Artificial intelligence for medicine: Progress, challenges, and perspectives

Abstract

<p>Artificial Intelligence (AI) has transformed how we live and how we think, and it will change how we practice medicine. With multimodal big data, we can develop large medical models that enables what used to unimaginable, such as early cancer detection several years in advance and effective control of virus outbreaks without imposing social burdens. The future is promising, and we are witnessing the advancement. That said, there are challenges that cannot be overlooked. For example, data generated is often isolated and difficult to integrate from both perspectives of data ownership and fusion algorithms. Additionally, existing AI models are often treated as black boxes, resulting in vague interpretation of the results. Patients also exhibit a lack of trust to AI applications, and there are insufficient regulations to protect patients�� privacy and rights. However, with the advancement of AI technologies, such as more sophisticated multimodal algorithms and federated learning, we may overcome the barriers posed by data silos. Deeper understanding of human brain and network structures can also help to unravel the mysteries of neural networks and construct more transparent yet more powerful AI models. It has become something of a trend that an increasing number of clinicians and patients will implement AI in their life and medical practice, which in turn can generate more data and improve the performance of models and networks. Last but not the least, it is crucial to monitor the practice of AI in medicine and ensure its equity, security, and responsibility.</p>

Future perspectives of non-invasive techniques for evaluating oocyte and embryo quality

“…Currently, metabolic markers for oocyte quality are still under exploration. 7 To discover predictive biomarkers for normal and abnormal metabolism, microfluidics platforms with automated, high-coverage, and high-throughput targeted metabolic assessments in real time will be necessary.…”

Performance enhancement in wavefront shaping of multiply scattered light: a review

“…As seen, artificial intelligence (AI) can effectively enhance the performance of WFS and, hopefully, further development can be seen in the future to explore the application in biomedicine, which has been currently being reformed by the AI technologies. 76 …”

“…Such a scheme allows the TFOTNet gain real-time knowledge of the medium status, which further assures the maintenance of optical focusing as measured by the PBR through a moving scattering medium (ground glass). As seen, artificial intelligence (AI) can effectively enhance the performance of WFS and, hopefully, further development can be seen in the future to explore the application in biomedicine, which has been currently being reformed by the AI technologies 76 …”

Challenges for translating implantable brain-computer interface to medical device

“…In the spirit of encouraging innovation and the urgent need for clinical devices to be launched as soon as possible, the Food and Drug Administration (FDA) of the United States released "Implanted Brain-Computer Interface Devices for Patients with Paralysis or Amputation-Non-Clinical Testing and Clinical Considerations" in May 2021. 2 This guidance provides an experimental device exemption (IDE) for iBCIs used in paralyzed or amputated patients, including devices connected to the central or peripheral nervous system to restore or replace motor function in paralyzed or amputated patients. In addition to previous medical device specifications, this guideline also adds new emphases on the collection of neural data, data security during transmission and analysis, as well as the need to train users (such as patients, surgeons, rehabilitation therapists, and nursing staff).…”