Paige, a global leader in end-to-end digital pathology solutions and clinical AI applications, announced the availability of Mindpeak's AI algorithms for IHC biomarker quantification in the Paige Platform. Mindpeak, a leader in image analysis AI software, brings state-of-the-art AI algorithms for analyzing IHC slides of lung and breast tissue into the Paige Platform. These two leading pathology AI providers have combined their technologies to help pathologists deliver faster, more accurate, and more reproducible quantitative cancer diagnoses, even in the most challenging and complex cases. "The Paige Platform is one of the most robust and easily integrated solutions available for both routine and AI-augmented digital workflows. The platform offers open compatibility with Mindpeak's image analysis algorithms and AI solutions to support a complete and optimized workflow experience for pathologists" This new integration allows a complete and seamless workflow on the Paige Platform through FullFocus, Paige's FDA-cleared whole-slide image viewer*.

The spread of online misinformation threatens public health, democracy, and the broader society. While professional fact-checkers form the first line of defense by fact-checking popular false claims, they do not engage directly in conversations with misinformation spreaders. On the other hand, non-expert ordinary users act as eyes-on-the-ground who proactively counter misinformation -- recent research has shown that 96% counter-misinformation responses are made by ordinary users. However, research also found that 2/3 times, these responses are rude and lack evidence. This work seeks to create a counter-misinformation response generation model to empower users to effectively correct misinformation. This objective is challenging due to the absence of datasets containing ground-truth of ideal counter-misinformation responses, and the lack of models that can generate responses backed by communication theories. In this work, we create two novel datasets of misinformation and counter-misinformation response pairs from in-the-wild social media and crowdsourcing from college-educated students. We annotate the collected data to distinguish poor from ideal responses that are factual, polite, and refute misinformation. We propose MisinfoCorrect, a reinforcement learning-based framework that learns to generate counter-misinformation responses for an input misinformation post. The model rewards the generator to increase the politeness, factuality, and refutation attitude while retaining text fluency and relevancy. Quantitative and qualitative evaluation shows that our model outperforms several baselines by generating high-quality counter-responses. This work illustrates the promise of generative text models for social good -- here, to help create a safe and reliable information ecosystem. The code and data is accessible on https://github.com/claws-lab/MisinfoCorrect.

Pearl, the global leader in dental AI solutions, and CareStack, a leading cloud-based dental practice management software, announced a partnership to integrate Pearl's Second Opinion real-time disease detection capabilities within CareStack's all-in-one practice management system, as well as extend CareStack users access to Pearl's clinical performance AI platform, Practice Intelligence . The partnership ensures that CareStack's customers have seamless comprehensive access to the most robust FDA-cleared AI feature set on the market. "CareStack equips a variety of dental practices around the country with the most transformative tools and technology designed to help streamline operations and optimize efficient workflows" "CareStack equips a variety of dental practices around the country with the most transformative tools and technology designed to help streamline operations and optimize efficient workflows," said Abhi Krishna, co-founder and CEO of CareStack. "Partnering with Pearl, we are able to help facilitate greater practice efficiency and improved patient outcomes by equipping our practitioners with dentistry's most advanced pathology detection and patient communication technology." Second Opinion, which will be available as a fully integrated service within CareStack's practice management system, is the first-and-only chairside AI software cleared by the FDA to help dentists detect numerous common conditions in both bitewing and periapical x-rays of adult teeth in patients as young as 12 years old.

Generative AI like ChatGPT is truly exciting, and it's easy to be seduced by the technology's potential to produce, well, almost any sort of output. The opportunity in generative AI is enormous but requires careful analysis of where the best applications lie. Healthcare, in particular, requires this assessment – this isn't an industry known for fast change, and the risks of inappropriately deploying new technology can be huge. For instance, consider the hype around IBM's Watson Health a few years ago; this AI was going to figure out complex cancers! It didn't, and it was sold off cheaply in parts last year. This includes what people need to stop doing in order to start embracing the new solution.

Neuralink's efforts to bring a brain-computer interface still have a way to go. According to a new report from Reuters, Elon Musk's startup was apparently denied authorization by the FDA in 2022 to conduct human trials using the same devices that, well, killed 1,500 animals. Those tests, according to internal reports, lead to needless suffering and death of test subjects. Current and former Neuralink employees told Reuters: "The agency's major safety concerns involved the device's lithium battery; the potential for the implant's tiny wires to migrate to other areas of the brain; and questions over whether and how the device can be removed without damaging brain tissue." The FDA is concerned that, because of the minuscule size of the electrical leads, they are at risk of breaking off during removal (or even during use). At Neuralink's open house last November, Musk claimed the company would secure FDA approval "within six months," basically by this spring.

Despite the repeated and audacious claims by its sometimes CEO, Elon Musk, the prospects of brain-computer interface (BCI) startup Neuralink bringing a product to market remain distant, according to a new report from Reuters. The BCI company was apparently denied authorization by the FDA in 2022 to conduct human trials using the same devices that killed all those pigs -- namely on account of; pig killing. "The agency's major safety concerns involved the device's lithium battery; the potential for the implant's tiny wires to migrate to other areas of the brain; and questions over whether and how the device can be removed without damaging brain tissue," current and former Neuralink employees told Reuters. The FDA's concerns regarding the battery system and its novel transdermal charging capabilities revolve around the the device's chances of failure. According to Reuters, the agency is seeking reassurances that the battery is "very unlikely to fail" because should it do so, the discharge of electrical current or heat energy from a ruptured pack could fry the surrounding tissue.

Elon Musk's Nueralink will not be testing its brain implant on humans anytime soon - the US Food and Drug Administration (FDA) has rejected the company's application. The agency outlined dozens of issues the company must address before human testing, a critical milestone for final product approval, Neuralink staffers told Reuters. The concerns include the device's lithium battery; the potential for the implant s tiny wires to migrate to other areas of the brain; and questions over whether and how the device can be removed without damaging brain tissue, the employees said. Musk applied in early 2022, but staffers said the company co-founder has yet to solve all the problems - even though the billionaire revealed human trials would start in six months back in November. Three staffers said they were skeptical the company could quickly resolve the issues.

SpectraWAVE, a medical imaging company focused on improving the treatment and outcomes for patients with coronary artery disease (CAD), announced Food and Drug Administration (FDA) 510(k) clearance of their flagship intravascular imaging system, HyperVue The system combines next-generation DeepOCT images and near infrared spectroscopy (NIRS) with state-of-the-art ease of use to support physicians optimizing coronary stenting in the cardiac catheterization lab. In addition, the system has now been used by multiple physicians as part of a first-in-human study. "Clinical evidence strongly suggests that patients benefit from intravascular imaging-guided stent optimization" "This is a landmark day for SpectraWAVE, but more importantly, a critical step towards improving outcomes for patients with coronary artery disease," said Eman Namati, Ph.D., Chief Executive Officer of SpectraWAVE. "Our proprietary DeepOCT-NIRS imaging system pushes the technological limits of optical coherence tomography--both in image quality and depth--while combining it with spectroscopy for the first time, packaging both into a no-flush catheter with an artificial intelligence-powered user experience. With this regulatory clearance, we are excited to begin the transition to a commercial entity and launch our product."

In the United States, the National Transportation Safety Board is widely respected for its prompt responses to investigate plane, train, and boat accidents. Its independent reports have done much to promote safety in civil aviation and beyond. Could a National Algorithms Safety Board have a similar impact in increasing safety for algorithmic systems, especially the rapidly proliferating Artificial Intelligence applications based on unpredictable machine learning? Alternatively, could agencies such as the Food & Drug Administration (FDA), Securities and Exchange Commission (SEC), or Federal Communications Commission (FCC) take on the task of increasing safety of algorithmic systems? In addition to federal agencies, could the major accounting firms provide algorithmic audits as they do in auditing financial statements of publicly listed companies?

Atomic partial charges are crucial parameters in molecular dynamics (MD) simulation, dictating the electrostatic contributions to intermolecular energies, and thereby the potential energy landscape. Traditionally, the assignment of partial charges has relied on surrogates of \textit{ab initio} semiempirical quantum chemical methods such as AM1-BCC, and is expensive for large systems or large numbers of molecules. We propose a hybrid physical / graph neural network-based approximation to the widely popular AM1-BCC charge model that is orders of magnitude faster while maintaining accuracy comparable to differences in AM1-BCC implementations. Our hybrid approach couples a graph neural network to a streamlined charge equilibration approach in order to predict molecule-specific atomic electronegativity and hardness parameters, followed by analytical determination of optimal charge-equilibrated parameters that preserves total molecular charge. This hybrid approach scales linearly with the number of atoms, enabling, for the first time, the use of fully consistent charge models for small molecules and biopolymers for the construction of next-generation self-consistent biomolecular force fields. Implemented in the free and open source package \texttt{espaloma\_charge}, this approach provides drop-in replacements for both AmberTools \texttt{antechamber} and the Open Force Field Toolkit charging workflows, in addition to stand-alone charge generation interfaces. Source code is available at \url{https://github.com/choderalab/espaloma_charge}.