What does AI bring to interventional surgical robots? Interventional surgical robots remove the physician from X-ray hazards, enable surgeries and stenting without compromising safety, and allow increased precision. Image navigation is the eye and brain of interventional robots, playing a crucial role in both diagnoses and as the primary guidance tool during interventions. Fortunately, powerful artificial intelligence (AI) technology is penetrating the medical imaging arena, holding significant promise for creating an'eye-hand-brain' collaborative system for interventional robots and optimizing fluoroscopic interventional procedures. From preoperative treatment plans to intraoperative imaging navigation and postoperative imaging follow-ups, AI can help realize image-guided precision medical visualization and provide physicians with additional information not available through conventional approaches.

This paper proposes a possible method using natural language processing that might assist in the FDA medical device marketing process. Actual device descriptions are taken and matched with the device description in FDA Title 21 of CFR to determine their corresponding device type. Both pre-trained word embeddings such as FastText and large pre-trained sentence embedding models such as sentence transformers are evaluated on their accuracy in characterizing a piece of device description. An experiment is also done to test whether these models can identify the devices wrongly classified in the FDA database. The result shows that sentence transformer with T5 and MPNet and GPT-3 semantic search embedding show high accuracy in identifying the correct classification by narrowing down the correct label to be contained in the first 15 most likely results, as compared to 2585 types of device descriptions that must be manually searched through. On the other hand, all methods demonstrate high accuracy in identifying completely incorrectly labeled devices, but all fail to identify false device classifications that are wrong but closely related to the true label.

Abstract--This article reviews the landscape of ethical challenges of integrating artificial intelligence (AI) into smart healthcare products, including medical electronic devices. Differences between traditional ethics in the medical domain and emerging ethical challenges with AI-driven healthcare are presented, particularly as they relate to transparency, bias, privacy, safety, responsibility, justice, and autonomy. Open challenges and recommendations are outlined to enable the integration of ethical principles into the design, validation, clinical trials, deployment, monitoring, repair, and retirement of AI-based smart healthcare products. Healthcare systems in countries around the been approved by the FDA, and there are many more globe are struggling to cope with health emergencies in the development pipeline [8]. Many direct to such as the COVID-19 pandemic, provide universal consumer AI devices for health monitoring and wellbeing health coverage, and improve general health and wellbeing.

In a recent study published in PLOS Digital Health, researchers reviewed existing literature on the use of artificial intelligence (AI) in health care to characterize the AI applications used in the clinical applications during the coronavirus disease 2019 (COVID-19) pandemic, investigate the location, timing, and extent of AI use in healthcare, and examine the United States (U.S.) regulatory approval processes. Despite the large number of approvals granted by the U.S. Food and Drug Administration (FDA) to AI applications in healthcare in the last six years, the adoption of AI applications in different areas of healthcare has been limited. Furthermore, there is limited information on the development and use of AI applications during the COVID-19 pandemic, unlike the significant and rapid growth in telehealth and vaccine technologies. While previous reviews have reviewed the potential uses, challenges, and impacts of AI applications for COVID-19 clinical response, many of the reviews found methodological flaws and potential biases in the use of AI applications in clinical practice. A scarcity of reviews provides a comprehensive report on the development, testing, and applications of AI in COVID-19 clinical responses.

Scholars explore regulatory approaches to artificial intelligence in the health care sector. Patient-centered care is a pillar of the American health care system. But, as the U.S. population grows and ages, providers need new ways to manage ever-increasing caseloads. Artificial intelligence (AI) offers an opportunity to improve the efficiency of health care administration, disease diagnosis and detection, drug development, and more. Although AI is poised to transform business operations across various sectors of the economy, experts agree that it holds heightened potential in the health care industry.

Researchers at the CUNY Graduate Center have created an artificial intelligence model, Context-aware Deconfounding Autoencoder (CODE-AE), that can screen drug compounds to accurately predict efficacy in humans. In tests, the model was able to theoretically identify personalized drugs that could better treat more than 9,000 cancer patients. The researchers expect the technique will improving the accuracy and reduce the time and cost of drug discovery and development, and accelerate precision medicine. "Our new machine learning model can address the translational challenge from disease models to humans," said Lei Xie, PhD, a professor of computer science, biology and biochemistry at the CUNY Graduate Center and Hunter College. "CODE-AE uses biology-inspired design and takes advantage of several recent advances in machine learning. For example, one of its components uses similar techniques in Deepfake image generation."

A novel artificial intelligence model could significantly improve the accuracy and reduce the time and cost of the drug development process. Between identifying a potential therapeutic compound and U. S. Food and Drug Administration (FDA) approval of a new drug is an arduous journey that can take well over a decade and cost upwards of a billion dollars. A team of researchers at the CUNY Graduate Center has developed a novel artificial intelligence model that could significantly improve the accuracy and reduce the time and cost of the drug development process. As described in a paper to be published today (October 17) in Nature Machine Intelligence, the new model, called CODE-AE, can screen novel drug compounds to accurately predict efficacy in humans. In tests, it was also able to theoretically identify personalized drugs for over 9,000 patients that could better treat their conditions.

It's a technique by which genomes of living organisms can be modified precisely, cheaply, and easily. It can be used for the creation of new medicines, agricultural products, and even genetically modified organisms. Trials are used to establish which formula is the most beneficial to the widest segment of society. Remember that half of any drug advert goes towards listing the risks for the rest. With the advent of modern genomics, it's possible to formulate medicines that are tailor-made to an individual's unique DNA makeup.

Molecular property prediction is essential for drug discovery. In recent years, deep learning methods have been introduced to this area and achieved state-of-the-art performances. However, most of existing methods ignore the intrinsic relations between molecular properties which can be utilized to improve the performances of corresponding prediction tasks. In this paper, we propose a new approach, namely Physics properties Enhanced Molecular Property prediction (PEMP), to utilize relations between molecular properties revealed by previous physics theory and physical chemistry studies. Specifically, we enhance the training of the chemical and physiological property predictors with related physics property prediction tasks. We design two different methods for PEMP, respectively based on multi-task learning and transfer learning. Both methods include a model-agnostic molecule representation module and a property prediction module. In our implementation, we adopt both the state-of-the-art molecule embedding models under the supervised learning paradigm and the pretraining paradigm as the molecule representation module of PEMP, respectively. Experimental results on public benchmark MoleculeNet show that the proposed methods have the ability to outperform corresponding state-of-the-art models.

… Administration for an advanced spinal cord stimulator (SCS) that uses artificial intelligence to individualize treatment for each patient.