Recently, a growing number of experts in artificial intelligence (AI) and medicine have be-gun to suggest that the use of AI systems, particularly machine learning (ML) systems, is likely to humanise the practice of medicine by substantially improving the quality of clinician-patient relationships. In this thesis, however, I argue that medical ML systems are more likely to negatively impact these relationships than to improve them. In particular, I argue that the use of medical ML systems is likely to comprise the quality of trust, care, empathy, understanding, and communication between clinicians and patients.

Generalist foundation model has ushered in newfound capabilities in medical domain. However, the contradiction between the growing demand for high-quality annotated data with patient privacy continues to intensify. The utilization of medical artificial intelligence generated content (Med-AIGC) as an inexhaustible resource repository arises as a potential solution to address the aforementioned challenge. Here we harness 1 million open-source synthetic fundus images paired with natural language descriptions, to curate an ethical language-image foundation model for retina image analysis named VisionCLIP. VisionCLIP achieves competitive performance on three external datasets compared with the existing method pre-trained on real-world data in a zero-shot fashion. The employment of artificially synthetic images alongside corresponding textual data for training enables the medical foundation model to successfully assimilate knowledge of disease symptomatology, thereby circumventing potential breaches of patient confidentiality.

Did you miss a session from MetaBeat 2022? Head over to the on-demand library for all of our featured sessions here. One of the most challenging and valuable domains for AI is medicine. Both the opportunities and the dangers are great in applying the technology to healthcare overall. The value of improved medical care is immediate, especially for people suffering from diseases that cannot presently be adequately treated.

Medical imaging is an important part of modern healthcare, enhancing both the precision, reliability and development of treatment for various diseases. Artificial intelligence has also been widely used to further enhance the process. However, conventional medical image diagnosis employing AI algorithms require large amounts of annotations as supervision signals for model training. To acquire accurate labels for the AI algorithms -- radiologists, as part of the clinical routine, prepare radiology reports for each of their patients, followed by annotation staff extracting and confirming structured labels from those reports using human-defined rules and existing natural language processing (NLP) tools. The ultimate accuracy of extracted labels hinges on the quality of human work and various NLP tools. The method comes at a heavy price, being both labour intensive and time consuming. An engineering team has now developed a new approach which can cut human cost down by 90%, by enabling the automatic acquisition of supervision signals from hundreds of thousands of radiology reports at the same time. It attains a high accuracy in predictions, surpassing its counterpart of conventional medical image diagnosis employing AI algorithms.

Medical imaging is a significant part of modern healthcare. It boosts both the precision, reliability and development of treatment for various diseases. Artificial intelligence has also been widely used to further enhance the process. However, conventional medical image diagnosis that employs AI algorithms requires large amounts of annotations as supervision signals for model training. To acquire accurate labels for the AI algorithms – radiologists, as part of the clinical routine, prepare radiology reports for each of their patients, followed by annotation staff extracting and confirming structured labels from those reports using human-defined rules and existing natural language processing (NLP) tools.

Medical imaging is an important part of modern healthcare, enhancing both the precision, reliability and development of treatment for various diseases. Artificial intelligence has also been widely used to further enhance the process. However, conventional medical image diagnosis employing AI algorithms require large amounts of annotations as supervision signals for model training. To acquire accurate labels for the AI algorithms--radiologists, as part of the clinical routine, prepare radiology reports for each of their patients, followed by annotation staff extracting and confirming structured labels from those reports using human-defined rules and existing natural language processing (NLP) tools. The ultimate accuracy of extracted labels hinges on the quality of human work and various NLP tools.

Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience. We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. To meet this need, we are building MedPerf, an open framework for benchmarking machine learning in the medical domain. MedPerf will enable federated evaluation in which models are securely distributed to different facilities for evaluation, thereby empowering healthcare organizations to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status, and our roadmap. We call for researchers and organizations to join us in creating the MedPerf open benchmarking platform.

In late February 2020, the European Commission published a white paper on artificial intelligence (AI)a and an accompanying report on the safety and liability implications of AI, the Internet of Things (IoT), and robotics.b In the white paper, the Commission highlighted the "European Approach" to AI, stressing "it is vital that European AI is grounded in our values and fundamental rights such as human dignity and privacy protection." In April 2021, the proposal of a Regulation entitled "Artificial Intelligence Act" was presented.2 This Regulation shall govern the use of "high-risk" AI applications which will include most medical AI applications. Referring to the above-mentioned statement, this Viewpoint aims to show European fundamental rights already provide important legal (and not merely ethical) guidelines for the development and application of medical AI.7 As medical AI can affect a person's physical and mental integrity in a very intense way and any malfunction could have serious consequences, it is a particularly relevant field of AI in terms of fundamental rights.

As Computers and Artificial Intelligence (AI) play a key role in improving medical fields, experts in medical and engineering research at the University of Iowa are merging disciplines to work towards the advancement of medical AI with the help of a $1 million grant from the National Science Foundation. Assistant Professor of Industrial and Systems...

Artificial intelligence is playing an increasingly important role in many fields of medicine, assisting physicians in most steps of patient management. In nephrology, artificial intelligence can already be used to improve clinical care, hemodialysis prescriptions, and follow-up of transplant recipients. However, many nephrologists are still unfamiliar with the basic principles of medical artificial intelligence. This review seeks to provide an overview of medical artificial intelligence relevant to the practicing nephrologist, in all fields of nephrology. We define the core concepts of artificial intelligence and machine learning and cover the basics of the functioning of neural networks and deep learning. We also discuss the most recent clinical applications of artificial intelligence in nephrology and medicine; as an example, we describe how artificial intelligence can predict the occurrence of progressive immunoglobulin A nephropathy.