma support groups and advertised on social media. The anonymous questionnaire evaluated patients' expectations and concerns toward artificial intelligence in general as well as their attitudes toward different application scenarios. Descriptive analysis was performed with expression of categorical variables as percentages and 95% confidence intervals. Statistical tests were performed to investigate associations between sociodemographic data and selected items of the questionnaire. Results: 298 individuals (154 with a melanoma diagnosis, 143 without) responded to the questionnaire. About 94% [95% CI = 0.91-0.97] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.85-0.92] would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% [95% CI = 0.35-0.46] of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% [95% CI = 0.92-0.97] to its use as assistance system for physicians. In sub-group analyses, only minor differences were detectable. Respondents with a previous history of melanoma were more amenable to the use of AI applications for early detection even at home. They would prefer an application scenario where physician and AI classify the lesions independently. With respect to AI-based applications in medicine, patients were concerned about insufficient data protection, impersonality and susceptibility to errors, but expected faster, more precise and unbiased diagnostics, less diagnostic errors and support for physicians. Conclusions: The vast majority of participants exhibited a positive attitude toward the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.

Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective.

Artificial Intelligence in Medicine: Where Are We Now?

Artificial Intelligence, Radiology, and Tuberculosis: A Review.

OBJECTIVE. At its advent, CT was too slow to image the heart. Temporal resolution improved with electron beam CT (EBCT); subsequently, the heart could be imaged, eventually leading to the discovery of prognostic information obtained from the coronary calcium score. In the early 2000s, EBCT was replaced by MDCT. In this review, we discuss the rise and fall of EBCT and explore its legacy in cardiac imaging. CONCLUSION. Although MDCT rendered EBCT obsolete, EBCT leaves a legacy in cardiac imaging regarding both diagnosis and prognosis. The creators of MDCT emulated the strengths of EBCT and learned from its weaknesses. Moreover, EBCT showed that imaging surrogates can predict outcomes, and the origins of substrate-guided treatment can be traced to EBCT.

Sagar Kulkarni

Papers

Artificial Intelligence in Medicine: Where Are We Now?

Artificial Intelligence, Radiology, and Tuberculosis: A Review.

Electron Beam CT: A Historical Review

Preoperative Computed Tomography in the Adult Cardiac Surgery Patient.