Artificial Intelligence, Real-World Automation and the Safety of Medicines

Bate, Andrew; Hobbiger, Steve F.

doi:10.1007/s40264-020-01001-7

Cited by 31 publications

(35 citation statements)

References 66 publications

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“…Aspects that have not yet been clarified, such as changes in ML-related software over time due to changing datasets, should be of particular interest. In the literature, suggestions are increasingly being submitted and discussed [ 30 , 72 ], both on general regulatory aspects [ 29 , 73 , 74 ] and on device- or subject-specific features, e.g., in view of medical imaging [ 75 , 76 ].…”

Section: Discussionmentioning

confidence: 99%

Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov

Zippel

Bohnet-Joschko

2021

IJERPH

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Although advances in machine-learning healthcare applications promise great potential for innovative medical care, few data are available on the translational status of these new technologies. We aimed to provide a comprehensive characterization of the development and status quo of clinical studies in the field of machine learning. For this purpose, we performed a registry-based analysis of machine-learning-related studies that were published and first available in the ClinicalTrials.gov database until 2020, using the database’s study classification. In total, n = 358 eligible studies could be included in the analysis. Of these, 82% were initiated by academic institutions/university (hospitals) and 18% by industry sponsors. A total of 96% were national and 4% international. About half of the studies (47%) had at least one recruiting location in a country in North America, followed by Europe (37%) and Asia (15%). Most of the studies reported were initiated in the medical field of imaging (12%), followed by cardiology, psychiatry, anesthesia/intensive care medicine (all 11%) and neurology (10%). Although the majority of the clinical studies were still initiated in an academic research context, the first industry-financed projects on machine-learning-based algorithms are becoming visible. The number of clinical studies with machine-learning-related applications and the variety of medical challenges addressed serve to indicate their increasing importance in future clinical care. Finally, they also set a time frame for the adjustment of medical device-related regulation and governance.

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Section: Discussionmentioning

confidence: 99%

Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov

Zippel

Bohnet-Joschko

2021

IJERPH

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“…The ability to link at scale huge numbers of different apps/devices has led to the emergence of various data streams with potential direct or indirect value for safety surveillance. Such examples are electrocardiogram signals from mobile heart monitoring, X-ray images, video and audio data [39,43], and digital biomarkers in clinical trial settings [44]. To date, there is limited research on the impact of sensor data on safety surveillance; however, some studies have described proof-of-concept work for aggregating multiple data streams, including from personal digital devices [45].…”

Section: Other Sourcesmentioning

confidence: 99%

“…Translating scientific research on automation of manual processing into actual improved capability for AE report management has taken some time [43]. More routine automation in the management of ICSRs has been seen in the past few years, with a slowly increasing number of publications on robotic process automation, and their impact on routine use [57,58].…”

Section: Agency Mah Mahmentioning

confidence: 99%

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Safety of medicines and vaccines – building next generation capability

Bate

Stegmann

2021

Trends in Pharmacological Sciences

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“…This can be done by an AI method called natural language processing (NLP). Likewise, optical character recognition is used to read handwritten documents and AI-based speech recognition systems are used to read the information reported over the call [5].…”

Section: Aimentioning

confidence: 99%

Aspects of Utilization and Limitations of Artificial Intelligence in Drug Safety

Sujith

Chakradhar

Marpaka

et al. 2021

Asian J Pharm Clin Res

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Previously, it was thought that computers cannot perform the works on its own and need the human intelligence but now it is possible with the help of artificial intelligence (AI). AI has the potential to impact nearly every aspect of medical science. As pharmacovigilance (PV) deals with data concerning drug safety, it is being considered the field to be enormously transforming in near future with the emergence of AI. This article explores and gives an overall review of the researches done to implement AI technologies in PV activities. Among many of the PV activities, case processing is the most resource-consuming area, and signal detection is considered to be a poorly functioning area due to various limitations. Introducing AI will potentially fulfill the limitations in these areas and help us to use the resources in a focused way to get the real-world risk-benefit ratio for a better understanding of the safety profile of drugs and to take timely action for the well-being of people.

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Artificial Intelligence, Real-World Automation and the Safety of Medicines

Cited by 31 publications

References 66 publications

Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov

Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov

Safety of medicines and vaccines – building next generation capability

Aspects of Utilization and Limitations of Artificial Intelligence in Drug Safety

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