WASHINGTON, D.C. – The House Judiciary Subcommittee on Crime and Federal Government Surveillance will hold a hearing on Wednesday, July 16, 2025, at 10:00 a.m. ET. The hearing, “Artificial Intelligence and Criminal Exploitation: A New Era of Risk,” will examine the growing threat of Artificial Intelligence (AI)-enabled crime, including how criminals are leveraging AI to conduct fraud, identity theft, child exploitation, and other illicit activities. It will also explore the capabilities and limitations of law enforcement in addressing these evolving threats, as well as potential legislative and policy responses to ensure public safety in the age of AI.

WITNESSES: 

• LTC Andrew Bowne, Former Counsel, Department of the Air Force Artificial Intelligence Accelerator at the Massachusetts Institute of Technology
• Ari Redbord, Global Head of Policy, TRM Labs;  former Assistant United States Attorney
• Zara Perumal, Co-Founder, Overwatch Data; former member, Threat Analysis Department, Google

Marine scientists have long marveled at how animals like fish and seals swim so efficiently despite having different shapes. Their bodies are optimized for efficient, hydrodynamic aquatic navigation so they can exert minimal energy when traveling long distances.

Autonomous vehicles can drift through the ocean in a similar way, collecting data about vast underwater environments. However, the shapes of these gliding machines are less diverse than what we find in marine life — go-to designs often resemble tubes or torpedoes, since they’re fairly hydrodynamic as well. Plus, testing new builds requires lots of real-world trial-and-error.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the University of Wisconsin at Madison propose that AI could help us explore uncharted glider designs more conveniently. Their method uses machine learning to test different 3D designs in a physics simulator, then molds them into more hydrodynamic shapes. The resulting model can be fabricated via a 3D printer using significantly less energy than hand-made ones.

The MIT scientists say that this design pipeline could create new, more efficient machines that help oceanographers measure water temperature and salt levels, gather more detailed insights about currents, and monitor the impacts of climate change. The team demonstrated this potential by producing two gliders roughly the size of a boogie board: a two-winged machine resembling an airplane, and a unique, four-winged object resembling a flat fish with four fins.

Peter Yichen Chen, MIT CSAIL postdoc and co-lead researcher on the project, notes that these designs are just a few of the novel shapes his team’s approach can generate. “We’ve developed a semi-automated process that can help us test unconventional designs that would be very taxing for humans to design,” he says. “This level of shape diversity hasn’t been explored previously, so most of these designs haven’t been tested in the real world.”

But how did AI come up with these ideas in the first place? First, the researchers found 3D models of over 20 conventional sea exploration shapes, such as submarines, whales, manta rays, and sharks. Then, they enclosed these models in “deformation cages” that map out different articulation points that the researchers pulled around to create new shapes.

The CSAIL-led team built a dataset of conventional and deformed shapes before simulating how they would perform at different “angles-of-attack” — the direction a vessel will tilt as it glides through the water. For example, a swimmer may want to dive at a -30 degree angle to retrieve an item from a pool.

These diverse shapes and angles of attack were then used as inputs for a neural network that essentially anticipates how efficiently a glider shape will perform at particular angles and optimizes it as needed.

Giving gliding robots a lift

The team’s neural network simulates how a particular glider would react to underwater physics, aiming to capture how it moves forward and the force that drags against it. The goal: find the best lift-to-drag ratio, representing how much the glider is being held up compared to how much it’s being held back. The higher the ratio, the more efficiently the vehicle travels; the lower it is, the more the glider will slow down during its voyage.

Lift-to-drag ratios are key for flying planes: At takeoff, you want to maximize lift to ensure it can glide well against wind currents, and when landing, you need sufficient force to drag it to a full stop.

Niklas Hagemann, an MIT graduate student in architecture and CSAIL affiliate, notes that this ratio is just as useful if you want a similar gliding motion in the ocean.

“Our pipeline modifies glider shapes to find the best lift-to-drag ratio, optimizing its performance underwater,” says Hagemann, who is also a co-lead author on a paper that was presented at the International Conference on Robotics and Automation in June. “You can then export the top-performing designs so they can be 3D-printed.”

Going for a quick glide

While their AI pipeline seemed realistic, the researchers needed to ensure its predictions about glider performance were accurate by experimenting in more lifelike environments.

They first fabricated their two-wing design as a scaled-down vehicle resembling a paper airplane. This glider was taken to MIT’s Wright Brothers Wind Tunnel, an indoor space with fans that simulate wind flow. Placed at different angles, the glider’s predicted lift-to-drag ratio was only about 5 percent higher on average than the ones recorded in the wind experiments — a small difference between simulation and reality.

A digital evaluation involving a visual, more complex physics simulator also supported the notion that the AI pipeline made fairly accurate predictions about how the gliders would move. It visualized how these machines would descend in 3D.

To truly evaluate these gliders in the real world, though, the team needed to see how their devices would fare underwater. They printed two designs that performed the best at specific points-of-attack for this test: a jet-like device at 9 degrees and the four-wing vehicle at 30 degrees.

Both shapes were fabricated in a 3D printer as hollow shells with small holes that flood when fully submerged. This lightweight design makes the vehicle easier to handle outside of the water and requires less material to be fabricated. The researchers placed a tube-like device inside these shell coverings, which housed a range of hardware, including a pump to change the glider’s buoyancy, a mass shifter (a device that controls the machine’s angle-of-attack), and electronic components.

Each design outperformed a handmade torpedo-shaped glider by moving more efficiently across a pool. With higher lift-to-drag ratios than their counterpart, both AI-driven machines exerted less energy, similar to the effortless ways marine animals navigate the oceans.

As much as the project is an encouraging step forward for glider design, the researchers are looking to narrow the gap between simulation and real-world performance. They are also hoping to develop machines that can react to sudden changes in currents, making the gliders more adaptable to seas and oceans.

Chen adds that the team is looking to explore new types of shapes, particularly thinner glider designs. They intend to make their framework faster, perhaps bolstering it with new features that enable more customization, maneuverability, or even the creation of miniature vehicles.

Chen and Hagemann co-led research on this project with OpenAI researcher Pingchuan Ma SM ’23, PhD ’25. They authored the paper with Wei Wang, a University of Wisconsin at Madison assistant professor and recent CSAIL postdoc; John Romanishin ’12, SM ’18, PhD ’23; and two MIT professors and CSAIL members: lab director Daniela Rus and senior author Wojciech Matusik. Their work was supported, in part, by a Defense Advanced Research Projects Agency (DARPA) grant and the MIT-GIST Program.

According to DelveInsight’s analysis, The demand for Artificial Intelligence in clinical trials is experiencing strong growth, primarily driven by the rising global prevalence of chronic conditions like diabetes, cardiovascular diseases, respiratory illnesses, and cancer. This growth is further supported by increased investments and funding dedicated to advancing drug discovery and development efforts. Additionally, the growing number of strategic collaborations and partnerships among pharmaceutical, biotechnology, and medical device companies is significantly boosting the adoption of AI-driven solutions in clinical trials. Together, these factors are anticipated to fuel the expansion of the AI in the clinical trials market during the forecast period from 2025 to 2032.

DelveInsight’s “Artificial Intelligence (AI) in Clinical Trials Market Insights, Competitive Landscape and Market Forecast-2032” report provides the current and forecast market outlook, forthcoming device innovation, challenges, market drivers and barriers. The report also covers the major emerging products and key Artificial Intelligence (AI) in Clinical Trials companies actively working in the market.

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Artificial Intelligence (AI) in Clinical Trials Overview

Artificial Intelligence (AI) in clinical trials refers to the use of advanced machine learning algorithms and data analytics to streamline and improve various aspects of clinical research. AI enhances trial design, patient recruitment, site selection, and data analysis by identifying patterns and predicting outcomes. It enables faster patient matching, optimizes protocol design, reduces trial timelines, and improves data quality and monitoring. AI also helps in real-time adverse event detection and adaptive trial management, making clinical trials more efficient, cost-effective, and patient-centric.

DelveInsight Analysis: The global Artificial Intelligence in clinical trials market size was valued at USD 1,350.79 million in 2024 and is projected to expand at a CAGR of 12.04% during 2025-2032, reaching approximately USD 3,334.47 million by 2032.

Artificial Intelligence (AI) in Clinical Trials Market Insights

Geographically, North America is expected to lead the AI in the clinical trial market in 2024, driven by several critical factors. The region’s growing burden of chronic diseases, substantial investments in R&D, and the rising volume of clinical trials contribute significantly to this dominance. Additionally, an increasing number of collaborations and partnerships among pharmaceutical and medical device companies, along with the advancement of sophisticated AI solutions, are accelerating market expansion. These developments are enhancing the ability to manage complex clinical trials efficiently, driving the adoption of AI technologies and supporting the market’s growth in North America throughout the forecast period from 2025 to 2032.

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Recent Developments in the Artificial Intelligence (AI) in Clinical Trials Market Report

• In May 2025, Avant Technologies, Inc. (OTCQB: AVAI) and joint venture partner Ainnova Tech, Inc. announced the initiation of acquisition discussions aimed at enhancing their presence in the rapidly growing AI-powered healthcare sector.

• In March 2025, Suvoda introduced Sofia, an AI-driven assistant created to optimize clinical trial management processes. Sofia aids study teams by providing quick access to essential trial data and real-time, intelligent insights. This tool boosts operational efficiency, minimizes manual tasks, and helps teams make faster, data-informed decisions throughout the clinical trial journey.

• In December 2024, ConcertAI and NeoGenomics unveiled CTO-H, an advanced AI-powered software platform designed to enhance research analytics, clinical trial design, and operational efficiency. CTO-H provides an extensive research data ecosystem, offering comprehensive longitudinal patient data, deep biomarker insights, and scalable analytics to support more precise, efficient, and data-driven clinical development processes.

• In June 2024, Lokavant introduced SpectrumTM, the first AI-powered clinical trial feasibility solution aimed at enhancing trial performance throughout the clinical development process. Spectrum enables study teams to forecast, control, and improve trial timelines and expenses in real-time.

• Thus, owing to such developments in the market, rapid growth will be observed in the Artificial Intelligence (AI) in Clinical Trials market during the forecast period

Key Players in the Artificial Intelligence (AI) in Clinical Trials Market

Some of the key market players operating in the Artificial Intelligence (AI) in Clinical Trials market include- TEMPUS, NetraMark, ConcertAI, AiCure, Medpace, Inc., ICON plc, Charles River Laboratories, Dassault Systèmes, Oracle, Certara, Cytel Inc., Phesi, DeepHealth, Unlearn.ai, Inc., H1, TrialX, Suvoda LLC, Risklick, Lokavant, Research Solutions, and others.

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Analysis on the Artificial Intelligence (AI) in Clinical Trials Market Landscape

To meet the growing needs of clinical trials, leading companies in the AI in Clinical Trials market are creating advanced AI solutions aimed at improving trial efficiency, optimizing patient recruitment, and enhancing clinical trial design at investigator sites. For example, in April 2023, ConcertAI introduced CTO 2.0, a clinical trial optimization platform that utilizes publicly available data and partner insights to deliver comprehensive site and physician-level trial data. This tool provides key operational metrics and site profiles to evaluate trial performance and site capabilities. Additionally, CTO 2.0 assists sponsors in complying with FDA requirements for inclusive trial outcomes, promoting a shift toward community-based trials with more streamlined and patient-centric designs.

As a result of these advancements, the software segment is projected to experience significant growth throughout the forecast period, contributing to the overall expansion of the AI in the clinical trials market.

Scope of the Artificial Intelligence (AI) in Clinical Trials Market Report

• Coverage: Global

• Study Period: 2022-2032

• Artificial Intelligence (AI) in Clinical Trials Market Segmentation By Product Type: Software and Services

• Artificial Intelligence (AI) in Clinical Trials Market Segmentation By Technology Type: Machine Learning (ML), Natural Language Processing (NLP), and Others

• Artificial Intelligence (AI) in Clinical Trials Market Segmentation By Application Type: Clinical Trial Design & Optimization, Patient Identification & Recruitment, Site Identification & Trial Monitoring, and Others

• Artificial Intelligence (AI) in Clinical Trials Market Segmentation By Therapeutic Area: Oncology, Cardiology, Neurology, Infectious Disease, Immunology, and Others

• Artificial Intelligence (AI) in Clinical Trials Market Segmentation By End-User: Pharmaceutical & Biotechnology Companies and Medical Device Companies

• Artificial Intelligence (AI) in Clinical Trials Market Segmentation By Geography: North America, Europe, Asia-Pacific, and Rest of the World

• Key Artificial Intelligence (AI) in Clinical Trials Companies: TEMPUS, NetraMark, ConcertAI, AiCure, Medpace, Inc., ICON plc, Charles River Laboratories, Dassault Systèmes, Oracle, Certara, Cytel Inc., Phesi, DeepHealth, Unlearn.ai, Inc., H1, TrialX, Suvoda LLC, Risklick, Lokavant, Research Solutions, and others

• Porter’s Five Forces Analysis, Product Profiles, Case Studies, KOL’s Views, Analyst’s View

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Table of Contents

1 Artificial Intelligence (AI) in Clinical Trials Market Report Introduction

2 Artificial Intelligence (AI) in Clinical Trials Market Executive summary

3 Regulatory and Patent Analysis

4 Artificial Intelligence (AI) in Clinical Trials Market Key Factors Analysis

5 Porter’s Five Forces Analysis

6 COVID-19 Impact Analysis on Artificial Intelligence (AI) in Clinical Trials Market

7 Artificial Intelligence (AI) in Clinical Trials Market Layout

8 Global Company Share Analysis – Key Artificial Intelligence (AI) in Clinical Trials Companies

9 Company and Product Profiles

10 Project Approach

11 Artificial Intelligence (AI) in Clinical Trials Market Drivers

12 Artificial Intelligence (AI) in Clinical Trials Market Barriers

13 About DelveInsight

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This release was published on openPR.