# From Klebsiella and Candida to artificial intelligence: a perspective from infectious diseases doctors and researchers

Daniele Roberto Giacobbe, Cristina Marelli, Marco Muccio, Sabrina

Guastavino, Ylenia Murgia, Sara Mora, Alessio Signori, Nicola Rosso, Antonio

Vena, Mauro Giacomini, et al.

► To cite this version:

Daniele Roberto Giacobbe, Cristina Marelli, Marco Muccio, Sabrina Guastavino, Ylenia Murgia, et al. From Klebsiella and Candida to artificial intelligence: a perspective from infectious diseases doctors and researchers. Frontiers in Medicine, 2025, 12, 10.3389/fmed.2025.1676920. inserm-05418525

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EDITED BY
Shisan (Bob) Bao,
The University of Sydney, Australia

REVIEWED BY
Virna Maria Stavros Tsitou,
Medical University of Sofia, Bulgaria

*CORRESPONDENCE
Daniele Roberto Giacobbe
✉ danieleroberto.giacobbe@unige.it

RECEIVED 31 July 2025
ACCEPTED 16 September 2025
PUBLISHED 29 September 2025

**CITATION**
Giacobbe DR, Marelli C, Muccio M, Guastavino S, Murgia Y, Mora S, Signori A, Rosso N, Vena A, Giacomini M, Campi C, Piana M and Bassetti M (2025) From *Klebsiella* and *Candida* to artificial intelligence: a perspective from infectious diseases doctors and researchers.
*Front. Med.* 12:1676920.
doi: 10.3389/fmed.2025.1676920

**COPYRIGHT**
© 2025 Giacobbe, Marelli, Muccio, Guastavino, Murgia, Mora, Signori, Rosso, Vena, Giacomini, Campi, Piana and Bassetti. This is an open-access article distributed under the terms of the [Creative Commons Attribution License (CC BY)](https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# From Klebsiella and Candida to artificial intelligence: a perspective from infectious diseases doctors and researchers

Daniele Roberto Giacobbe¹,²*, Cristina Marelli³,⁴,
Marco Muccio¹, Sabrina Guastavino⁵, Ylenia Murgia⁶,
Sara Mora⁷, Alessio Signori⁸,⁹, Nicola Rosso⁷, Antonio Vena¹,²,
Mauro Giacomini⁶, Cristina Campi⁵,¹⁰, Michele Piana⁵,¹⁰ and
Matteo Bassetti¹,²

¹UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy, ²Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy, ³Oncostat, CESP, Inserm U1018, Université Paris-Saclay, Labeled Ligue Contre le Cancer, Gustave Roussy, Villejuif, France, ⁴Institut Curie - INSERM U1331, Team Statistics Applied to Personalized Medicine, Paris, France, ⁵Department of Mathematics (DIMA), University of Genoa, Genoa, Italy, ⁶Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy, ⁷UO Information and Communication Technologies, IRCCS Ospedale Policlinico San Martino, Genoa, Italy, ⁸Section of Biostatistics, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy, ⁹IRCCS Ospedale Policlinico San Martino, Genoa, Italy, ¹⁰Life Science Computational Laboratory (LISCOMP), IRCCS Ospedale Policlinico San Martino, Genoa, Italy

The advent of artificial intelligence (AI) and machine learning (ML) is progressively influencing clinical reasoning in infectious diseases, particularly in the management of septic shock where timely empirical antimicrobial therapy is crucial. In this perspective, we discuss how AI and ML approaches intersect with established clinical decision-making processes through two examples from our research and practice: prediction of bloodstream infection by carbapenem-resistant *Klebsiella pneumoniae* and prediction of candidemia. Traditionally, risk estimation has relied on interpretable models such as logistic regression, offering clinicians transparent insights into the contribution of specific risk factors. In contrast, some ML models leverage complex relationships within large datasets. Despite expectations, in several cases these complex models have not consistently outperformed classical approaches yet, a phenomenon we refer to as the “accuracy paradox,” possibly stemming from limitations in data specificity and granularity. Furthermore, the opacity of many ML models still challenges their integration into clinical practice, raising ethical and practical concerns around explainability and trust. While explainable AI offers partial solutions, ML may also capture hidden patterns undetectable through classical reasoning that could be unexplainable to clinicians *per definition*. Achieving a reasonable and shared balance will require continued collaboration between clinicians, data scientists, and ethicists. As the field evolves, future research should prioritize the development of models that not only perform well but can also integrate meaningfully into the complex cognitive processes underpinning bedside clinical reasoning.

**KEYWORDS**

artificial intelligence, machine learning, prediction, invasive candidiasis, carbapenem resistance
# Introduction

As infectious disease specialists, part of our routine clinical work includes consultations for patients with septic shock across various hospital wards. In these patients, empirical antimicrobial therapy is typically initiated while awaiting blood culture results for etiological diagnosis, which may take up to 48–72 h. This practice reflects the deleterious prognostic impact of delaying effective therapy (1–5).

At the bedside, two crucial therapeutic considerations often arise in patients with septic shock in our hospital: (i) whether empirical antibacterial therapy should include coverage against carbapenem-resistant Gram-negative bacteria, and (ii) whether empirical antifungal therapy should be added in the suspicion of candidemia. Over recent decades, these considerations have become core to our clinical research interests (6–8). As a result, clinical reasoning and research interpretation have become increasingly intertwined, mutually enhancing and deepening our approaches to both fields.

Recently, the terms artificial intelligence (AI), referring to machines capable of performing human-like tasks, and machine learning (ML), a subset of AI referring to machines capable of learning a specific task from data without being programmed to do so, have gained prominence in the infection prediction literature, inevitably intersecting with our key areas of clinical practice and research (9–16). Importantly, this shift is not merely terminological; the principles underlying AI and ML raise new considerations, reshaping our clinical and research reasoning.

With the support of expert statisticians, mathematicians, and bioengineers, while maintaining a clinically oriented perspective, this brief perspective discusses how AI and ML are beginning to reshape our field. We illustrate these changes through two longstanding topics in our research and clinical practice that guide empirical therapeutic decisions in septic shock: prediction of bloodstream infection (BSI) by carbapenem-resistant Klebsiella pneumoniae (CRKP) and prediction of candidemia (i.e., BSI by Candida spp.).

# Clinical reasoning at the bedside for suspected CRKP BSI

When selecting empirical therapy for patients with severe acute conditions (e.g., septic shock) and suspected BSI, we carefully weigh the risk of CRKP etiology. If this risk is deemed negligible, empirical therapy without CRKP coverage is prescribed. Conversely, if the risk is appreciable, empirical therapy with CRKP coverage is initiated, pending blood culture results to adjust treatment accordingly (e.g., de-escalation to non-CRKP-covering regimens if other non-resistant organisms and/or carbapenem-susceptible Klebsiella pneumoniae are identified).

One might wonder whether it would be preferable to routinely start with CRKP-active regimens and de-escalate after blood cultures. While theoretically justifiable in consistently high-risk settings, such scenarios are rare. Routine use of broad CRKP coverage is usually unjustifiable from an antimicrobial stewardship perspective, as it fosters resistance development without clear survival benefit (17–23). Hence, accurately estimating CRKP risk is central to guiding empirical therapy.

# CRKP BSI risk prediction from classical models

Estimating CRKP risk highlights the connection between clinical practice and research. Risk estimation in real world practice relies on identifying risk factors derived from research studies, typically using logistic regression or other classical statistical models (24–27).

In univariable logistic regression, the association between a potential risk factor (e.g., prior hospitalization) and the event of interest (e.g., CRKP BSI) is quantified. In this example, prior hospitalization is the independent variable ($x_1$) and CRKP BSI is the dependent variable (y). A dataset comprising $x_1$ and y values from multiple previous patients is used to “train” the logistic regression model to predict y based on $x_1$. More in detail, training on the actual data in the dataset means logistic regression coefficients (intercept $\beta_0$ and slope $\beta_1$) are calculated through maximum likelihood estimation (28). Then, the probability (p) of CRKP BSI for any new patient based on the presence or not of previous hospitalization can be calculated through the following formula:

$$p = p(x_1) = P(y = 1 | x_1) = \sigma(\beta_0 + \beta_1 x_1) = \frac{1}{1 + e^{-(\beta_0 + \beta_1 x_1)}}$$

A linearized version via the logit function clarifies interpretation:

$$\operatorname{logit}(p) = \log\left(\frac{p}{1-p}\right) = \beta_0 + \beta_1 x_1$$

Indeed, exponentiating $\beta_1$ yields the odds ratio (OR) for $x_1$, interpreted as the odds of CRKP BSI in patients with vs. without prior hospitalization. Notably, although OR reflects association rather than causation, it provides clinicians with a quantifiable sense of risk.

Risk scores typically incorporate multiple variables and are derived not from univariable but from multivariable models (e.g., multivariable logistic regression) (28), with p depending on multiple independent variables. Consequently, multivariable logistic regression includes several independent variables ($x_1, ..., x_n$), each with its coefficient ($\beta_1, ..., \beta_n$), according to the following formula (linearized via the logit function):

$$\operatorname{logit}(p) = \beta_0 + \sum_{i=1}^{n} \beta_i x_i$$

For example, the Giannella score predicts CRKP BSI in colonized patients, after training of a multivariable logistic regression model led to the identification of predictive factors such as ICU admission (OR 1.65), abdominal procedures (OR 1.87), chemotherapy/radiation (OR 3.07), and additional colonization sites (OR 3.37 per site) (25). Such a classic approach of predicting CRKP BSI via logistic regression and presenting the results via OR is certainly not obsolete, as several similar studies from different parts of the world have been published in the last 12 months (29–34), with the persistence of this technique’s application over the years testifying to its usefulness for both researchers and clinicians.

Of note, $\beta$ coefficients are also called parameters or weights in ML terminology (we will continue using the terms $\beta$ coefficients throughout the manuscript for consistency).
# The advent of AI and ML in risk prediction: what is truly new?

Recent literature abounds with studies using ML models to predict severe infections (9–12, 14–16). A source of possible confusion for clinicians is that logistic regression is frequently included among ML techniques in these studies, and also introductory ML courses often begin with describing logistic regression as a ML model. Technically, this is valid: logistic regression models learn from prior $x = (x_1, ..., x_n)$ and y data to predict outcomes for new patients, meeting ML's definition (35).

Where, then, lies the difference between traditional logistic regression and the recent surge in ML-based predictions? One might assume that novel, more accurate models have emerged only recently, justifying the influx of studies. However, this is incorrect. ML models like neural networks (NNs) date back to the mid-20th century (36). What was truly lacking until a very few years ago was the computational power and data volume (“big data”) necessary to train complex ML models in a reasonable amount of time and without overfitting (i.e., when a model learns the training data too well, possibly including its noise and outliers, leading to poor performance on new, unseen data (37)).

# The example of neural networks and deep learning

With modern computational advances and data availability, it may be expected that more complex ML models could deliver more accurate risk predictions than classical models (e.g., logistic regression). For instance, NNs capture intricate feature interactions by combining them through intermediate layers. Conceptually, logistic regression resembles a two-layer NN (input and output), whereas NNs include at least one “hidden” layer (Figure 1). Multiple hidden layers constitute “deep learning,” reflecting the depth of learned associations (38).

Unlike logistic regression's two types of coefficients ($\beta_0$ and $\beta_{1...n}$ with n equal to the number of features), NNs involve other numerous additional trainable coefficients across hidden layers (Figure 1).

Although architectures vary across complex ML models other than NNs, their shared principle is capturing complex patterns through training of multiple additional $\beta$ coefficients potentially enhancing predictive accuracy.

If sufficient high-quality data are available to prevent overfitting, complex models should theoretically outperform simpler ones (e.g., logistic regression) in predicting infections and resistance, informing antimicrobial choices in septic shock. Yet, the reality is more nuanced, as discussed below.

# The accuracy paradox

Accuracy measures a model's ability to correctly classify true positives (TP) and true negatives (TN) (39):

$$Accuracy = \frac{TP + TN}{N}$$

Where N is the total number of observations, i.e., the sum of TP, TN, false negatives, and false positives.

In this regard, it is of nonetheless of note that another performance measure is commonly used to assess the model discriminatory ability for classification problems (due to technical reasons that are outside the scope of the present paper), the area under the curve (AUC). The AUC evaluates the model discriminatory ability across all thresholds, reflecting sensitivity-specificity trade-offs (39). Higher AUC indicates superior discrimination between positive and negative cases (Figure 2).

Recent ML infection prediction studies trained on very large samples of thousands of patients frequently report AUCs $\ge 0.6–0.7$ (11, 40, 41). However, we encourage scrutiny also of other performance metrics (e.g., sensitivity, specificity). Indeed, this may allow to note more intuitively that, despite training complex models on extensive datasets, their sensitivity and specificity often do not surpass—and sometimes underperform—those of classical models trained on smaller datasets (11, 24–27, 40, 41). Possible explanations vary (e.g., selection biases, suboptimal architectures). A key factor may nonetheless be features specificity and granularity:

<figure><img src="image_3.png" /><figcaption>FIGURE 1</figcaption></figure>

Comparison between: (a) multivariable logistic regression (2 layers), (b) neural network (just 2 hidden layers for concept illustration purposes). $x_1... x_n$: Input variables (features); +1: Bias node; $\beta_0$: Logistic regression intercept; $\beta_1... \beta_n$: Logistic regression coefficients (weights); $p(x) = P(y = 1|x)$: Predicted probability; x: Vector of input variables; $\sigma$: Sigmoid function; $\alpha_1^{(2)}... \alpha_n^{(2)}$: 1st hidden layer transformed features; $\alpha_1^{(3)}... \alpha_n^{(3)}$: 2nd hidden layer transformed features; $\beta_0... \beta_n$: NN final weights; $h_{w,b}$: NN prediction function (in which w represent the synaptic connection weights between layers, and b the bias terms). Figure created using Microsoft PowerPoint (Office 2016, Microsoft Corporation, Redmond, WA, USA).

<figure><img src="image_4.png" /></figure>
<figure><img src="image_5.png" /><figcaption>FIGURE 2
Model discrimination based on sensitivity-specificity trade-offs. Receiver Operating Characteristic (ROC) curves comparing True Positive Rate (TPR, sensitivity) and False Positive Rate (FPR, 1-specificity) across thresholds. Area Under the Curve (AUC): Highest = 1, Very high = 0.9, High = 0.8, Low = 0.6, Lowest = 0.5. Curves nearer the upper-left corner indicate better discrimination. Figure created using SAS software (version 9.4, SAS Institute Inc., Cary, NC, USA).</figcaption></figure>

large datasets often rely on administrative codes and routine, nonspecific laboratory results, whereas smaller datasets capture granular clinical details (e.g., invasive devices, acute conditions). Since manual collection of granular features from thousands to millions health records, as required not to overfit complex ML predictive models, is frequently unfeasible, current limitations in automated granular feature extraction may constrain ML's potential (42, 43).

We term this the “accuracy paradox” (technically, it should be the “AUC paradox,” but we think using the term “accuracy” could be more intuitive for readers), i.e., the apparent lack of performance improvement despite leveraging complex models and big data. However, advancements in AI-driven feature extraction from electronic health records (including unstructured text) may soon resolve this paradox (44, 45). This leads us to another crucial consideration: explainability.

## Explainable artificial intelligence (XAI)

Figure 1 illustrates another key point. In logistic regression, exponentiating β coefficients yields interpretable ORs. In contrast, NNs' hidden layers usually obscure feature contributions, rendering

them “black-box” models (46). This opacity extends to other complex ML techniques and challenges their clinical adoption. Indeed, interpretability underpins clinical reasoning. Recognizing independent risk factors, although reflecting association and not causation, yet supports plausible causality (e.g., the association of prior rectal CRKP colonization with the possible development of CRKP BSI is plausible from a pathophysiological standpoint, reflecting possible translocation into the bloodstream). This also means that, if researchers and clinicians encounter “strange” or absent associations (in terms of their plausible causality), this may help them to recognize possible biases or data errors, issues harder to detect with black-box models.

XAI seeks to approximate black-box predictions through more interpretable (but expectedly less accurate) models, aimed to identify influential features (47–49). Yet, such explanations usually carry inherent uncertainty and remain less robust than those from inherently interpretable models (46, 50–52). This also means that if a simpler, interpretable model matches a black-box model's accuracy, the former is preferable. Moreover, explainability aligns with the autonomy principle in biomedical ethics, facilitating shared decision-making (53). XAI's evolution aims to enhance explanation reliability to favor this crucial alignment. Still, further considerations arise, as discussed next.
Should we always explain? Insights
from candidemia research

As discussed above, we suspect suboptimal performance of some
complex models stems from reliance on nonspecific data. However,
the modest but present predictive power achievable from such data
warrants additional reflection. To illustrate our point, we present an
example from research on the use of ML to predict candidemia, an
emerging field of research with a handful of original studies published
in recent years (11, 13, 14, 16, 54, 55). In the largest of these studies at
the time of this perspective, a deep learning model trained on
nonspecific laboratory results (e.g., platelets, serum creatinine) and
previous *Candida* colonization achieved sensitivity 70%, specificity
58%, positive predictive value 16%, and negative predictive value 95%
for distinguishing candidemia from bacteremia across >12,000 BSI
events (11). These metrics mirror those from classical models using
known risk factors, suggesting ML's capacity to leverage complex
feature interactions imperceptible to clinicians (none of us, as
clinicians, is indeed able to predict candidemia based only on platelet
count and other nonspecific laboratory features) (56, 57). Notably,
integrating nonspecific features with established biomarkers of
candidemia (e.g., serum β-D-glucan) yielded no additive predictive
benefit in our experience, highlighting the need for further research
(11). Should future methods successfully harness such interactions of
nonspecific features to improve predictions, clinical reasoning
paradigms may shift. We might need to accept that part of our
predictions cannot be meaningfully explained—even with XAI. This
raises important ethical questions. Indeed, while improved predictions
may benefit patients, opaque reasoning challenges autonomy and
informed consent. Finding any possible acceptable compromise will
require robust multidisciplinary dialogue.

Discussion

In this perspective, we have reflected on how the growing
integration of AI and ML into infection prediction is beginning to
reshape the clinical reasoning traditionally applied to empirical
antimicrobial therapy in critically ill patients. Through the examples
of CRKP BSI and candidemia, we highlighted how these emerging
tools challenge established paradigms rooted in classical risk scores.

Classical statistical models, such as logistic regression, have long
been appreciated for their transparency and alignment with clinicians'
intuitive reasoning processes. They provide interpretable outputs, such
as ORs, which allow healthcare professionals to understand and
validate the rationale behind risk predictions. These characteristics
have facilitated their integration into both research and
clinical practice.

In contrast, modern ML models may (albeit not always) offer the
potential for enhanced predictive accuracy through their capacity to
model complex, non-linear relationships within large datasets. As
we have discussed, this potential has not always translated into clear
improvements in real-world predictive performance. This "accuracy
paradox" may stem in part from the nature of the available data: large
datasets often lack the clinical granularity of smaller, manually curated
cohorts, relying instead on administrative codes and routine
laboratory values with limited direct relevance to the pathophysiology
of infection. On the other hand, it should be noted that the literature

also includes positive examples of ML-enhanced improved prediction
of antimicrobial resistance using limited electronic health record and
laboratory data. Yang and colleagues reported on an improved
performance of ML models over classical logistic regression
approaches for predicting resistance to four first-line antibiotics in
patients with complicated urinary tract infections (58). In addition,
Vihta and colleagues exploited historical antimicrobial resistance and
antibiotic use data to predict through ML techniques future resistance
in different hospitals in England, showing the models ability to capture
complex relationships across use of different antibiotics and different
types of resistance to predict future resistance (59). While any
generalizability of these proof of concept findings requires external
validation across diverse clinical settings (this may require additional
efforts in low- and middle-income countries, where a more limited
deployment of digital infrastructure and electronic health records
could delay external validation and consequently compromise the
local applicability of predictive models), the presence of positive
examples underscores the possibility to effectively circumvent the
accuracy paradox, which requires a complex but achievable balance
in terms of dataset size, feature selection, performance metrics, and
generalizability to other clinical settings.

Beyond accuracy, a further critical issue is explainability. Complex ML
models frequently operate as black boxes, obscuring the pathways by which
predictions are generated. This opacity limits clinicians' ability to scrutinize
predictions for plausibility, undermines trust, and complicates shared
decision-making with patients. The field of XAI has emerged to address
these limitations, but current approaches remain imperfect and less robust
than explanations derived from traditional models. The preliminary
experience with candidemia prediction nonetheless underscores a further
nuance: models can capture subtle patterns from nonspecific features,
achieving predictive performance comparable to classical risk scores. While
this hints at untapped opportunities to enhance clinical decision-making,
it also raises fundamental questions about the acceptability of predictions
that cannot be meaningfully explained in clinical terms. In this regard, it
should be noted that some authors have suggested that the critique of AI
models as black boxes is controversial, since clinicians reasoning also
operates within equally opaque decision-making processes that cannot
be fully described or reproduced, but which lead expert clinicians to
integrate, not necessarily awarely, subtle cues that ultimately produce
effective outcomes (60). This can be used to support the argument that it is
not the nature of the reasoning (human vs. machine) and the explanation
of predictions that are important, but rather whether the predictions are
correct, useful, safe, and appropriately validated. However, in our view, this
argument reflects an incomplete picture, at least from two standpoints. The
first is that of abductive reasoning, i.e., one could assume, as educated guess,
that a clinician's opaque reasoning and intuitions are similar to those of
other clinicians (since both the former and the latter are humans and share
evolutionary patterns of reasoning processes), but the same may not be true
for machines. To illustrate our point with an example, we cannot rule out
the possibility that, even if predictions based on optimized and validated
ML models may be more accurate than human ones, when errors occur,
these could be easily identified and prevented by expert clinicians thanks
to shared human intuition or classical human reasoning. Certainly, this
would require confirmation through rigorous research on the human
ability to anticipate or rapidly recognize ML-based prediction errors before
they become actionable; in the meantime, we nonetheless believe this
possibility already and further underscores the need for AI as an assistant
rather than a substitute for clinicians. The second standpoint, as partly
anticipated above, is that of shared decision-making between clinicians and patients, which could be hampered by clinicians' inherently limited or absent ability to explain ML-assisted decisions when they derive from black-box models, and by patients' consequent inability to truly understand the related informed consent, also regarding data use and sharing for research purposes. In this light, achieving a reasonable and shared balance will require continued collaboration between clinicians, data scientists, and ethicists. As the field evolves, future research should prioritize the development of models that not only perform well but can also integrate meaningfully into the complex cognitive processes underpinning bedside clinical reasoning.

## Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

## Author contributions

DG: Conceptualization, Writing – original draft, Writing – review & editing. CM: Conceptualization, Writing – original draft, Writing – review & editing. MM: Conceptualization, Writing – original draft, Writing – review & editing. SG: Conceptualization, Writing – review & editing. YM: Conceptualization, Writing – review & editing. SM: Conceptualization, Writing – review & editing. AS: Conceptualization, Writing – review & editing. NR: Conceptualization, Writing – review & editing. AV: Conceptualization, Writing – review & editing. MG: Conceptualization, Writing – review & editing. CC: Conceptualization, Writing – review & editing. MP: Conceptualization, Writing – review & editing. MB: Conceptualization, Writing – review & editing.

## Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

## Acknowledgments

SG acknowledges the support of the “Hub Life Science - Digital Health (LSH-DH) PNCE3-2022-23683267 - Progetto DHEAL-COM - CUP: D33C2200198000,” granted by the Italian Ministero della Salute within the framework of the Piano Nazionale Complementare to the “PNRR Ecosistema Innovativo della Salute – Codice univoco investimento: PNC-E.3”.

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## Conflict of interest

Outside the submitted work, MB has received funding for scientific advisory boards, travel, and speaker honoraria from Cidara, Gilead, Menarini, MSD, Mundipharma, Pfizer, and Shionogi. Outside the submitted work, DG reports investigator-initiated grants from Pfizer, Shionogi, BioMérieux, Menarini, Tillotts Pharma, and Gilead Italia, travel support from Pfizer, and speaker/advisor fees from Pfizer, Menarini, BioMérieux, Advanz Pharma, MSD, and Tillotts Pharma.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

## Generative AI statement

The authors declare that Gen AI was used in the creation of this manuscript. A large language model (LLM)-based chatbot (ChatGPT-4o) was utilized to enhance the readability and conciseness of this manuscript after the complete draft, discussion excluded, was written by the authors without assistance by LLM-based chatbots. The following prompt was used: “Could you improve (in terms of readability) the text of the following scientific manuscript (without adding information, removing information, or changing the meaning of the sentences)? The total length of the text should remain under 3000 words”. The text produced by ChatGPT-4o was then thoroughly reviewed by all authors to ensure the preservation of content and meaning. Finally, based on the contents of the manuscript, we exploited ChatGPT-4o support during drafting of the discussion section and the abstract, to summarize the article’s main points (then substantially expanded and further elaborated by the authors by manually adding concepts and more in-depth discussion). The final text was again carefully reviewed by all authors to ensure its content and meaning were preserved.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

## Publisher's note

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