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LiverAI: New tool in the landscape for liver health
LiverAI: nueva herramienta en el panorama de la salud hepática
David Marti-Aguadoa,
, Javier Pazób, Alvaro Diaz-Gonzalezc, Berta de las Heras Páez de la Cadenad, Andres Conthee, Rocio Gallego Duranf, Miguel A. Rodríguez-Gandíag, Juan Turnesh, Manuel Romero-Gomezf
Corresponding author
a Digestive Disease Department, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain
b AI and IT Solutions Manager, Spanish Association for the Study of the Liver (AEEH), Spain
c Gastroenterology and Hepatology Department, Clinical and Translational Research in Digestive Diseases Group, Valdecilla Research Institute (IDIVAL), Marqués de Valdecilla University Hospital, Santander, Spain
d Department of Digestive Diseases, Hospital Universitario 12 de Octubre, Madrid, Spain
e Department of Gastroenterology and Hepatology, Hospital General Universitario Gregorio Marañón, Madrid, Spain
f Digestive Diseases Unit and CIBERehd, Virgen del Rocío University Hospital, Institute of Biomedicine of Seville (HUVR/CSIC/US), University of Seville, Seville, Spain
g Department of Gastroenterology and Hepatology, Hospital Universitario Ramón y Cajal, Instituto Ramón y Cajal de Investigación Sanitaria, Madrid, Spain
h Department of Gastroenterology and Hepatology, Complejo Hospitalario Universitario Pontevedra & IIS Galicia Sur, Spain
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In the last years, Artificial Intelligence (AI) has emerged as a tool rising to such a challenge and trying to improve the experience of medical doctors and patients. Actually, AI encompasses a range of techniques and related technologies that might support clinical decision making in healthcare and perform tasks traditionally thought to require human reasoning. For example, in gastroenterology, AI already provides a precise tool in screening colonoscopies as to improve the adenoma detection rate.<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">1</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">In the field of hepatology, there is a growing number of research articles that apply AI techniques for the management of chronic liver diseases (CLD). Most studies have been published in the field of diagnosis.<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">2</span></a> Particularly it has been shown how deep learning (DL) approaches such as convolutional neural networks (CNN), can automatically extract clinically useful information from histopathology and radiology images.<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">3</span></a> Nevertheless, there are some obstacles on the way of AI tools to clinical implementation. The obstacles that stand out most are the lack of knowledge and trust in AI systems among medical personnel, and the significant room for improvement in terms of the low explainability of most of the marketed products.<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">4</span></a> A recent survey performed in Spain, showed that only 17% of physicians use clinical decision support systems at their workplace.<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">5</span></a> Most surveyed physicians reported little awareness about the use of digital health interventions and they did not recommend application tools for the management of patients with CLD.</p><p id="par0015" class="elsevierStylePara elsevierViewall">In the last years, generative large language models (LLMs) have gathered attention for their ability to generate human-like text responses to natural-language inquiries. ChatGPT, for example, has achieved worldwide adoption because of its conversational interface, allowing users to communicate with the AI in a human-like way.<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">6</span></a> This technology has already been tested in several medical contexts, including addressing patients questions related to MASLD and radiological images examinations, or even solving clinical case challenges.<a class="elsevierStyleCrossRefs" href="#bib0095"><span class="elsevierStyleSup">7–9</span></a> Beyond textual content, the performance of generative AI has also shown promising results in processing and interpreting histological images.<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">10</span></a> Acknowledging its innovation, ChatGPT and other LLMs are trained on the general-purpose text and not specifically designed for health care needs.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">LiverAI and Turing test</span><p id="par0020" class="elsevierStylePara elsevierViewall">In February 2022, the Asociación Española para el Estudio del Hígado (AEEH) launched LiverAI as a specialized area focused on AI applications in hepatology. This initiative was motivated by the need to improve knowledge and access to AI tools specifically designed for clinical practice and research in liver diseases.<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">5</span></a> The AEEH infused LiverAI with extensive scientific content related to the pathophysiology, diagnosis, treatment, and research of liver diseases. DL algorithms enable LiverAI to analyze medical texts, responding to user inquiries and prompts. One of LiverAI's primary services is the first AI-powered chatbot developed by a medical scientific society. This chatbot offers personalized support and education to its users. Beyond this question-answer functionality, LiverAI now offers multiple AI tools developed specifically for hepatologists (available online for AEEH members: <a href="https://aeeh.es/liverai-3-0/">https://aeeh.es/liverai-3-0/</a>). To foster trust and overcome initial skepticism, the AEEH is committed to generating evidence demonstrating LiverAI's potential to complement healthcare delivery.</p><p id="par0025" class="elsevierStylePara elsevierViewall">In 1950, the mathematician Alan Turing speculated that machines would end up being able to think and behave like humans. He developed a test to examine a machine's ability to show seemingly intelligent behavior equivalent to that of a human. This test was the “<span class="elsevierStyleItalic">imitation</span><span class="elsevierStyleItalic">game</span>” in which a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses.<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">11</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">In June 2023, AEEH organized an AI workshop with 20 invited participants. The opportunity was taken to test LiverAI with the “<span class="elsevierStyleItalic">imitation game</span>”. A total of 8 liver-related questions were generated from the following categories: portal hypertension, autoimmune hepatitis, hepatocelular carcinoma, cirrhosis-associated immune disfunction, alcohol related liver disease, MASLD, hepatic encephalopathy, and surgical risk in patients with CLD. Each question was independently answered in text-only channel by an expert hepatologist and by LiverAI chatbot. These questions were posed to LiverAI with a short prompt instructing the model to provide an accurate and easy-to-understand response. Then, the answers were evaluated by the 20 physicians that participated in the workshop. These evaluators were aware that one of the two answers was a machine, and all three participants (evaluator, expert hepatologist, LiverAI). were blinded from one another. The task of the evaluator was to: (I) determine which answer was given by the human and which one was given by the computer; and (II) assess the accuracy of the answers using the five-level Likert scale (disappointed, not satisfied, adequate, quite satisfied, completely satisfied).<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">8</span></a> If the evaluator could not reliably tell the machine from the human in more than a 30% chance, then LiverAI would have passed the Turing test.<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">12</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">The main results of our exploratory Turing test are summarized in <a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>. In total, 43% of the answers given by LiverAI resembled those an expert hepatologist will give. In addition, metrics of accuracy showed a similar performance capacity between the machine and human. For the answers given by LiverAI: 1% were disappointed; 16% not satisfied; 21% considered them as adequate; 28% as quite satisfied; and 34% were completely satisfied. On the other hand, for the answers given by the experts hepatologists: 1% were disappointed; 11% not satisfied; 34% considered them as adequate; 26% as quite satisfied; and 38% were completely satisfied. These results generate evidence to gain certain trust in the use of LiverAI among medical personnel. Our exploratory analysis underscores the potential of LiverAI to provide accurate answers to specific liver-related questions, aiming to provide support in clinical decision making and automate time-consuming tasks within the healthcare setting.</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Conclusion</span><p id="par0040" class="elsevierStylePara elsevierViewall">In hepatology, LiverAI has emerged as a new and specific tool that can assist clinicians in various tasks related to liver disease, representing a shortcut in access to information. Although LLMs have great potential in healthcare, they should be used as a tool to support clinical decisions and further enhance critical thinking, rather than replace, human expertise and judgment. As limitations, AI algorithms are trained on large datasets, which can contain biases that are reflected in the system output. To address this, it is essential to monitor the outputs of AI systems to ensure that they are free from bias. LiverAI is currently under the process of <span class="elsevierStyleItalic">continuous learning</span>, with the goal of minimizing or eradicating any potential biases. This process is crucial for its evolution as a trusted AI assistant, providing essential support to clinicians and researchers in the field of liver diseases. By refining its ability to deliver unbiased and accurate analyses, LiverAI strives to be a tool that complements and enriches human expertise and judgment in hepatology.</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Declaration of generative AI and AI-assisted technologies in the writing process</span><p id="par0045" class="elsevierStylePara elsevierViewall">The development of this scientific research paper involved the utilization LiverAI specifically to generate a series of texts for subsequent evaluation within the Turing test. For the writing of the manuscript, no AI-assisted technology was used.</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Funding</span><p id="par0065" class="elsevierStylePara elsevierViewall">This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Conflicts of interest</span><p id="par0050" class="elsevierStylePara elsevierViewall">LiverAI is property of Asociación Española para el Estudio del Hígado (AEEH) and access is free for its members.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:8 [ 0 => array:2 [ "identificador" => "sec0005" "titulo" => "Artificial intelligence in hepatology" ] 1 => array:2 [ "identificador" => "sec0010" "titulo" => "LiverAI and Turing test" ] 2 => array:2 [ "identificador" => "sec0015" "titulo" => "Conclusion" ] 3 => array:2 [ "identificador" => "sec0020" "titulo" => "Declaration of generative AI and AI-assisted technologies in the writing process" ] 4 => array:2 [ "identificador" => "sec0030" "titulo" => "Funding" ] 5 => array:2 [ "identificador" => "sec0025" "titulo" => "Conflicts of interest" ] 6 => array:2 [ "identificador" => "xack744709" "titulo" => "Acknowledgements" ] 7 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "multimedia" => array:1 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1649 "Ancho" => 2925 "Tamanyo" => 240477 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Graphical abstract of Turing test results: LiverAI <span class="elsevierStyleItalic">vs</span>. expert hepatologists.</p>" ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:12 [ 0 => array:3 [ "identificador" => "bib0065" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "The application of artificial intelligence in improving colonoscopic adenoma detection rate: where are we and where are we going" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:5 [ 0 => "P. Gan" 1 => "P. Li" 2 => "H. Xia" 3 => "X. Zhou" 4 => "X. Tang" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:5 [ "tituloSerie" => "Gastroenterol Hepatol" "fecha" => "2023" "volumen" => "46" "paginaInicial" => "203" "paginaFinal" => "213" ] ] ] ] ] ] 1 => array:3 [ "identificador" => "bib0070" "etiqueta" => "2" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:5 [ 0 => "D. Nam" 1 => "J. Chapiro" 2 => "V. Paradis" 3 => "T.P. Seraphin" 4 => "J.N. Kather" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.jhepr.2022.100443" "Revista" => array:5 [ "tituloSerie" => "JHEP Rep" "fecha" => "2022" "volumen" => "4" "paginaInicial" => "100443" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/35243281" "web" => "Medline" ] ] ] ] ] ] ] ] 2 => array:3 [ "identificador" => "bib0075" "etiqueta" => "3" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Automated whole-liver MRI segmentation to assess steatosis and iron quantification in chronic liver disease" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "D. Martí-Aguado" 1 => "A. Jiménez-Pastor" 2 => "Á. Alberich-Bayarri" 3 => "A. Rodríguez-Ortega" 4 => "C. Alfaro-Cervello" 5 => "C. Mestre-Alagarda" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1148/radiol.2021211027" "Revista" => array:6 [ "tituloSerie" => "Radiology" "fecha" => "2022" "volumen" => "302" "paginaInicial" => "345" "paginaFinal" => "354" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/34783592" "web" => "Medline" ] ] ] ] ] ] ] ] 3 => array:3 [ "identificador" => "bib0080" "etiqueta" => "4" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Automation bias and assistive AI: risk of harm from AI-driven clinical decision support" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "R. Khera" 1 => "M.A. Simon" 2 => "J.S. Ross" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1001/jama.2023.22557" "Revista" => array:6 [ "tituloSerie" => "JAMA" "fecha" => "2023" "volumen" => "330" "paginaInicial" => "2255" "paginaFinal" => "2257" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/38112824" "web" => "Medline" ] ] ] ] ] ] ] ] 4 => array:3 [ "identificador" => "bib0085" "etiqueta" => "5" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Physicians’ use of digital health interventions in the management of nonalcoholic fatty liver disease" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "J.V. Lazarus" 1 => "M. Villota-Rivas" 2 => "C. Jiménez-González" 3 => "A. Santos-Laso" 4 => "P. Iruzubieta" 5 => "M.T. Arias-Loste" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.cld.2023.01.021" "Revista" => array:6 [ "tituloSerie" => "Clin Liver Dis" "fecha" => "2023" "volumen" => "27" "paginaInicial" => "515" "paginaFinal" => "533" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/37024221" "web" => "Medline" ] ] ] ] ] ] ] ] 5 => array:3 [ "identificador" => "bib0090" "etiqueta" => "6" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Artificial intelligence-based text generators in hepatology: ChatGPT is just the beginning" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "J. Ge" 1 => "J.C. Lai" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1097/HC9.0000000000000097" "Revista" => array:5 [ "tituloSerie" => "Hepatol Commun" "fecha" => "2023" "volumen" => "7" "paginaInicial" => "e0097" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/36972383" "web" => "Medline" ] ] ] ] ] ] ] ] 6 => array:3 [ "identificador" => "bib0095" "etiqueta" => "7" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Enhancing patient communication with Chat-GPT in radiology: evaluating the efficacy and readability of answers to common imaging-related questions" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "E.B. Gordon" 1 => "A.J. Towbin" 2 => "P. Wingrove" 3 => "U. Shafique" 4 => "B. Haas" 5 => "A.B. Kitts" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:5 [ "tituloSerie" => "J Am Coll Radiol" "fecha" => "2024" "volumen" => "21" "paginaInicial" => "353" "paginaFinal" => "359" ] ] ] ] ] ] 7 => array:3 [ "identificador" => "bib0100" "etiqueta" => "8" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Accuracy reliability, and comprehensibility of ChatGPT-generated medical responses for patients with nonalcoholic fatty liver disease" "autores" => array:1 [ 0 => array:3 [ "colaboracion" => "NAFLD Expert Chatbot Working Group" "etal" => false "autores" => array:6 [ 0 => "N. Pugliese" 1 => "V. Wai-Sun Wong" 2 => "J.M. Schattenberg" 3 => "M. Romero-Gomez" 4 => "G. Sebastiani" 5 => "A. Aghemo" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:4 [ "tituloSerie" => "Clin Gastroenterol Hepatol" "fecha" => "2024" "volumen" => "22" "paginaInicial" => "886-889.e5" ] ] ] ] ] ] 8 => array:3 [ "identificador" => "bib0105" "etiqueta" => "9" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Use of GPT-4 to diagnose complex clinical cases" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "A.V. Eriksen" 1 => "S. Möller" 2 => "J. Ryg" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:3 [ "tituloSerie" => "NEJM AI" "fecha" => "2023" "volumen" => "1" ] ] ] ] ] ] 9 => array:3 [ "identificador" => "bib0110" "etiqueta" => "10" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Preliminary fatty liver disease grading using general-purpose online large language models: ChatGPT-4 or bard?" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:5 [ 0 => "Y. Zhang" 1 => "H. Liu" 2 => "B. Sheng" 3 => "Y.C. Tham" 4 => "H. Ji" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:5 [ "tituloSerie" => "J Hepatol" "fecha" => "2023" "volumen" => "S0168-8278" "paginaInicial" => "05293" "paginaFinal" => "05295" ] ] ] ] ] ] 10 => array:3 [ "identificador" => "bib0115" "etiqueta" => "11" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "What was the Turing test actually about?" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "B. Gonçalves" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1038/d41586-023-04062-7" "Revista" => array:5 [ "tituloSerie" => "Nature" "fecha" => "2023" "volumen" => "624" "paginaInicial" => "523" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/38114681" "web" => "Medline" ] ] ] ] ] ] ] ] 11 => array:3 [ "identificador" => "bib0120" "etiqueta" => "12" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Computing machinery and intelligence" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "A.M. Turing" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:5 [ "tituloSerie" => "Mind" "fecha" => "1950" "volumen" => "LIX" "paginaInicial" => "433" "paginaFinal" => "460" ] ] ] ] ] ] ] ] ] ] "agradecimientos" => array:1 [ 0 => array:4 [ "identificador" => "xack744709" "titulo" => "Acknowledgements" "texto" => "<p id="par0055" class="elsevierStylePara elsevierViewall">The authors want to thank the physicians that participated in the Liver AI workshop and the experts hepatologists that completed the Turing test: Rocio Aller (Hospital Clínico Universitario de Valladolid), Rosa Martín Mateos (Hospital Universitario Ramón y Cajal, Madrid), María Carlota Londoño (Hospital Clínic de Barcelona), Elisa Pose (Hospital Clínic de Barcelona), Edilmar Alvarado-Tapias (Hospital Santa Creu I Sant Pau, Barcelona), Alejandro Forner (Hospital Clínic de Barcelona), Luis Tellez (Hospital Universitario Ramón y Cajal, Madrid).</p>" "vista" => "all" ] ] ] "idiomaDefecto" => "en" "url" => "/02105705/0000004700000006/v1_202405150504/S021057052400075X/v1_202405150504/en/main.assets" "Apartado" => array:4 [ "identificador" => "96400" "tipo" => "SECCION" "es" => array:2 [ "titulo" => "Rincón Joven" "idiomaDefecto" => true ] "idiomaDefecto" => "es" ] "PDF" => "https://static.elsevier.es/multimedia/02105705/0000004700000006/v1_202405150504/S021057052400075X/v1_202405150504/en/main.pdf?idApp=UINPBA00004N&text.app=https://www.elsevier.es/" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S021057052400075X?idApp=UINPBA00004N" ]
| Year/Month | Html | Total | |
|---|---|---|---|
| 2024 October | 11 | 2 | 13 |
| 2024 September | 34 | 5 | 39 |
| 2024 August | 13 | 0 | 13 |
| 2024 July | 17 | 2 | 19 |
| 2024 June | 28 | 10 | 38 |
| 2024 May | 64 | 18 | 82 |
| 2024 April | 54 | 21 | 75 |
