AI News, Combining Diagnostic Imaging and Pathology for Improving ... artificial intelligence

Artificial intelligence and machine learning in clinical development: a translational perspective

FDA’s current strategic policy places emphasis on leveraging innovation, advancing digital health technologies, and developing next-generation analytical approaches to improve health care, broaden access, and advance public health goals.

For example, FDA has approved diagnostics company IDx’s ML-based software system for autonomous detection of diabetic retinopathy.26 In addition, Viz.ai’s software, which uses a ML techniques to scan Computed Tomography images for indicators associated with stroke, also obtained the regulatory approval.27 Other software systems listed included automated detection of atrial fibrillation and coronary calcification scores.28,29 The FDA is also considering the ability of AI/ML-based SaMD for continuously learning and adaptive algorithms that have the potential to adapt and optimize device performance in real time to improve health care for patients in its regulatory framework.

Discussions also focused on RWD-based simulations are now accepted as a reasonable way to inform clinical study design, modeling the impact of different study eligibility criteria, the timing of endpoint assessments, and study timelines at the FDA.31 Treatment and regulatory decisions are based largely on data obtained from clinical trials and observational real-world data, and evidence from hospitals, EHR, and primary care are considered ancillary.

By using RWD and tracking patients longitudinally via EHR for example, necessity for certain traditionally conducted late-phase trials could be reduced or eliminated altogether: one would provide a drug (after it has proven safe and efficacious in phase I and II trials) to patients and keep track of their experience.

This perspective aims to engage and inform researchers from fields, such as computer science, biology, medicine, engineering, biostatistics, and policy makers, with value of emerging technologies of AI and ML in solving key challenges facing modernization of the current clinical development process.

Schwarzman College of Computing have been launched to support machine-learning research for health care needs.35,36 Partnership in AI-assisted care at Stanford, Center for Artificial Intelligence in Diagnostic Medicine at University of California, Irvine and Center for Clinical Data Sciences at Massachusetts General Hospital and Brigham and Womens Hospital have joined the ecosystem.37,38,39 A number of key recommendations and successful use cases and value and challenges facing AI and ML adoption in clinical development outlined in this perspective (summarized in Box I) can thus be implemented for taking advantage of digital algorithmic evidence to improve medical care for patients.

Collaboration with the National Imaging Academy Wales to develop leading-edge AI-based ultrasound tools for Diagnostic Imaging - Intelligent Ultrasound

Intelligent Ultrasound Group plc is entering into a collaboration with the National Imaging Academy Wales (NIAW) to develop artificial intelligence (AI) tools to aid ultrasound scanning and to enhance ultrasound education. Both located in South Wales, NIAW and Intelligent Ultrasound share a common desire to improve ultrasound practice through education, innovation and research that will lead to improved clinical technique and as a result, improved patient care and outcomes.

Intelligent Ultrasound aims to make ultrasound easier for clinicians to use: for the clinic, it is developing real-time artificial intelligence-based clinical ultrasound image analysis software to improve scan quality and workflow;

The company recently announced that it had signed its first long-term licencing and co-development agreement with one of the world’s leading ultrasound equipment manufacturers to install its AI real-time image analysis software onto a range of specialty specific ultrasound systems marketed in the global healthcare market.

Early Detection Innovation Sandpit and Award

The theme for the November 2019 workshop is applying artificial intelligence techniques to digital pathology images for cancer early detection.

In some cases, pathologists examine a pre-cancerous condition with the aim of identifying the transition to cancer early, allowing for an intervention to take place, even before an invasive cancer is established.  There is currently a shortage of histopathologists and the workload is increasing.

The diagnostic process could be aided by digital pathology and artificial intelligence (AI), improving turnaround times and diagnostic accuracy, provide data for further research and potentially pick up early signs of cancer which may otherwise be missed.

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