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Leveraging explainable artificial intelligence for early detection and mitigation of cyber threat in large-scale network environments

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This work introduces a novel LXAIDM-CTLSN method. The aim is to detect and classify cyberattacks to achieve cybersecurity. The LXAIDM-CTLSN model encompasses processes such as data normalization, MOA-based feature selection, SDAE-based cyberthreat detection, HOA-based parameter selection, and the LIME process, shown in Fig. 2. The novelty of the LXAIDM-CTLSN model is in its integrated and customized framework that integrates min-max normalization, MOA-based feature selection, and SDAE-based cyberattack detection. Enhanced further by the HOA model for hyperparameter tuning and LIME for explainable threat classification, this comprehensive approach outperforms conventional methods in both performance and interpretability.

Fig. 2

Overall process of LXAIDM-CTLSN method.

Data normalization

In the initial phase, the LXAIDM-CTLSN method performs data normalization using the Min-max normalization technique34. Min-max normalization is a data preprocessing method that converts features into the typical scale, usually from zero to one. Based on cyberattacks, min-max normalization standardizes several attack indicators, namely response times, frequency of attacks, and severity scores. This is vital for successful threat comparison and detection, allowing cybersecurity systems to detect anomalies and patterns accurately. Data normalization makes it easy to analyze and integrate data from numerous sources, augmenting the performance of threat detection systems.

MOA-based feature selection

The MOA is used for the feature selection method, which sequentially decreases the computational complexity35. This model is chosen due to its efficiency in mitigating computational complexity while maintaining high accuracy. Unlike conventional methods, MOA can effectually navigate large and intrinsic datasets by selecting the most relevant features, thus enhancing the overall model performance. Its behaviour of replicating the mayfly’s swarm behaviour allows it to explore the feature space effectively, avoiding the risk of local optima. Also, MOA is computationally less expensive than other optimization techniques, making it more appropriate for real-time applications. By mitigating the number of features, MOA not only speeds up the training process but also assists in mitigating the risk of overfitting, ensuring a more robust model. This makes it an ideal choice for handling large-scale datasets in cybersecurity applications. Figure 3 illustrates the steps involved in the MOA technique.

Fig. 3
figure 3

Steps involved in the MOA model.

This study discusses the usage of MOA as an optimization algorithm. A comparative study was conducted using PSO and FA with a similar objective function to evaluate the effectiveness of the MOA model. This study sheds light on the mayfly (MF) species, well-known for its extraordinarily short twenty-four‐hour lifespan. The researcher workers witnessed a difference between female and male MFs within flocks; due to natural force differences, male MFs constantly show higher optimization levels than female MFs. These characteristics resemble PSO, where individuals in the PSO, similar to MFs, update position \(X\left(t\right)\) and velocity \(v\left(t\right)\) according to the present state.

(1) The actions of male mayflies

In the context of MOA, males change their location according to their individual speed. \({x}_{i}\) refers to the area of the \({i}^{th}\) male MFs at \({t}^{th}\) existing time step in the search range.

$${x}_{i}\left(t+1\right)={x}_{i}\left(t\right)+{v}_{i}\left(t+1\right)$$

(1)

The male MF actively engages in exploration and exploitation duties at the initial iteration. During velocity updating, the MF considers the existing fitness values as \(\left({x}_{i}\right)\), and\(f\left({x}_{hi}\right)\) is the better fitness value witnessed on its trajectory in the past. The males alter the velocity if \((X)\)is higher than \(\left({x}_{hi}\right)\). This can be defined by three major factors: its existing speed, the separation between its exiting and the best locations, and the best trajectory in the past. Also, it allows the males to enhance their movement strategy once they observe fitness progress.

$${v}_{i}\left(t+1\right)=g.{v}_{i}\left(t\right)+{a}_{i}{e}^{-\beta{r}_{p}^{2}}\left[{X}_{hi}-{x}_{i}\left(t\right)\right]+a2{e}^{-\beta{r}_{g}^{2}}\left[{x}_{g}-{x}_{i}\left(t\right)\right]$$

(2)

The linear descent of the variable \(g\) from maximum to minimum values is directed by weight parameters \(a1,a2\), and \(\beta\). The \({r}_{p}\) and \({r}_{g}\) are variables that evaluate the Cartesian distance between prior best placements and individuals. The second norm is calculated for the distance array in Cartesian space. The distances between individuals and the past best location within the swarm are as follows:

$$\left|\left|{x}_{i}-{x}_{j}\right|\right|=\sqrt{{\sum}_{k=1}^{n}({x}_{ik}-{x}_{jk}{)}^{2}}$$

(3)

The male MF uses a random dance coefficient, represented by ‘\(d,\)‘ to update the velocity from the present value once the fitness values \(f\left({x}_{i}\right)\) are less than \(f\left({x}_{hi}\right)\). \({r}_{i}\) is a uniform distribution random value in [1,1].

$${v}_{i}(t+1)=g\left({v}_{i}\right(t)+d.{r}_{i}$$

(4)

(2) The actions of female mayflies

Compared to male MFs Female, MFs show different behaviours. They actively find males for the breeding purpose rather than congregating. Consider that it is the existing location of the females in the search range at \(t\) time; its location can be changed by adding the speed \({v}_{i}\left(t1\right)\) to the present location as follows:

$${y}_{i}\left(t+1\right)={y}_{i}\left(t\right)+{v}_{i}\left(t+1\right)$$

(5)

Different techniques are used to update females’ speed. Wingless females usually have a lifespan of 1–7 days. They exhibit a sense of urgency in locating males for breeding and mating. In response to the actions and characteristics of the chosen male MF, the females adapt their velocity.

When \(\left({y}_{i}\right)>f\left({x}_{i}\right)\), then the \({i}^{th}\) female MF utilizes Eq. (6) for updating the velocity. Here, the speed can be adjusted by the further constant, \({a}_{3}\), and \({r}_{m}\)is the Cartesian distance between them.

$${v}_{i}\left(t+1\right)=g.{v}_{i}\left(t\right)+{a}_{3}{e}^{-\beta{r}_{mf}^{2}}\left[{x}_{i}\left(t\right)-{y}_{i}\left(t\right)\right]$$

(6)

If \(f\left({y}_{i}\right), then females alter the velocity speed using a random dance coefficient represented by \(fl\). \({r}_{2}\) is a variable representing a random number within [\(\text{1,1}\)].

$${v}_{i}\left(t\right)=g.{v}_{i}\left(t\right)+fl.{r}_{2}$$

(7)

(3) Mayflies mating

Most male and female MFs participate in mating, which leads to offspring production. They inherit qualities from their parents and undergo random evolutionary changes where \(L\) is the set of random numbers from the Gaussian distribution.

$$Offspring1=L\text{*}male+\left(1-L\right)*female$$

(8)

$$offspring2=L\text{*}female+\left(1-L\right)\text{*}male$$

(9)

(4) Mayflies variation

To overcome the premature convergence issues, the optimum value might be a local optimum instead of a global one, so a uniform distribution random number is introduced into the mutation process for offspring MFs. This can be mathematically modelled as follows:

$$\text{o}ffsprin{g}_{n}=\text{o}ffsprin{g}_{n}+\sigma.N\left(\text{0,1}\right)$$

(10)

In Eq. (10), \(\sigma\) indicates the standard deviation. \(N(0,1)\) is a uniform distribution with a mean of \(0\) and variance of \(1\). The mutant individual is estimated to be around 5% of the male MFs, rounded to the near whole number.

The FF considers the classifier outcomes and the number of features chosen. It reduces the classifier performance and the size of the selected features. Therefore, the succeeding FF is used to assess the individual solution.

$$Fitness=\alpha*ErrorRate+\left(1-\alpha\right)*\frac{\#SF}{\#All\_F}$$

(11)

In Eq. (11), \(ErrorRate\) denotes the classifier error rate. \(ErrorRate\) is evaluated as the amount of improper classification to the amount of classifier made within [0,1]. \(\#SF\) refers to the number of features chosen, and \(\#All\_F\) denotes the overall number of attributes in the dataset. \(\alpha\) controls the prominence of classifier quality and subset length.

Cyberthreat detection using SDAE

Next, the SDAE technique recognizes and classifies cyber threats36. This model is chosen because it can learn robust feature representations while effectually denoising input data. Unlike conventional models, SDAE can handle noisy and incomplete data, making it ideal for real-world cybersecurity scenarios where data may be corrupted or sparse. Its sparse structure forces the network to concentrate on the most significant features, improving its generalization and ability to detect complex threats. Additionally, SDAE is highly effective in detecting subtle cyberattack patterns, which other models may overlook. The method’s capability to learn hierarchical features from raw data ensures it can detect known and novel threats. Moreover, its unsupervised learning capability reduces the requirement for large labelled datasets, making it more flexible and scalable for various cybersecurity tasks. Figure 4 represents the architecture of SDAE.

Fig. 4
figure 4

An AE contains a decoder and an encoder where the encoder draws higher-dimension input instances to the lower‐dimension abstract representation to attain sample density and decrease dimensionality. At the same time, the decoder transforms the lower‐dimension demonstration into the predictable output for achieving the representation of an input. An AE displays effective non-linear extraction of feature capability that can get feature vectors, signify the structure of input data, and cover non-linear features.

The procedure of encoder and decoder as defined below:

$$h=f\left({W}_{1}x+{\lambda}_{1}\right)$$

(12)

$$y=g\left({W}_{2}x+{\lambda}_{2}\right)$$

(13)

Whereas \(y\) and \(x\) signify the output and input data, respectively; \(h\) signifies the reduction of dimensionality; \({W}_{1}\) and \({W}_{2}\) epitomize the weights of the encoder and decoder networks; \({\lambda}_{1}\) and \({\lambda}_{2}\) signify the unit bias of the output layer and the hidden layer (HL); and \(f\) and \(g\) represent the activation functions.

When equated beside input data, the \(h\) dimension decreases, but it still covers the key data of an input. Analyzing and processing \(h\) decreases the computational cost. AE feature extraction permits dealing with non-linear data constructs like load curves, which are shown in real-world use.

To rebuild an input, the objective of AE is to minimize the error of reconstruction that defines the intimacy between output and input. The error of reconstruction \({L}_{AE}\) is definite as below

$${L}_{AE}=\frac{1}{n}{\sum}_{i=1}^{n}({x}_{i}-{y}_{i}{)}^{2}$$

(14)

The enhancement of AE is achieved by including restraints that are dissimilar to \({L}_{AE}\). This presents noise coding and sparse constraint, which is known as SDAE. The sparse constraint denotes the defeat of a few neurons in HL to recover the computational efficacy and network speed and express higher-dimension data; therefore, the NN can yet remove the feature and form of the instance when there are numerous neurons in HL. The noise coding denotes including noise in an input dataset to improve the sturdiness of AE and allow the absorption of the vital features of input data.

Let’s assume \({a}_{h}^{\left(2\right)}\left({x}^{i}\right)\) signifies the neuron activation degree in \(h\); the \(\wedge\rho\wedge\rho\) of neuron on every training sample have been expressed below:

$$\rho=\frac{1}{m}{\sum}_{i=1}^{m}[{a}_{h}^{\left(2\right)}\left({x}^{i}\right)$$

(15)

The divergence of KL is applied to assess the sparsity of neurons. Its formulation is expressed below:

$$\:\sum\:KL(\rho\:\Vert\:\widehat{\rho\:})=\sum\:\left[\rho\:\log \widehat{\rho\:}+\left(1-\rho\:\right)\log\frac{1-\rho\:}{1-\widehat{\rho\:}}\right]$$

(16)

Here, \(\rho\) refers to a parameter of sparsity, which is near to \(0.\)

The noised input \(\wedge x \wedge x\) was produced by inserting noise at random into \(\chi\) initial input, and the equivalent output is \(\wedge\:y\wedge\:y.\) If \(\wedge\:y\wedge\:y\) generates \(x\) to the highest degree, it specifies that AE holds effectual strength. The \({L}_{DAE}\) of denoising AE is expressed below

$${L}_{DAE}=\frac{1}{n}\sum\limits_{i=1}^{n}({x}_{i}-{y}_{i})^{2}+\frac{\lambda}{2}\left(\Vert{W}_{1}^{2}{\Vert}_{F}^{2}+{\Vert}{W}_{2}^{2}{\Vert}_{F}^{2}\right)$$

(17)

Here, \(\lambda\) denotes the constraint of noise weight.

Finally, \({L}_{SDAE}\) of SDAE is modified as

$${L}_{SDAE}=\frac{1}{n}{\sum}_{i=1}^{n}({x}_{i}-{\widehat{y}}_{i}{)}^{2}+\beta{\sum}_{m=1}^{n}KL(\rho{\Vert}\rho)+\frac{\lambda}{2}(\Vert{W}_{1}^{2}{\Vert}_{F}^{2}+{\Vert}{W}_{2}^{2}{\Vert}_{F}^{2})$$

(18)

Here, \(\beta\) refers to the weight co-efficient of the sparsity penalty.

The SDAE is made by minimalizing the loss function of Eq. (18) over the gradient descent method.

Parameter optimizer

This work employs the HOA method for fine-tuning the hyperparameter included in the SDAE approach37. This methodology is chosen because it can effectively explore and exploit the solution space. HOA replicates the behaviour of hikers seeking the highest peak, effectively fine-tuning hyperparameters to improve the model’s performance. Unlike other optimization techniques, HOA strikes a balance between exploration and exploitation, preventing premature convergence and improving the search for optimal solutions. Its simplicity and adaptability allow it to work well with complex models and massive datasets, making it appropriate for various applications, including cybersecurity. Additionally, HOA is computationally efficient, which assists in real-time optimization tasks, reducing the time required for hyperparameter tuning. This makes it a robust choice for improving the accuracy and robustness of ML models. Figure 5 specifies the architecture of the HOA model.

Fig. 5
figure 5

Workflow of the HOA technique.

This section discusses the mathematical background and inspiration of HOA. In addition, the study defines the HOA technique and its computation difficulty. The HOA is inspired by the hiker’s experience attempting to summit mountain rocks, hills, or peaks.

High, steep trails and terrains decelerate the hikers and eventually increase the hike.

Hikers can estimate or determine the time taken to reach the peak equipped with an awareness of the terrain’s geography. This is similar to the agent searching for the global or local optima of the optimization problems. Furthermore, in the search for global optima, the agent becomes stuck in the search area due to its complication of optimization problems, and this might extend the time it will take to locate the global optima, which is much like how hikers experience during hiking.

The mathematical background of HOA is inspired by Tobler’s Hiking Function (THF), an exponential equation that defines a hiker’s speed, which considers the slope or steepness of the trail or terrain.

The THF is represented as follows:

$${\mathcal{W}}_{i,t}=6{e}^{-35|{S}_{jt}+005|}$$

(19)

In Eq. (19), \({\mathcal{W}}_{i,t}\) refers to hiker \(j\) velocity \((\)viz., \(km/h)\) at \({t}^{th}\) time or iteration, and \({S}_{i,t}\) denotes the slope of the trail or terrain.

$${S}_{i,t}=\frac{dh}{dx}=\text{t}\text{a}\text{n}{\theta}_{i,t},$$

(20)

In Eq. (20), \(dh\) and \(dx\) are the elevation variance and the hiker’s travel distances. \({\theta}_{i,t}\), refers to the inclination angle of the terrain or trail in \(\left[\text{0,5}{0}^{o}\right].\)

HOA exploits the advantages of the hiker’s social thinking and the cognitive capabilities of hiker individuals. The THF, the lead hiker location, and the actual hiker location define the actual or updated hiker’s velocity of initial velocity. Therefore, the present velocity of \(\:{j}^{th}\) hikers is as follows:

$$\:{\mathcal{W}}_{i,t}={\mathcal{W}}_{i,t-1}+{\gamma\:}_{i,t}\left({\beta\:}_{best}-{\alpha\:}_{i,t}{\beta\:}_{i,t}\right),$$

(21)

In Eq. (21), \(\:{\gamma\:}_{i,t}\) denotes a uniform distribution number in [0,1]; \(\:{\mathcal{W}}_{i,t}\) and \(\:{\mathcal{W}}_{i,t-1}\) are the present and initial velocities of the \(\:{j}^{th}\) hikers. \(\:{\beta\:}_{best}\) indicates the location of the lead hiker and \(\:{\alpha\:}_{i,t}\) shows the sweep factor (SF) of \(\:{j}^{th}\) hikers within [1,3]. The SF confirms that the hiker doesn’t stray far from the leader hikers; hence, they can perceive where the lead hikers are headed and receive signals from them.

The updated position \(\:{\beta\:}_{i,t+1}\) of hiker \(\:j\) is given by considering the hiker velocity in Eq. (19):

$$\:{\beta\:}_{i,t+1}={\beta\:}_{i,t}+{W}_{i,t}$$

(22)

In metaheuristic algorithms such as the HOA, the agent’s initial position is an essential factor that considerably affects the possibility of a feasible solution and the speed at which convergence is obtained. The HOA performs the random initialization method to initialize the agent location, although alternate methods, such as problem-specific initialization or heuristic-based approaches, also exist.

The initialization of hiker locations \(\:{\beta\:}_{i,t}\) can be defined by the \(\:{\varphi\:}_{j}^{1}\) and \(\:{\varphi\:}_{j}^{2}\) lower and upper bounds of the solutions as follows:

$$\:{\beta\:}_{i,t}={\varphi\:}_{j}^{1}+{\delta\:}_{j}\left({\varphi\:}_{j}^{2}-{\varphi\:}_{j}^{1}\right),$$

(23)

In Eq. (23), \(\:{\delta\:}_{j}\) refers to a uniform distribution integer in \(\:\left[\text{0,1}\right].\) \(\:{\varphi\:}_{j}^{1}\) and \(\:{\varphi\:}_{j}^{2}\) are the lower and upper boundaries of the \(\:{j}^{th}\) parameter. This profoundly affects the distance between the lead trails and other hikers. Moreover, the trail’s slope influences hikers’ velocity and the HOA’s exploitative and exploratory behaviours.

Increasing the SF range encourages an exploitation stage within the HOA. On the other hand, once the SF range is decreased, the HOA leans towards an exploratory stage. In addition, reducing the trail’s inclination angle tends to lead to the exploitation stage. This factor collaborates to shape the HOA’s performance and behaviour in resolving optimization problems.

The HOA is used to derive an FF to attain enhanced classifier outcomes. It defines a positive integer to characterize the better outcome of the candidate solution. Now, reducing the classifier error rate is regarded as an FF.

$$\:fitness\left({x}_{i}\right)=ClassifierErrorRate\left({x}_{i}\right)\:=\frac{No.\:of\:misclassified\:samples}{Total\:No.\:of\:samples}\times\:100$$

(24)

Model explanation of XAI using LIME

Finally, the LXAIDM-CTLSN method incorporates the XAI method LIME for the optimum understanding and explainability of the Blackbox technique for superior classification of cyberattacks. Interpretability is selected in cyber threat detection by combining LIME to improve the model’s accuracy38. LIME delivers clarifications for distinct predictions prepared by the method, presenting insights into the features donating to every identification decision.

LIME aids in understanding the model, delivering insights into the forecasts prepared by an assumed method. By interpreting the model’s reasoning, consumers can get assurance in its predictions. LIME attains this by clarifying distinct instances, which assess the model performance. The LIME formulation is shown in Eq. (25), with goals to minimize the loss function \(\:L\:\)while ensuring that the clarification closely looks like the original behaviour of the model. Where \(\:\varphi\:\left(x\right)\) denotes the clarification, for instance, \(\:x\) produced by the method \(\:\theta\:.\)

$$\:\phi\:(xf=\alpha\:rgmi{n}_{t}[L\left({\theta\:}_{t}\left(x\right),g\right)+\varOmega\:\left({\phi\:}_{t}\right)$$

(25)

Here, \(\:\theta\:\) signifies the interpretable method in the class G. \(\:g\) denotes the family of methods. \(\:\psi\:\left(x\right)\) represents the proximity evaluation of the neighbourhood employed to make the description, for instance. \(\:\varOmega\:\left({\phi\:}_{t}\right)\) denotes the complication of the model, just like the amount of features included. \(\:P\) signifies the possibility of \(\:x\:\)thatbelongs to an exact class.



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Artificial Intelligence and Criminal Exploitation: A New Era of Risk

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July 9, 2025

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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



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AI shapes autonomous underwater “gliders” | MIT News

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July 9, 2025

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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.



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Global Artificial Intelligence (AI) in Clinical Trials Market

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July 9, 2025

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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.

To know more about why North America is leading the market growth in the Artificial Intelligence (AI) in Clinical Trials market, get a snapshot of the report Artificial Intelligence (AI) in Clinical Trials Market Trends

https://www.delveinsight.com/sample-request/ai-in-clinical-trials-market?utm_source=openpr&utm_medium=pressrelease&utm_campaign=gpr

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.

To read more about the latest highlights related to Artificial Intelligence (AI) in Clinical Trials, get a snapshot of the key highlights entailed in the Artificial Intelligence (AI) in Clinical Trials

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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.

Which MedTech key players in the Artificial Intelligence (AI) in Clinical Trials market are set to emerge as the trendsetter explore @ Key Artificial Intelligence (AI) in Clinical Trials Companies

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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

Interested in knowing how the Artificial Intelligence (AI) in Clinical Trials market will grow by 2032? Click to get a snapshot of the Artificial Intelligence (AI) in Clinical Trials Market Analysis

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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.



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