10.4081/btvb.2023.105
Artificial intelligence (AI) is rapidly becoming more important in our daily lives, and it’s beginning to be used in life sciences and in healthcare. AI and machine learning (ML) models are just starting to be applied in the field of hemostasis and thrombosis, but there are already many examples of how they can be useful in basic research/pathophysiology, laboratory diagnostics, and clinical settings. This review wants to shortly explain how AI works, what have been its uses in hemostasis and thrombosis so far and what are possible future developments. Besides the great potential advantages of a correct application of AI to the field of hemostasis and thrombosis, possible risks of inaccurate or deliberately mischievous use of it must be carefully considered. A close monitoring of AI employment in healthcare and research will have to be applied over the next years, but it is expected that the appropriate employment of this new revolutionary technology will bring great advances to the medical field, including to the hemostasis and thrombosis area. The current review, addressed to non-experts in the field, aims to go through the applications of AI in the field of hemostasis and thrombosis that have been explored so far and to examine its advantages, drawbacks and future perspectives.
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Paolo Gresele
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Bleeding, Thrombosis and Vascular Biology
hemostasis and thrombosis
Artificial intelligence and machine learning in hemostasis and thrombosis
Text.Serial.Journal
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2024-01-31
2023-12-21
2023-10-01
2024-01-31
Bleed Thromb Vascul Biol
Paolo Gresele
2023
10.4081/btvb.2023.105
Department of Medicine and Surgery, Hemostasis and Thrombosis Center, University of Perugia
2785-5309
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Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med 2022;28:31-8.
Artificial intelligence and machine learning in hemostasis and thrombosis
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                                                                                                                                REVIEW

Artificial intelligence and machine learning in hemostasis
and thrombosis
Paolo Gresele

Department of Medicine and Surgery, Hemostasis and Thrombosis Center, University of Perugia, Italy



ABSTRACT
    Artificial intelligence (AI) is rapidly becoming more important in our daily lives, and it’s beginning to be used in life sciences and
in healthcare. AI and machine learning (ML) models are just starting to be applied in the field of hemostasis and thrombosis, but there
are already many examples of how they can be useful in basic research/pathophysiology, laboratory diagnostics, and clinical settings.
This review wants to shortly explain how AI works, what have been its uses in hemostasis and thrombosis so far and what are possible
future developments. Besides the great potential advantages of a correct application of AI to the field of hemostasis and thrombosis,
possible risks of inaccurate or deliberately mischievous use of it must be carefully considered. A close monitoring of AI employment
in healthcare and research will have to be applied over the next years, but it is expected that the appropriate employment of this new
revolutionary technology will bring great advances to the medical field, including to the hemostasis and thrombosis area. The current




                                                                                                       ly
review, addressed to non-experts in the field, aims to go through the applications of AI in the field of hemostasis and thrombosis that




                                                                                               on
have been explored so far and to examine its advantages, drawbacks and future perspectives.


                                                                                 Besides its current uses, going from investment advice in fi-




                                                                                         e
Artificial intelligence and machine learning                                nancial business to image analysis or self-driving vehicles, AI
    Artificial Intelligence (AI) is becoming an inescapable tool
in many aspects of contemporary life, and the place that it oc-
                                                                                 us
                                                                            employment in healthcare and biomedical research is progres-
                                                                            sively growing.
                                                                                 Artificial intelligence (AI) has been defined as the designing
                                                                            al
cupies is destined to grow exponentially in the next few years.
                                                                            of artificial systems that have an intelligence comparable to hu-
                                                                  ci

                                                                            mans. This is achieved by providing knowledge to computers al-
                                                                            lowing them to learn from examples, what is conventionally
                                                                er


  Correspondence: Paolo Gresele, Department of Medicine and Sur-            defined as machine learning (ML). Deep learning is a form of ML
  gery, University of Perugia, Strada Vicinale Via delle Corse, S.          inspired by the human brain and by cognition mechanisms and is
                                                               m



  Andrea delle Fratte, 06132 Perugia, Italy.                                based on neural networks (NN), which draw inspiration from how
  E-mail: paolo.gresele@unipg.it                                            neurons compute in the human brain (https://www.youtube.com/
                                                    om




  Citation: Gresele P. Artificial intelligence and machine learning in      watch?v=YKhA39T-Dxs). Deep NN learn by adjusting the
  hemostasis and thrombosis. Bleeding, Thrombosis, and Vascular             strength of their connections to better convey input signals
  Biology 2023;2:105.                                                       through multiple layers of artificial neurons. When data are fed
                                             -c




                                                                            into a neural network each artificial neuron that fires transmits
  Key words: artificial intelligence, machine learning, hemostasis
                                                                            signals to certain neurons in the next layer, which are then likely
                                    on




  and thrombosis.
                                                                            to fire when multiple signals are received. This process allows fil-
  Conflict of interest: the author declares no potential conflict of in-    tering out noise and retaining the most relevant features, for ex-
                              N




  terest.                                                                   ample in the recognition as a chicken of a chicken image broken
                                                                            into pixels (Figure 1) (https://www.quantamagazine.org/new-the-
  Availability of data and material: data and materials are available
  by the author.                                                            ory-cracks-open-the-black-box-of-deep-learning-20170921/).
                                                                                 ML employs different categories of algorithms depending
  Received: 21 December 2023.                                               on the intended uses. These include supervised learning, the
  Accepted: 17 January 2024.                                                most widely used, in which data fed into the system have been
                                                                            labeled and targets assigned by a human expert. The output is
  Publisher’s note: all claims expressed in this article are solely those
  of the authors and do not necessarily represent those of their affili-    typically expressed as a class (e.g., diagnosis yes or no) or as
  ated organizations, or those of the publisher, the editors and the re-    numerical data (e.g., a regression coefficient).1 This has been the
  viewers. Any product that may be evaluated in this article or claim       most frequently adopted ML method in hemostasis and throm-
  that may be made by its manufacturer is not guaranteed or endorsed        bosis research so far. Reinforcement learning, which has as a
  by the publisher.
                                                                            goal to perform a task in the fastest and most efficient way,
  ©
   Copyright: the Author(s), 2023                                           works on the basis of a reward and penalty system. Every time
  Licensee PAGEPress, Italy                                                 the system receives a reward it reinforces that behavior. It is
  Bleeding, Thrombosis and Vascular Biology 2023; 2:105                     largely used in the development of automated robots, natural
  doi:10.4081/btvb.2023.105
                                                                            language systems or image processing and in many other poten-
  This work is licensed under a Creative Commons Attribution-               tially wide-impact applications. It has also been used in health-
  NonCommercial 4.0 International License (CC BY-NC 4.0).                   care, mainly in the oncology field,2 but apparently not for
                                                                            hemostasis and thrombosis research so far.


                                             Bleeding, Thrombosis and Vascular Biology 2023; 2:105
                                Artificial intelligence and machine learning in hemostasis and thrombosis                                 69



     Finally, unsupervised ML does not require the involvement            Then, the ML model to be applied should be selected, often two
of a human expert and the system is aimed to identify patterns            or more ML models may be compared for their efficiency. Sub-
within a large amount of unselected data filtering out back-              sequently, the testing of the system is made, typically using 80%
ground noise. This category of ML has been used for the analysis          of the available dataset for system training and the remaining
of big data, including in the hemostasis field.3                          20% for the initial validation of the model. Finally, the model is
     Biomedical examples for each of the three ML algorithm               made operational for wide testing with real-word data in the
categories in the hemostasis and thrombosis field are: for super-         field. It is crucial that the model is then continuously validated
vised learning, a study aimed at developing a fully automated             with new datasets to avoid that slight changes in the distribution
analysis of in vitro platelet spreading assays captured by differ-        of the new data fed into the model deteriorate the performance
ential interference contrast microscopy;4 for reinforced learning,        of the system.
one example close to hemostasis and thrombosis is a study                     Pre-processing of data is often required to achieve a good
aimed to pinpoint individuals susceptible to cardiovascular               performance of the study, including data selection, aimed at
events among athletes, showing that this ML model provided a              choosing a relevant subset of the features to be analyzed while
superior predictive value compared with traditional techniques;5          discarding the irrelevant of redundant ones, and normalization,
for unsupervised ML, a study on the identification of microRNA            which involves the transformation of features in a common
(miRNA) intracellular signaling regulatory networks linked to             range.7 A crucial prerequisite is also the definition of the optimal
cardiovascular disease in monocytes from patients with the an-            model architecture for the proposed task, also called hyperpara-
tiphospholipid syndrome. This advanced ML approach allowed                meter tuning, which in simple terms means configuring the sys-




                                                                                             ly
to identify specific miRNAs signatures related to cardiovascular          tem, that should contemplate questions like how many artificial




                                                                                     on
disease in subgroups of antiphospholipid syndrome patients with           neurons should be included in each NN layer, how many layers
distinct clinical phenotypes.3                                            should be considered, the number of branches that the decision
     The key fields of application of AI in medicine have been            tree should have, etc.




                                                                               e
grouped into six categories, i.e., image analysis systems (e.g.,
radiology, histo-cytopathology, dermoscopy), big data analysis
(the omics technologies), natural language processing (clinical
reports, medical notes, scientific writing), systems for real-word
                                                                        usArtificial intelligence and machine learning
                                                                          in hemostasis and thrombosis
data analysis (electronic health record analysis, adverse event
                                                                  al
recording), medical decision support systems (complex deci-                    The importance that ML methods are assuming in medical
                                                   ci

sion-making processes), and medical hardware-related systems              research and clinical practice is documented by the number of
(medical devices or portable sensors data analysis).6                     publications. A search in Pubmed on 20 December 2023 using
                                                 er


     A medical research investigation employing ML must typi-             “machine learning” yielded over 136,190 articles, but when one
cally involve some sequential steps to ensure the reliability of          adds “AND hemostasis AND thrombosis” the number of hits
                                                m



results. It is first necessary to check that the dataset provided to      decreased to 45, although with an exponential growth over the
the NN is representative of its intended subsequent application.          last 5 years. The application of AI to the hemostasis and throm-
                                      om
                                -c
                        on
                   N




Figure 1. Schematic representation of a typical artificial neural network and its functioning. When data are fed into a neural networks
(in this case a chicken image) each artificial neuron (small circles) that fires (bold small circles) transmits signals to certain neurons in
the subsequent layer, which are then likely to fire if multiple signals are received. The process allows filtering out noise and retaining
the most relevant features finally identifying a chicken from the broken pixels received as an input.


                                         Bleeding, Thrombosis and Vascular Biology 2023; 2:105
70                                                                         P. Gresele




bosis field spans from basic research and pathophysiology,                            Application of artificial intelligence to laboratory
through laboratory diagnostics and the identification and vali-                       diagnostics and biomarker identification
dation of disease biomarkers to clinical diagnostics and predic-
tion tools (Table 1). A few examples of the three categories will
                                                                                      in hemostasis
be discussed below.                                                                       Preanalytical quality control is essential to ensure reliable
                                                                                      results in diagnostic hemostasis testing but so far there are no
Application of artificial intelligence to basic                                       automatic methods to identify samples in which small blood
and pathophysiological research in hemostasis                                         clots have formed. One study explored the use of ML in identi-
                                                                                      fying clotted samples received by highly automated, high-
      Current techniques for the study of platelet aggregation, in-                   throughput hemostasis laboratories. By employing an ad hoc
cluding quite sensitive ones such as flow cytometry, cannot dis-                      trained NN assessing five laboratory variables (prothrombin
tinguish aggregates formed by different agonists. In a study, ML                      time, activated partial thromboplastin time, thrombin clotting
was used to enhance the resolution power of the analysis of platelet                  time, fibrinogen, and D-dimer) in 189 clotted and 2889 not clot-
aggregates formed upon stimulation of whole blood with four dif-                      ted samples, the authors showed that the model identified with
ferent agonists. Aggregates formed upon stimulation by adenosine                      high precision the clotted ones.11
diphosphate, collagen, U46619, or thrombin receptor-activating                             Another very promising study dealt with the complex prob-
peptide-6 were analyzed either by flow cytometry or by an                             lem of disseminated intravascular coagulation (DIC) diagnosis.
optofluidic time-stretch microscope for image acquisition. Ac-




                                                                                                                      ly
                                                                                      Currently, the diagnosis of DIC is cumbersome and involves the
quired images were used to train two different convolutional NN                       interpretation of a combination of laboratory and clinical param-




                                                                                                            on
models that classified images based on their morphological fea-                       eters through the use of some probability scores. In this study,
tures, a method that the authors called Intelligent Platelet Aggre-                   the authors employed a supervised NN model analyzing 32 clin-
gate Classifier (IPAC).8 Results showed that while aggregates                         ical and laboratory parameters in an initial development model




                                                                                                      e
formed by the four different agonists were indistinguishable by                       on 656 patients with suspected DIC (428 without and 228 with
flow cytometry, the IPAC readily classified them into four different
types. Besides providing a tool for the deeper analysis of platelet
aggregate formation, the IPAC might provide a tool to study
                                                                                             us
                                                                                      confirmed DIC) and subsequently in an external validation
                                                                                      model on 217 patients (137 without and 80 with DIC) and com-
                                                                                      pared its diagnostic performance with that of three largely used
                                                                                    al
platelet aggregates circulating in blood, allowing to obtain poten-                   probability scores (International Society on Thrombosis and
tial information on the type of agonists that generated them in vivo                  Haemostasis, Japanese Ministry of Health and Welfare, and
                                                                    ci

or on the site of aggregate formation in the circulatory system.8                     Japanese Association of Acute Medicine). Not only the ML
      Other promising studies include the identification of distinct                  model outperformed all three scores, but it also revealed that
                                                                  er


subpopulations of platelets generated by the exposure to different                    some variables usually disregarded for DIC diagnosis, like the
agonists using an unsupervised ML system,9 or the study of platelet                   blood eosinophil count or the platelet or red cell distribution
                                                                 m




adhesion under flow conditions by the analysis of high frame rate                     width, have some importance in DIC diagnostics.12
                                                      om




videos using a semi-unsupervised learning system (Table 1).10                             Another controversial diagnostic issue is that of heparin-in-
                                               -c




Table 1. Applications of artificial intelligence to thrombosis and hemostasis.
                                     on




Basic research/pathophysiology             -   Platelet activation morphodynamics
                                           -   Differentiation of platelet aggregates induced by different agonists
                                           -   Platelet calcium calculator
                               N




                                           -   Agonist- and primer-specific formation of platelet subpopulations
                                           -   Shear-mediated platelet adhesion kinematics
                                           -   Multiscale prediction of platelet-specific platelet function under flow
Laboratory diagnostic/biomarkers           -   Identification of clotted specimens in the coagulation laboratory
                                           -   Deciphered coagulation profile to diagnose antiphospholipid syndrome
                                           -   Diagnosis of disseminated intravascular coagulation
                                           -   Minimization of misdiagnosis of HIT
                                           -   Platelet RNA-sequencing for cancer diagnostics (liquid biopsies)
                                           -   D-dimer as biomarker for prognosis in TTP
                                           -   Analysis of blood parameters for immune thrombotic dysregulation in COVID-19
                                           -   Role of genetic polymorphisms in VTE
Clinical diagnostics/predictions           -   Characterization of antiphospholipid syndrome atherothrombotic risk by transcriptomic analysis
                                           -   Prediction and diagnosis of VTE
                                           -   Prediction of VTE in acutely ill medical patients
                                           -   Prediction of precision warfarin dosing
                                           -   Using AI to reduce non-adherence to anticoagulation therapy
                                           -   Prediction of splanchnic vein thrombosis in acute pancreatitis
                                           -   Processing of radiology reports for VTE detection
                                           -   Prediction of recurrent VTE
HIT, heparin-induced thrombocytopenia; TTP, thrombotic thrombocytopenic purpura; VTE, venous thromboembolism; AI, artificial intelligence.


                                               Bleeding, Thrombosis and Vascular Biology 2023; 2:105
                                Artificial intelligence and machine learning in hemostasis and thrombosis                                71



duced thrombocytopenia (HIT) which also involves the combined           after 17 unsuccessful consultations with medical doctors of various
interpretation of clinical and laboratory parameters and their im-      specialties, interrogated ChatGPT providing to the system all
plementation in various proposed algorithms.13 In a prospective         symptoms and medical reports of the child. ChatGPT suggested
multicenter cohort study including 1393 consecutive patients with       the diagnosis of tethered spinal cord syndrome, a rare congenital
suspected HIT, a supervised ML model applied to a training data         disorder due to malformations of the spinal cord, but invited to
subset (75% of patients) and to a validation dataset (25% of pa-        consult a medical specialist. The child was then referred to a pedi-
tients) was substantially more accurate in HIT diagnosis than the       atric neurosurgeon who confirmed the diagnosis (https://ts2.space/
currently recommended algorithms.14 Many other examples of the          en/ai-helps-diagnose-rare-condition-in-child/#gsc.tab=0). While
potential of AI for laboratory diagnostics in hemostasis and throm-     this case highlights the terrific potential of AI for diagnostic aid
bosis have been explored, including the study of the amazing ge-        with complex clinical cases, it also alarms for its possible inappro-
netic complexity of platelets and its exploitation for cancer           priate widespread and unsupervised use by ordinary people with
diagnostics.15,16                                                       consequent erroneous self-medication, etc. A recent study involv-
                                                                        ing 457 clinicians across 13 US States explored their diagnostic
Application of artificial intelligence to clinical                      performance when asked to respond to a respiratory distress-related
diagnostics and risk prediction in hemostasis                           clinical vignette, either unassisted or with support from an AI sys-
                                                                        tem for chest X-ray interpretation. The study showed that diagnos-
     A relevant clinical goal in patient management is that of pre-     tic performance was only slightly increased by AI assistance but
dicting outcomes in subjects anticoagulated with anti-vitamin K         also, worryingly, that when the AI system was biased, diagnostic




                                                                                           ly
agents based on the results of the prothrombin time expressed as        performance was drastically worsened.24 Therefore, even for




                                                                                   on
international normalized ratio (INR). INR data obtained over the        healthcare professionals, AI support for complex clinical case di-
first 30 days after prescription from 4708 patients enrolled in the     agnostics and decision-making must be adopted carefully, strictly
GARFIELD registry were used to train a supervised ML NN (3185           monitoring the system performance.25




                                                                             e
patients) and to test its prediction accuracy for clinical outcome at        Another illustrative case that shows the lights and shadows
one year in a validation cohort (1523 patients). The model outper-
formed the conventionally used time in therapeutic range (TTR)
parameter,17 in the prediction of major bleeding, stroke/systemic
                                                                        us
                                                                        of AI use in the medical field is an experiment carried out by
                                                                        Valentin Fuster, editor-in-chief of the Journal of the American
                                                                        College of Cardiology (JACC), who asked a generative AI tool
embolism and death.18 Several other applications of AI in the field
                                                                   al
                                                                        (ChatGPT) to re-write a published JACC manuscript in author-
of oral anticoagulation have been explored, such as its use in re-      specific styles. In <60 min the system produced the paper rewrit-
                                                    ci

ducing the risk of non-adherence to anticoagulation therapy,19 in       ten in 8 different styles, including Valentin Fuster’s, Bob
deciding precise warfarin dosing or in predicting the incidence of      Dylan’s, and William Shakespeare’s, with incredible plausibility
                                                  er


adverse outcomes after premature discontinuation of anticoagula-        and a perfect style.26 However, in the interpretation of results the
tion in patients with pulmonary embolism (Table 1).20,21                AI model lacked accuracy and included generic statements. The
                                                 m



     Another widely explored area with very encouraging results         conclusions of the authors were that while AI may provide great
is that of the prediction of venous thromboembolism (VTE) risk
                                       om




                                                                        benefits for scholarship writing, its use warrants caution from
or its recurrence in different patient populations (Table 1). One       the authors for generating conclusions and discussions.26
interesting example is the identification of subjects at high risk           Besides these possible faults, there may be real cases of de-
for VTE among acutely ill medical patients. Currently, the pre-
                                 -c




                                                                        liberate misuse of AI for health. In an experiment carried out by
diction is based on clinical scores, such as the Padua or the IM-
                                                                        a group of Australian investigators, the authors instructed some
PROVE scores, which however have a low predictive value. The
                         on




                                                                        publicly accessible generative large-language models to create
application of two different supervised ensemble learning algo-
                                                                        blogs aimed at spreading vaccine disinformation. In 65 min 102
rithms, analyzing 68 or 16 variables respectively, to patients en-
                                                                        very convincing anti-vax blogs were generated, showing that
                   N




rolled in the APEX trial showed that both AI systems significantly
                                                                        when the guardrails of AI instruments are not strict, the risks of
outperformed the IMPROVE score in predicting VTE.22
                                                                        health disinformation may be very high. The authors suggested
     The prediction and diagnosis of VTE by AI has actually been
                                                                        that the key principles of pharmacovigilance, including trans-
explored in several studies and a meta-analysis of 12 reports (7
                                                                        parency, surveillance, and regulation, could be an inspiring ex-
studying only a training dataset and 5 both a training and a val-
                                                                        ample for managing the risks of AI misuse in health care.27
idation dataset), including 51,383 patients, showed that AI may
help in the diagnosis and prediction of VTE independent of ve-
nous thrombosis type, AI model employed, type of outcome (di-
agnosis or prediction) and of whether the analyzed period was           Final remarks
perioperative or not.23
                                                                            Despite its great potential and its already wide penetration
                                                                        in several domains of human activities, AI is still at its early
                                                                        stages of application for clinical and research studies in health-
Limitations and warnings on the use                                     care, particularly in hemostasis and thrombosis. Indeed, there
of artificial intelligence in health research                           are several reasons that slow down AI adoption in this field, in-
and care                                                                cluding the fact that healthcare data are more heterogeneous and
                                                                        variable than data in other sectors, lack of patient confidence,
     An impressive recent example of the potentials and risks of        regulatory and ethical issues, methodologic concerns, and the
widely accessible generative AI is the story appeared in the news       primary role that quality and safety have in healthcare respect
of a young child’s long-lasting undiagnosed illness whose mother,       to other domains.28


                                          Bleeding, Thrombosis and Vascular Biology 2023; 2:105
72                                                                 P. Gresele




     One critical aspect limiting the wide application of ML models                  evaluation of methods, review of literature, and future di-
in healthcare is their interpretability, since most of them are based                rections. J Thromb Haemost 2023;21:728-43.
on highly complex calculations and the elaboration of a large num-              2.   Yala A, Mikhael PG, Lehman C, et al. Optimizing risk-based
ber of parameters (in the order of millions) making the decisions                    breast cancer screening policies with reinforcement learning.
taken by the model often impossible to understand, especially be-                    Nat Med 2022;28:136-43.
cause no justification for the decisions taken is provided, the so-             3.   Peréz-Sanchéz L, Patiño-Trives AM, Aguirre-Zamorano
called black-box models.29 Lack of interpretability in predictive                    MA, et al. Characterization of antiphospholipid syndrome
ML models should raise caution, especially when the decisions                        atherothrombotic risk by unsupervised integrated transcrip-
taken may impact patients’ health and lives.30 Indeed, AI models                     tomic analyses. Arterioscler Thromb Vasc Biol 2021;41:
most often lack the broader clinical context that is relevant for pa-                865-77.
tient care25 thus a new clinical knowledge-enhanced ML pipeline                 4.   Kempster C, Butler G, Kuznecova E, et al. Fully automated
is being developed to improve model performance and physicians’                      platelet differential contrast image analysis via deep learn-
confidence with their application.31 The training on data using both                 ing. Sci Rep 2022;22:4614.
“explainable” and “black box” ML models to enhance confidence                   5.   Bostani A, Mirzaeibonekhater H, Najafi H, et al. MLP-RL-
in the system and the involvement of a multidisciplinary team of                     CRD: diagnosis of cardiovascular risk in athletes using a re-
clinicians, data scientists, software engineers, and statisticians in                inforcement learning-based multilayer perceptron. Physiol
ML model design may be ways to overcome the current limitations                      Meas 2023;44. doi: 10.1088/1361-6579/ad1459.
                                                                                6.   Rösler W, Altenbuchinger M, Baeßler B, et al. An overview




                                                                                                            ly
of the application of AI/ML to healthcare.1,30
     The possible mistakes or misuse of AI in medicine prompt                        and a roadmap for artificial intelligence in hematology and




                                                                                                    on
strict regulations, from both scientific societies and government                    oncology. J Cancer Res Clin Oncol 2023;149:7997-8006.
authorities, ensuring continuous monitoring with built-in mecha-                7.   Singh D, Singh B. Investigating the impact of data normal-
nisms allowing speedy regulatory updates permitting to guide the                     ization on classification performance. Appl Soft Comput J




                                                                                              e
continuously evolving capabilities of AI for medicine and                            2020;97:105524.
science.26,30 Indeed, an executive order on AI implications for
healthcare organization was issued by President Biden on October
30th 2023, calling on agencies to establish standards for safe and
                                                                                8.
                                                                                      us
                                                                                     Zhou Y, Yasumoto A, Huang CJ, et al. Intelligent classifica-
                                                                                     tion of platelet aggregates by agonist type. Elife 2020;9:
                                                                                     e52938.
                                                                          al
trustworthy AI use, the European Union has drafted AI use rules                 9.   Veninga A, Baaten CCFMJ, De Simone I, et al. Effects of
which will be adopted in the coming months and the World Health                      platelet agonists and priming on the formation of platelet
                                                            ci

Organization has released a detailed document on regulatory con-                     populations. Thromb Haemost 2022;122:726-38.
siderations on AI for health (https://www.who.int/news/item/28-            10.       Wang P, Sheriff J, Zhang P, et al. A multiscale model for shear-
                                                          er


06-2021-who-issues-first-global-report-on-ai-in-health-and-six-g                     mediated platelet adhesion dynamics: correlating in silico
                                                                                     with in vitro results. Ann Biomed Eng 2023;51:1094-97.
                                                         m



uiding-principles-for-its-design-and-use). A watchful control over
the evolving implications of AI employment in healthcare and re-           11.       Fang K, Dong Z, Chen X, et al. Using machine learning to
                                               om




search, with the setting of boundaries to avoid misuse, balanced                     identify clotted specimens in coagulation testing. Clin Chem
by the awareness of the great advantages and progress that AI may                    Lab Med 2021;59:1289-97.
bring, seems to be the best way to govern the application of this          12.        Yoon GJ, Heo JN, Kim M, et al. Machine learning-based
                                                                                     diagnosis for disseminated intravascular coagulation (DIC):
                                         -c




new revolutionary technology to the medical field without stifling
its innovative and beneficial potential.32,33                                        development, external validation, and comparison to scoring
                                 on




                                                                                     systems. PLoS One 2018;13:e0195861.
                                                                           13.       Marcucci R, Berteotti M, Gori AM, et al. Heparin induced
Conclusions                                                                          thrombocytopenia: position paper from the Italian Society
                            N




                                                                                     on Thrombosis and Haemostasis (SISET). Blood Transfus
     After several years of research, AI has undergone an incredi-                   2021;19:14-23.
ble acceleration in the last few years becoming a technology that          14.       Nilius H, Cuker A, Haug S, et al. A machine learning model
may transform our society and broadly reshape medicine.33 Not                        for reducing misdiagnosis in heparin-induced thrombocy-
realizing this may delay the chance that AI provides to expand the                   topenia: a prospective, multicentre, observational study.
access to more accurate, efficient and cost-effective healthcare.                    EClinical Medicine 2022;55:101745.
In medical research too, AI may speed up the discovery and trans-          15.       Bury L, Gresele P. The amazing genetic complexity of anu-
lation of new knowledge from research into clinical improvement.                     cleated platelets. Bleeding, Thrombosis and Vascular Biol-
The hemostasis and thrombosis community must seize the oppor-                        ogy 2022;1:33.
tunity that AI offers to fasten the advancements in the field, but a       16.       Antunes-Ferreira M, D'Ambrosi S, Arkani M, et al. Tumor-
very close and continuous scrutiny, watching over the adoption                       educated platelet blood tests for non small cell lung cancer
of appropriate methodological and ethical rules, will have to be                     detection and management. Sci Rep 2023;13:9359.
exerted in order to avoid misuse or bias in the results obtained.          17.       De Girolamo G, Sarti L, Cecoli S, et al. Safety and efficacy
                                                                                     of treatment with vitamin K antagonists in patients managed
                                                                                     in a network of anticoagulant services or a routine general
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