x402 enables Hypertext Transfer Protocol (HTTP) services like application programming interfaces (APIs), data feeds, and inference providers to accept cryptocurrency payments for access. As agents increasingly consume these services, discovery becomes critical: which swap interface should an agent trust? Which data provider is the most reliable? We introduce TraceRank, a reputation-weighted ranking algorithm where payment transactions serve as endorsements. TraceRank seeds addresses with precomputed reputation metrics and propagates reputation through payment flows weighted by transaction value and temporal recency. Applied to x402's payment graph, this surfaces services preferred by high-reputation users rather than those with high transaction volume. Our system combines TraceRank with semantic search to respond to natural language queries with high quality results. We argue that reputation propagation resists Sybil attacks by making spam services with many low-reputation payers rank below legitimate services with few high-reputation payers. Ultimately, we aim to construct a search method for x402 enabled services that avoids infrastructure bias and has better performance than purely volume based or semantic methods.

Reject Inference (RI) methods aim to address sample bias by inferring missing repayment data for rejected credit applicants. Traditional approaches often assume that the behavior of rejected clients can be extrapolated from accepted clients, despite potential distributional differences between the two populations. To mitigate this blind extrapolation, we propose a novel Confident Inlier Extrapolation framework (CI-EX). CI-EX iteratively identifies the distribution of rejected client samples using an outlier detection model and assigns labels to rejected individuals closest to the distribution of the accepted population based on probabilities derived from a supervised classification model. The effectiveness of our proposed framework is validated through experiments on two large real-world credit datasets. Performance is evaluated using the Area Under the Curve (AUC) as well as RI-specific metrics such as Kickout and a novel metric introduced in this work, denoted as Area under the Kickout. Our findings reveal that RI methods, including the proposed framework, generally involve a trade-off between AUC and RI-specific metrics. However, the proposed CI-EX framework consistently outperforms existing RI models from the credit literature in terms of RI-specific metrics while maintaining competitive performance in AUC across most experiments.

Mediation is often treated as an extension of negotiation, without taking into account the unique role that norms and facts play in legal mediation. Additionally, current approaches for updating argument acceptability in response to changing variables frequently require the introduction of new arguments or the removal of existing ones, which can be inefficient and cumbersome in decision-making processes within legal disputes. In this paper, our contribution is two-fold. First, we introduce a QuAM (Quantitative Argumentation Mediate) framework, which integrates the parties' knowledge and the mediator's knowledge, including facts and legal norms, when determining the acceptability of a mediation goal. Second, we develop a new formalism to model the relationship between the acceptability of a goal argument and the values assigned to a variable associated with the argument. We use a real-world legal mediation as a running example to illustrate our approach.

Opinions abound on what's right and wrong with our U.S. healthcare system, but there's one thing most can agree on: There's a need to transform the experience for patients, providers and payers. The Covid-19 pandemic served as a catalyst for us to relook at and reimagine the digitization of the healthcare system. The strategic adoption of artificial intelligence (AI) could be transformational, but technology leaders at healthcare organizations are constrained by stringent compliance requirements and security concerns. And their fears aren't unfounded--any data breach could be catastrophic. Trust in AI doesn't come easily--one must tread cautiously, particularly in this industry.

Summary The AI in healthcare market size was valued at US$7,679.39 million in 2021 and is expected to grow at a compound annual growth rate (CAGR) of 39.05% during 2022-2027. The key to the growth has been increasing investment and development in AI and increasing strategic moves by market players are stimulating. Additionally, key strategic partnerships and mergers and acquisitions are expected to accelerate market growth. Healthcare, including pharma, medical devices, healthcare providers, and payers, is a highly regulated industry, and therefore can be slow to adopt new technologies and modernize.However, the healthcare industry is realizing the benefits artificial intelligence (AI) can bring, and it is now being used in different areas across the entire value chain. Additionally, its use in the healthcare space is expected to continue to increase in the next five years. The integration of software with artificial intelligence is creating growth avenues for the global artificial intelligence in healthcare market.Integration of software with artificial intelligence offers immediate decision support and best results to diagnose diseases.

I recently presented the plenary session at a pharma conference covering how Artificial Intelligence (AI) is transforming pharma sales and marketing, I provided examples Eularis had completed for pharma client projects. Several of the attendees sent me emails afterwards wanting to know more about the specific examples I gave, which were as varied as our client needs. It was interesting to learn how few of these types of applications they were familiar with, and I thought the readers of my white papers would want to know about them, too. I've written about many of these topics before, and I'm including those links at the end of each section in case you are interested in digging deeper into a specific topic. According to Takeda Pharmaceuticals, the average time taken to diagnose a rare disease without technology is 7.6 years and comes after countless tests and physician visits. This creates a high cost to the healthcare system, not to mention much suffering for the patient. And some cases are even worse.

Physicians spend nearly two full business days per week on prior authorization requests as part of an antiquated, manual process. Prior authorization is the "single highest cost for the healthcare industry" in the U.S., totaling some $767 million a year, according to the CAQH index. Physicians spend nearly two full business days per week on prior authorization requests, and payers devote thousands of manhours reviewing and approving them in an antiquated, manual process involving phone calls and faxes. The arduous task often delays necessary treatment and sometimes results in treatment abandonment -- patients just get tired of waiting, so they give up -- both of which hurt patient outcomes and ultimately raise costs in the long run. Prior authorization has been identified as one of the biggest opportunities for applying artificial intelligence (AI) to help lower the administrative burden and cost.

Cybersecurity is a growing area of risk in healthcare, and organizations are grappling with the vulnerabilities and the ways patient data can be used against patients and organizations. From identity theft to healthcare fraud, waste and abuse, cybercriminals breached 642 accounts of 500 or more patient profiles in 2020. That's a rate of more than 1.76 per day, reports HIPAA Journal, adding up to 29 million healthcare records breached last year. Security breaches cost healthcare companies $6 trillion dollars by the end of 2020. According to Health IT Security, three security data breaches in 2020 alone affected almost 2,000,000 records, opening opportunities for identity theft and online fraud.

According to Cognizant Center for the Future of Work research, in the post-pandemic world, payers find themselves uniquely positioned to leverage technologies to spur innovation and efficiencies, says Bill Shea a Vice President within Cognizant Consulting's Healthcare Practice. Payers emerged from the pandemic relatively unscathed, but as businesses move to digital channels, their mandate is clear: deploy advanced technologies to create efficiencies, generate revenue, and spur innovation to meet customer needs. Chatbots, the Internet of Things (IoT), and big data have emerged as top focal areas for payers, with many achieving wide-scale implementation. With machines to supplement and in some cases, extend human work, many payers are well-positioned to do just that. Cognizant's Center for the Future of Work (CFoW), working with Oxford Economics, recently surveyed 4,000 C-level executives globally, including 50 senior healthcare payers in the U.S. to understand how this agenda is moving forward.

COVID-19 has led to an increase in payer adoption of technology and innovation, according to Shreesh Tiwari, principal at ZS, speaking during the HIMSS State of Healthcare event. Sixty-four percent of health insurance executives report an accelerated adoption of digital health initiatives such as virtual health. Another 53% report an acceleration in adoption of artificial intelligence and machine learning practices, while 42% said COVID-19 has helped facilitate the adoption of value-based care arrangements, according to State of Healthcare research by HIMSS, the parent company of Healthcare Finance News. COVID-19 has helped to drive changes not just in technology, but in attitude, Tiwari said. The mental and cultural barriers in terms of adoption are no longer being seen as issues. Around half of payers have an innovation lab and believe AI and machine learning will drive innovation forward.