Silicon Valley-based Atropos Health has successfully raised $33 million in a Series B funding round, marking a significant step forward in its mission to integrate AI-powered, personalized real-world evidence into healthcare decision-making.

In a recent announcement, Atropos Health, a pioneer in generating personalized real-world evidence, disclosed a substantial $33 million acquisition in Series B funding. This investment round featured prominent contributions from healthcare behemoths like McKesson, Merck, and Cencora Ventures, indicating a robust industry endorsement of Atropos’ innovative approach to healthcare.

The funds are earmarked for a strategic expansion aimed at enhancing the company’s operational capacity and doubling down on critical initiatives. These include a deeper penetration into the life sciences sector, broadening channel partnerships in value-based care and oncology, and expanding its network of data partners to enrich its evidence base.

Brigham Hyde, PhD, CEO and co-founder of Atropos Health, expressed his enthusiasm in a VentureBeat interview, stating, “We’re on a mission to bring personalized evidence for care to everybody in the world. This funding is a pivotal step in that journey. Specifically, we’ll be focusing on reinforcing our strategic initiatives, continuing our successful launch in life sciences, and enhancing our partnerships, particularly in value-based and specialty care oncology.”

Atropos Health is not just another player in the healthcare field; it is a trailblazer aiming to close the pervasive “evidence gap” in medical decision-making. The company’s flagship technology, Geneva OS, harnesses artificial intelligence (AI) and automation to rapidly generate clinical-grade evidence from real-world data. This platform, which has been developed over nearly a decade of research at Stanford University, powers applications such as the generative AI assistant, ChatRWD.

The technology enables clinicians, researchers, and other healthcare stakeholders to swiftly access reliable clinical evidence, personalized to specific patient populations—a capability often missing in current healthcare practices. Dr. Hyde highlighted a concerning statistic in his interview: “Only about 14% of daily medical decisions have any high-quality evidence behind them. Our goal is to use high-quality data, analyzed correctly, to fill this evidence gap.”

The central mission of Atropos is to provide clinicians with easy access to personalized evidence, thereby enhancing patient outcomes. Dr. Hyde used the example of heart failure patients to illustrate the need for tailored evidence that caters to subpopulations with unique characteristics and comorbidities, which could lead to more effective treatments and cost control.

Atropos’ applications extend beyond clinical decision-making. The company collaborates with pharmaceutical leaders, including Janssen, to expedite drug development by leveraging real-world evidence for clinical trial design, patient recruitment, and more. Dr. Hyde even suggested that the platform could simulate clinical trials, potentially revolutionizing the way pharmaceutical research is conducted by reducing cycle times and de-risking trials.

Despite the excitement surrounding large language models (LLMs) and generative AI, Atropos prioritizes building trust through methodological rigor and transparency. Dr. Hyde expressed concerns about the “hallucination rates” in current AI models and emphasized that Geneva OS ensures clinical-grade quality and transparency, backed by a decade of publications.

Strategic Use of Series B Funding

With a fresh influx of capital and a roster of strategic backers, Atropos is poised to bring its vision of personalized, automated clinical evidence to the global healthcare landscape. “Evidence is the currency of value in healthcare,” Dr. Hyde posited. “What if I could give doctors more evidence, more personalized, so they make better decisions? Fundamentally, we’re trying to move the world to a point where all patients and all providers have access to quality, personalized evidence for their decision-making.”

This bold vision by Atropos Health not only promises to transform patient care but also positions the company as a frontrunner in the integration of AI and healthcare. As they continue to bridge the evidence gap, the future of healthcare looks promisingly precise, personalized, and powered by artificial intelligence.

The Consumer Financial Protection Bureau (CFPB) has filed a lawsuit against SoLo Funds, a fintech company specializing in peer-to-peer lending. The CFPB accuses the company of employing “digital dark patterns” to mislead borrowers and illegally extracting fees, despite advertising a fee-free service.

Allegations of Deceptive Practices

In a press release on May 17, CFPB Director Rohit Chopra stated, “The CFPB is suing SoLo for using digital trickery to hide interest and fees on its online loans. SoLo has had repeated run-ins with state regulators, and we are putting a stop to their fake tipping scheme.”

The CFPB’s lawsuit details several allegations against SoLo Funds:

• Misrepresentation of Loan Costs: The company is accused of obscuring the true cost of loans, making it difficult for consumers to understand the financial commitments they were making.
• Illegal Fee Collection: Despite advertising no fees, SoLo allegedly took fees from borrowers under the guise of tips and donations.
• False Credit Reporting Threats: SoLo is charged with making unfounded threats related to credit reporting to coerce payments.
• Lack of Safeguards: The CFPB claims that SoLo’s business model lacks necessary consumer protection measures.

The CFPB contends that these practices not only deceived consumers but also contravened federal consumer financial laws.

Breakdown of Allegations Against SoLo Funds

Background and Growth of SoLo Funds

SoLo Funds was founded in 2018 by Rodney Williams and Travis Holoway, with a mission to provide financial services to underserved Americans who are frequently targeted by predatory lenders. The company’s innovative approach aimed to create a peer-to-peer lending platform where users could borrow money from each other.

SoLo Funds has garnered significant financial backing, raising approximately $13 million in venture funding. Notable investors include Serena Ventures, founded by tennis star Serena Williams, Endeavor Catalyst, Alumni Ventures, and Techstars. In 2021, TechCrunch highlighted SoLo Funds when it secured $10 million in Series A funding. The platform has seen considerable growth, claiming to have reached one million registered users and over 1.3 million downloads by 2023.

Recent Legal Challenges

The CFPB lawsuit is the latest in a series of legal troubles for SoLo Funds. The company settled multiple lawsuits last year with the District of Columbia and the State of California, addressing accusations of predatory lending practices. Additionally, the Connecticut Department of Banking issued a temporary cease-and-desist order against SoLo in 2022.

In December 2023, the company faced scrutiny from the State of Maryland, adding to its ongoing regulatory challenges.

SoLo Funds’ Response to the Lawsuit

In a statement to TechCrunch, SoLo Funds asserted that it had been working cooperatively with the CFPB for the past 18 months to establish a regulatory framework. The company claimed that an agreement had been reached on May 16, only to be “blindsided” by the lawsuit the following morning.

CEO Travis Holoway expressed his frustration, stating, “Minority innovators were challenged to create new models to address our communities’ financial inequalities. Now that we are doing that, the regulators seem driven by press releases when they should be motivated by true consumer protection and empowering equitable solutions.”

CFPB’s Objectives in the Lawsuit

The CFPB aims to rectify SoLo Funds’ practices and secure refunds for affected customers. The bureau is seeking several remedies, including:

• Preventing Future Violations: Ensuring that SoLo Funds implements compliant business practices.
• Monetary Relief: Providing financial restitution to consumers harmed by SoLo’s practices.
• Disgorgement of Gains: Requiring SoLo to return any ill-gotten gains.
• Civil Penalties: Imposing additional fines to deter future violations.

The lawsuit seeks to hold SoLo Funds accountable and establish a precedent for other fintech companies, emphasizing the importance of transparent and fair lending practices.

Key Takeaways

• The CFPB is suing SoLo Funds for allegedly using deceptive digital practices to mislead borrowers.
• SoLo is accused of misrepresenting loan costs, illegally collecting fees, and making false credit threats.
• The company has faced multiple legal challenges and settlements with various state regulators.
• SoLo Funds claims it was working towards regulatory compliance and was surprised by the lawsuit.
• The CFPB aims to enforce consumer protection laws, secure refunds for customers, and impose financial penalties on SoLo Funds.

This legal action by the CFPB underscores the regulatory scrutiny faced by fintech companies and highlights the ongoing efforts to protect consumers from deceptive financial practices.

In a cheeky nod to last year’s viral video featuring a montage of every utterance of the word “AI” during the Google I/O keynotes, CEO Sundar Pichai decided to turn the tables this year. As he brought the keynote to a close, Pichai couldn’t resist the temptation to showcase the capabilities of Google’s latest Gemini AI model. With a glimmer of amusement in his eyes, he fed the entire transcript of the keynote into Gemini’s eager algorithms, challenging it to count the number of times “AI” appeared. The result? A staggering 120 mentions, a testament to Google’s unwavering commitment to AI.

Google I/O 2024 Announcements: AI Integration and Competition

Over the past few days, the announcements from Google I/O were nothing short of impressive. Google is going head-to-head with OpenAI and Microsoft, integrating Gemini into a wide range of products. From the introduction of Astra, a multimodal AI tool, to Gemini’s integration across search, photos, and personal assistants, Google is cementing itself as a strong competitor in the AI landscape.

Key Highlights from Google I/O 2024:

• Astra AI Tool: A new multimodal AI tool.
• Gemini Integration: Extending across search, photos, and personal assistants.
• AI Agents and AI Teammate: New AI functionalities aimed at improving user experience.
• Gemini Models: Range from lightweight Gemini Nano to powerful Gemini 1.5 Pro and Flash.
• Developer-Focused Tools: Enhancements in Google Colab and other development platforms.

The variety of Gemini’s applications—from the lightweight Gemini Nano to the powerful Gemini 1.5 Pro and Flash—demonstrates Google’s ambitious plans. With innovations like AI Agents, AI Teammate, and the new Gemini app, Google aims to make AI a central part of everyday digital experiences, highlighting its strategy to outpace its rivals.

A Glimpse into the Past: Lessons from Microsoft’s Developer Focus

As I attended the keynote and subsequent breakout sessions, a sense of cognitive dissonance emerged. I was reminded of a distant memory from another tech giant and its eccentric CEO. The year was 2006, and a middle-aged man stood on a brightly illuminated stage, wearing a corporate blue button-down shirt and pleated khaki pants. This man, sweating profusely to the point of absurdity, was Steve Ballmer, the CEO of Microsoft. He pounded his fist into the palm of his hand and began to chant the word “developers” over and over again, whipping the crowd into a frenzy.

Suddenly, I was back in the Google I/O lab session. Developers, a hundred or more, sat in front of me, tuned in. Some struggled to follow along, raising their hands timidly to ask questions but quickly putting them down again as the speaker marched on, leaving no room for stragglers in the AI revolution. It hit me: Google has the wrong mantra. The AI war is being fought for the hearts and minds of developers, but Google is distracted by its consumer core.

Developer Sentiments: Google vs. Microsoft

Despite Google’s impressive technical achievements, developers are the ones who have to build the apps, use the tools, and ultimately decide the victors. Microsoft, in collaboration with OpenAI, remains focused on delighting developers. OpenAI, while known for its consumer-facing products, is actually highly developer-focused, providing comprehensive APIs, extensive documentation, and robust support to make AI integration seamless. Meta, with its open-source Llama 3 models, also positions itself as developer-friendly, allowing anyone to take its AI and build whatever they want, free of charge.

Comparison of AI Developer Focus:

This is not the first time Google has fumbled a lead. Developer traction has been the Achilles’ heel of Google Cloud, a marvelous infrastructure technically superior in many respects. Yet, after over a decade and $45 billion invested, Google Cloud still trails behind with an 11% market share, versus Amazon Web Services at 31% and Microsoft Azure at 25%. Google had a three-year head start, but Microsoft now has almost three times the market share. The reason? Developers.

Google’s Renewed Developer Push with AI

Will Google’s AI push finally break Microsoft’s grip on developers? I asked some of them at I/O. As our lab session came to a close, I approached Layla Bouzoubaa, a doctoral candidate at Drexel University, who was particularly attentive during a session on fine-tuning Gemini models.

“I think impressed is a strong word. I’m currently working with large language models (LLMs) in my research, and I am curious to see if I can use Gemini for my specific research topic which involves fine-tuning,” said Bouzoubaa. “I use OpenAI regularly, and it’s been very straightforward. Learning how to use Gemini within Google Colab seemed a little bit more intimidating from a developer’s perspective.”

Despite some challenges, Google has plenty of inroads with developers and significant opportunities. The company even landed OpenAI’s former developer advocate Logan Kilpatrick, which Business Insider called a big win in the war for AI talent.

Bouzoubaa sees fine-tuning smaller language models (SLMs) as something that is quickly becoming essential. “I don’t have the hardware to host my own model. So, before, fine-tuning a large model wasn’t really a possibility.” However, with smaller language models, Bouzoubaa sees the potential for better results with health data. “I know the models aren’t necessarily trained on the health data that I use, so being able to fine-tune is going to enable more AI applications.”

Developer Perspectives: Opportunities and Challenges

A few rows away, another developer was deeply focused on her laptop. “I’m kind of a Google fangirl,” said April Johnson from Extensis, a 30-year-old software company focused on managing licensed fonts for teams. “Microsoft is doing amazing stuff, but I think Google is still ahead with the deeper problems to solve with AI.”

Johnson was enthusiastic about learning with Google tools. “I use Google Colab all the time, and I like the way you can easily set up prototyping. It’s so easy to just prototype on my computer and then move it to the cloud.”

Near the last row of the room, a man with a beard, suggesting he almost certainly has the root password, hunches over a laptop with a thousand stickers from a hundred conferences like this one. He is Nick Bates from Cyber Drive, a startup creating a smartphone specifically for children. “What we’re using ML for right now is to check for patterns of abuse,” Bates says. “We are not creating our own AI models ourselves, but with Gemini being open, we’re looking at ways to leverage it.”

Another developer seated nearby who wished to remain anonymous echoed, “The problem is the big models are really big, so you need a big machine, and you need a lot of users to make it profitable to actually host yourself. Many of the open-source models are so big I don’t have the space to run them on my laptop.”

Microsoft’s Edge with Developers

As I left the Google I/O conference, it was clear that despite Google’s renewed push to win over developers with AI, Microsoft still has the upper hand. The Redmond giant’s deep roots in the enterprise, combined with its strong developer ecosystem and strategic partnership with OpenAI, give it a significant advantage in the battle for the hearts and minds of developers.

Microsoft’s release of the Phi-3 family of small language models, particularly the Phi-3-mini, demonstrates its capability to compete with SLMs. The model’s optimization for various platforms and its support for larger context windows make it an attractive option for developers looking to integrate AI into their projects.

Google, while technically impressive, still has some way to go in terms of making its AI tools more accessible and user-friendly for developers. The feedback from developers at I/O suggests that Google’s AI offerings can be intimidating to approach, especially compared to the more streamlined experience offered by Microsoft and OpenAI.

The Path Forward: Empowering Developers

As the AI race intensifies, it’s becoming increasingly clear that the key to success lies not just in the quality of the AI models themselves, but in the ability to empower developers to harness their potential. Microsoft, with its long-standing developer focus and strategic partnerships, seems poised to maintain its grip on this crucial audience. Google, despite its technical prowess, will need to double down on its efforts to simplify and streamline its AI offerings for developers if it hopes to catch up.

In the end, the winner of the AI race may well be determined by which company can most effectively rally developers to its cause. And right now, it looks like Microsoft is the one still chanting that word.

In the complex landscape of cancer treatment, clinical trials represent a beacon of hope, offering new therapies that could potentially extend or even save lives. Despite this, the rate of enrollment in these trials remains startlingly low, with only 3% to 5% of eligible patients participating annually in the United States. The reasons behind this are multifaceted, but a significant barrier has been the time-intensive process required for medical professionals to match patients with appropriate trials. This is where Triomics, a generative AI startup, steps in with a promise to transform this critical aspect of cancer care.

The Challenge of Clinical Trial Enrollment

Clinical trials are crucial for the development of new cancer treatments. They offer patients access to cutting-edge therapies that are not yet available on the market. However, the enrollment process is fraught with challenges. One major hurdle is the sheer amount of time it takes for oncologists and nurses to identify suitable trials for their patients. These medical professionals are often overwhelmed by their day-to-day responsibilities and struggle to keep up with the vast array of ongoing clinical trials.

The task of matching a patient with a trial is not straightforward. Most trials have stringent eligibility criteria, including the stage of cancer, specific mutations, and prior treatments the patient has undergone. Reviewing these factors against a patient’s medical records to find a suitable trial is a time-consuming process, often requiring several hours of meticulous work.

Personal Stories Highlight the Struggle

The difficulties of finding the right clinical trials are not just statistics—they affect real people. Many individuals, like myself, have experienced the challenge firsthand while trying to find trials for loved ones. I spent countless hours navigating clinicaltrials.gov to find options for my father and, more recently, for a friend with stage IV cancer. In many cases, doctors may only suggest one trial, if any, leaving patients and their families to do their own research for additional options.

Triomics: A New Hope with AI

Founded by Sarim Khan, a former MIT biotech researcher, and Hrituraj Singh, an AI scientist previously with Adobe, Triomics aims to tackle these issues head-on. The duo, friends since their college days, launched the startup in 2021 after recognizing how advances in generative AI and large language models (LLMs) could revolutionize the process of trial matching.

Triomics developed a specialized LLM, OncoLLM, designed to integrate seamlessly with the electronic health records (EHR) systems used by cancer centers and oncology departments. This AI-driven tool can rapidly parse through a patient’s medical history and match them with suitable clinical trials in minutes—a task that previously took several hours.

Table: Impact of OncoLLM on Clinical Trial Matching

Expanding Impact and Market Growth

Since its inception, Triomics has seen significant adoption of its technology. Six cancer centers and hospitals are currently using or piloting OncoLLM, with plans to double this number by the end of the year. This expansion has been supported by a robust $15 million Series A funding round from prominent investors including Lightspeed, Nexus Venture Partners, General Catalyst, and Y Combinator.

Triomics is not merely a clinical trials company. The data processed by OncoLLM can also assist medical staff in preparing for patient visits and submitting detailed cancer progression reports to state regulatory agencies. The potential applications of Triomics’ technology extend beyond just clinical trials, touching various aspects of oncological care.

Competing in a Crowded Field

Triomics is not alone in the AI-driven clinical trial matching space. Other startups like Deep 6 AI, QuantHealth, and Trajectory are also developing technologies to improve the efficiency and effectiveness of trial matching. However, Khan believes that Triomics sets itself apart by processing extensive datasets specifically tailored for cancer centers.

Key Points

• Low Enrollment Rates: Despite the availability of numerous clinical trials, enrollment rates among eligible cancer patients remain low.
• Time Constraints: Oncologists and medical staff face significant time constraints, making it difficult to stay informed about available trials.
• AI Solutions: Triomics utilizes generative AI to dramatically reduce the time required to match patients with clinical trials.
• Broader Applications: Beyond trial matching, Triomics’ technology helps in preparing for patient visits and regulatory reporting.

In conclusion, Triomics represents a significant step forward in leveraging AI to address long-standing inefficiencies in clinical trial enrollment. By reducing the time and effort required to match patients with trials, Triomics not only enhances the accessibility of potentially life-saving treatments but also paves the way for a more responsive and patient-centric approach to cancer care.