Alessandra Retico
INFN
Sezione di Pisa
Artificial Intelligence in Medicine:
focus on Multi-Input Analysis
Plenary meeting, March 2nd 2026
Outline
Outline
→ deadline for project report (consuntivi) March 25th 2026
A. Retico
AIM_MIA
Artificial Intelligence in Medicine: focus on Multi-Input Analysis
2
Artificial Intelligence in Medicine: �focus on Multi-Input Analysis (AIM_MIA)
Funded by INFN, CSN5: 2025-2027
Objectives
⇒ FAIR Guiding Principles:
Scientific data should be Findable,
Accessible, Interoperable, and Reusable
General goal: to take a step forward in the development and validation of AI-based tools for medical data analysis
Resp. Naz.: A. Retico
13 Research Units 2026:
Bari (S. Tangaro)� Bologna (D. Remondini)� Cagliari (B. Golosio)� Catania (M. Marrale)� Ferrara (G. Di Domenico)� Firenze (C. Talamonti)� Genova (A. Chincarini)� Lecce (G. De Nunzio)
LNS (G. Russo)
Milano (C. Lenardi)
Napoli.dz (G. Mettivier)
Pavia (A. Lascialfari)
Pisa (M.E. Fantacci)
(eXtensible Neuroimaging
Archive Toolkit)
People
Report 2025
Plans 2026
A. Retico
AIM_MIA
Artificial Intelligence in Medicine: focus on Multi-Input Analysis
4
2025
85 persone, 27.45 FTE (0.32 FTE per persona)
2026
94 persone, 34 FTE (0.36 FTE per persona)
Budget
Report 2025
Plans 2026
A. Retico
AIM_MIA
Artificial Intelligence in Medicine: focus on Multi-Input Analysis
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Risorse di calcolo su infrastruttura INFN nazionale ReCaS
Budget 2025
| Sede | Miss | Miss SJ | Cons | Pub | Pub SJ | Manut | Invent | Lic SW | Tot | Tot SJ |
| BA | 3 | | 1 | | | | | | 4 | |
| BO | 3 | | 1 | | | | | | 4 | |
| CA | 5 | | 2 | | | | | 0.5 | 7.5 | |
| CT | 3 | | 2 | | | | | | 5 | |
| FE | 2 | | 2 | | | | | | 4 | |
| FI | 5 | | 2 | | | | | | 7 | |
| GE | 2 | 2 | 1.5 | | | 2 | | 0.5 | 6 | 2 |
| LE | 2 | | 0.5 | | | | | | 2.5 | |
| LNS | 2 | | 1 | | | | | | 3 | |
| MI | 2.5 | | 2.5 | | | | | | 5 | |
| PI | 7 | | 2 | | 10 | | | 0.5 | 9.5 | 10 |
| PV | 5 | | 2 | | | | 2 | | 9 | |
| Tot | 41.5 | 2 | 19.5 | | 10 | 2 | 2 | 1.5 | 66.5 | 12 |
Tot usufruito: 59.5 kE
Risorse di calcolo (~8.5 kE) su infrastruttura INFN nazionale ReCaS
| Sede | Miss | Miss SJ | Cons | Sem | Sem SJ | Pub | Pub SJ | Invent | Tot | Tot SJ |
| BA | 3 | | 1 | | | | | | 4 | |
| BO | 2 | 1 | 1 | | | | | 1 | 4 | 1 |
| CA | 2 | | | | | | | | 2 | |
| CT | 3 | | 2 | | | | | | 5 | |
| FE | 2 | | 1 | | | | | | 3 | |
| FI | 3 | | 1 | | 5 | | | | 4 | 5 |
| GE | 2 | | 2 | | | | | | 4 | |
| LE | 2 | | 0.5 | | | | | | 2.5 | |
| LNS | 2 | | 1 | | | | | 3.5 | 6.5 | |
| MI | 2.5 | | 2 | | | | | | 4.5 | |
| NA | 1.5 | | 0.5 | | | | | | 2 | |
| PI | 7 | 3 | 2 | | | | 10 | | 9 | 13 |
| PV | 4 | | 1.5 | | | | | 5 | 10.5 | |
| Tot | 36 | 4 | 15.5 | | 5 | | 10 | 9.5 | 61 | 19 |
Organization of webinars on AI with AIFM
Publication fees (SJ) concentrated in PI
richiesto 2025
richiesto 2026
Budget 2026
Budget 2026 - dettaglio sede per sede
Report 2025
Plans 2026
A. Retico
AIM_MIA
Artificial Intelligence in Medicine: focus on Multi-Input Analysis
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assegnati 7 kE SJ sotto dot5 a PI
Report 2025: WP and task coordinators
Gantt 2025 with coordination roles
Report 2025
Plans 2026
A. Retico
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Artificial Intelligence in Medicine: focus on Multi-Input Analysis
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Sept. 12, 2025
Report 2025: activity WP1 and WP2
Final goal: to find research objectives to collaborate on by dividing the tasks according toa the specific skills of the different research groups
Report 2025
Plans 2026
A. Retico
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Artificial Intelligence in Medicine: focus on Multi-Input Analysis
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Sept. 12, 2025
WP1. Mining multi-modal information
Chair: F. Sensi (GE)
Task1.1 Feature-based approach to multi-input analysis
Task leader: P. Oliva (PI)
Task1.2 Integration of multi-parametric and multi-modal imaging data
Task leader: C. Talamonti (FI)
Task1.3 AI solutions for heterogeneous data analysis
Task leader: M. Marrale (CT)
| WP1 | | WP2 | | | |
(Multi-)input data | Method | Goal | Method | Goal | Clinical area | Group |
CT, PET | Radiomics + ML, XAI | improve diagnostic power | | | Lung Tumor | BA |
MRI T1, T2, clinical data | ML and feature selection (DnetPRO) | data compression | | | | BO |
CT, CT perfusion | image analysis | quantification | | | | BO |
DWI | | | GAN | improve angular resolution | | BO |
MRI, fMRI | Radiomics + ML, XAI | case-control classification | | | ASD | CA |
MRI T1 | Radiomics + ML, XAI | case-control classification | | | Alzheimer | CT |
MRI T1 | Unet | segmentation of brain structures | | | Parkinson | CT |
CT sinogram | | | VAE | data curation (e.g., artifacts) | | FE |
CT, PET, MRI, dose | Radiomics + ML, XAI | metastasis prediction | | | soft tissue sarcoma | FI |
FDG-PET, MRI, clinical data | Image processing, statistical analysis and ML | quantification | | | proteinopathiae | GE |
PT-tau, MRI, clinical data | Graph analysis, Random Forest | patient outcome | | | multiple sclerosis | GE |
DCE-MRI, clinical data | Radiomics + ML, XAI; federated learning | detection of TNBC | traditional techniques, | harmonization, missing data curation, sample balanching | breast cancer | LE |
PET/CT | MatRadiomics; Radiomics + ML | segmentation; classification | | | breast cancer | LNS |
CT, clinical data | Radiomics + ML, Lasso, XAI | response to therapy | | | hepatocellular carcinoma | MI |
MRI, T1 T2 FLAIR | Radiomics + ML, XAI; SAM | classification; segmentation | | | breast cancer | PI |
CT | 2D, 3D Unet | segmentation | | | pancreas tumor | PI |
CT | 2D, 3D Unet | segmentation | | | lung tumor | PI |
WP2. Handling incomplete/missing/limited datasets
Chair: C. Testa (BO)
Task2.1 Traditional approaches for data curation and augmentation
Task leader: G. De Nunzio (LE)
Task2.2 Medical Image Data Generation
Task leader: C. Testa (BO)
Task2.3 Data inpainting with CNN
Task leader: G. Di Domenico (FE)
Report 2025: activity WP3
Report 2025
Plans 2026
A. Retico
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Artificial Intelligence in Medicine: focus on Multi-Input Analysis
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Sept. 12, 2025
It will be based on an open-source imaging IT platform developed by Washington University for neuroimaging data analysis: XNAT
Thanks to its extensibility, XNAT can be used to support a wide range of imaging-based projects.
T3.1 Definition of requirements and user roles
T3.2 Realization of the data platform prototype
WP3. Data platform and computing infrastructure
Chair: F. Lizzi (PI)
Task3.1 Definition of requirements and user roles
Task leader: C. Scapicchio (PI)
Task3.2 Realization and maintenance of the data platform prototype
Task leader: A. Formuso (PI)
Task3.3 Integration of data processing pipelines and output storing
Task leader: F. Sensi (GE)
Task3.4 SW organization and repository
Task leader: I. Postuma (PV)
Task3.5 Data collection
Task leader: N. Curti (BO)
Report 2025: activity WP3
Report 2025
Plans 2026
A. Retico
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Artificial Intelligence in Medicine: focus on Multi-Input Analysis
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Sept. 12, 2025
T3.3 Integration of data processing pipelines and output storing (starts in July 2026)
T3.4 SW organization and repository
T3.5 Data collection
WP3. Data platform and computing infrastructure
Chair: F. Lizzi (PI)
Task3.1 Definition of requirements and user roles
Task leader: C. Scapicchio (PI)
Task3.2 Realization and maintenance of the data platform prototype
Task leader: A. Formuso (PI)
Task3.3 Integration of data processing pipelines and output storing
Task leader: F. Sensi (GE)
Task3.4 SW organization and repository
Task leader: I. Postuma (PV)
Task3.5 Data collection
Task leader: N. Curti (BO)
Report 2025: motivation and ideas for dataset selection
Priority was given to these 4 datasets:
The upload of other datasets of interest will follow during 2026
Report 2025
Plans 2026
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Sept. 12, 2025
Report 2025: activity WP4
Report 2025
Plans 2026
A. Retico
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Sept. 12, 2025
WP4. Project management, outreach and networking
Chair: A. Retico (PI)
Task4.1. Project management and networking
Task leader: A. Retico (PI)
Task4.2. Collaboration with AIFM and other associations
Task leaders: C. Talamonti (FI), A. Chincarini (GE);
Task4.3. Outreach
Task leader: ME Fantacci
WP4 | Project management, outreach and collaborations with external parties | Task coordinator(s) | Task participants | Activity |
| T4.1 Project management and networking (A. Retico) | A. Retico | local group coordinators and WP leaders | The board of group coordinators and WP chairs:
The group coordinators and WP chairs organize local meetings and WP meetings, respectively All participants to the AIM_MIA project are included in the aim-all@pi.infn.it |
| T4.2 Collaboration with AIFM and medical associations | C. Talamonti, A. Chincarini | all groups |
|
| T4.3 Outreach | M.E. Fantacci | all groups | All groups are involved in outreach activities (Scienzestate 2025, 11-12 June 2025, Firenze Bright Night 2025, Sept 26th, some sections) |
Papers 2025
Report 2025
Plans 2026
A. Retico
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Artificial Intelligence in Medicine: focus on Multi-Input Analysis
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Sept. 12, 2025
Berti, Andrea et al. | An explainable-by-design end-to-end AI framework based on prototypical part learning for lesion detection and classification in Digital Breast Tomosynthesis images | COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL | 2025 |
Biondi, Riccardo et al. | Towards Precision Medicine in Sinonasal Tumors: Low-Dimensional Radiomic Signature Extraction from MRI | DIAGNOSTICS | 2025 |
Buzzi, Simone et al. | A Time-Series Approach for Machine Learning-Based Patient-Specific Quality Assurance of Radiosurgery Plans | BIOENGINEERING-BASEL | |
Cabini, Raffaella Fiamma et al. | A KINETIC APPROACH TO CONSENSUS-BASED SEGMENTATION OF BIOMEDICAL IMAGES | KINETIC AND RELATED MODELS | 2025 |
Damiani, Simone et al. | Deep Learning Denoising of Low-Dose Computed Tomography usingConvolutional Autoencoder: a Phantom Study | PROCEEDINGS OF THE 12th CONFERENCE ON BIOIMAGING | 2025 |
Lossano, Simone et al. | Generative super-resolution AI accelerates nanoscale analysis of cells | MACHINE LEARNING-SCIENCE AND TECHNOLOGY | 2025 |
Marini, Lorenzo et al. | Artificial intelligence for in-vivo dosimetry using EPID in external beam photon radiotherapy | RADIOTHERAPY AND ONCOLOGY | 2025 |
Novielli, Pierfrancesco et al. | Personalized colorectal cancer risk assessment through explainable AI and Gut microbiome profiling | GUT MICROBES | 2025 |
Pelagotti, Virginia et al. | [18F]FDG PET metabolic correlates of cerebrospinal fluid TAR DNA-binding protein 43 in prodromal Alzheimer's disease | NEUROBIOLOGY OF DISEASE | 2025 |
Ricchi, Mattia et al. | Connectivity related to major brain functions in Alzheimer disease progression: microstructural properties of the cingulum bundle and its subdivision using diffusion-weighted MRI | EUROPEAN RADIOLOGY EXPERIMENTAL | 2025 |
Ricchi, Mattia et al. | Assessment of a diffusion phantom for quality assurance in brain microstructure diffusion MRI studies | SCIENTIFIC REPORTS | 2025 |
Tenerani, Maria Irene et al. | Use of radiomics in low dose chest CT: a proposal for a phantom multi-centric study | PROCEEDINGS OF THE 12th CONFERENCE ON BIOIMAGING | 2025 |
Tiddia, Gianmarco et al. | Explainable AI Highlights the Most Relevant Gait Features for Neurodegenerative Disease Classification | APPLIED SCIENCES-BASEL | 2025 |
Tsewalo Tondji Idriss Cabrel et al. | 2.5D Deep Learning Model with Attention Mechanism for Pancreas Segmentation on CT Scans | Proc. 18th International Joint Conference on Biomedical Engineering Systems and Technologies | 2025 |
Status on Sept. 2025: to be completed
Theses 2025
Tesi Triennali
Matteo Bergamaschi (UniMI), Analisi di trattamenti chemioterapici di lesioni al fegato con tecniche di machine learning
Mounia FELALI BELGHITI (UniFI), L'intelligenza artificiale in medicina: le nuove responsabilità del tecnico sanitario di radiologia medica
Tesi Magistrali
Simone Santoro, (UniBO), Development of new approaches for generating synthetic Diffusion Gradient Directions in Diffusion Tensor Imaging
Giuseppe Antonio Motisi (UniPI), Human Connectome Analysis with Graph Neural Networks: the Autism Spectrum Disorder case study
Simone Damiani (UniPI), Denoising of Chest CT (Computed Tomography) images with a U-Net based Convolutional Autoencoder
Gianluca Morcaldi (UniPI), A machine learning approach to discriminate malignant and benign breast lesions using multimodal MRI
Martina Scagni (UniPV), Pre-processing, development, and evaluation of a convolutional neural network for diagnosing paranasal sinus diseases
Rosanna Tripi (UniPA, LNS), SEGMENTAZIONE DI IMMAGINI PRE-CLINICHE E CLINICHE ATTRAVERSO L’UTILIZZO DI DEEP NEURAL NETWORKS
Eleonora Giovagnoli (UniRM1, LNS), Automated Classification of Breast Tumors by a Radiomic Approach with matRadiomics toolbox to Distinguish Benign from Malignant Lesions
Lorenzo Piana (UniGE) Sviluppo di un modello dinamico a compartimenti per la valutazione delle malattie dei giunti in pazienti pediatrici
Tesi di Dottorato
Andrea Berti (UniPI), Towards Trustworthy AI in Healthcare: Transparent and Interpretable Models for Medical Imaging
Carlo Golini (UniBO), CT-based radiomics for differentiating post trombectomy hyperdensities
Matteo Parodi (UniGE) Generazione di un sistema di coordinate "clinically-driven" per la valutazione delle proteinopatie in dati multimodali
..
Report 2025
Plans 2026
A. Retico
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Sept. 12, 2025
Status on Sept. 2025: to be completed
Report 2025: Milestones 2025
Report 2025
Plans 2026
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Deadline | Milestones | Achievements | Proposed % of completion (Sept 2025 → today) | |
December 31, 2025 | M1.1 | Development of a DL-based analysis pipeline for feature extracted from multiple sources and release to the SW repository | Several analysis pipelines have been implemented for multi-input data (func, struct, blood, gene data) and are under testing. Maturity of each solution will be evaluated to find readiest candidate for the release in the SW repository | 40% → ??? |
December 31, 2025 | M2.1 | Implementation and release to the SW repository of traditional data augmentation and data imputation techniques. | Traditional data augmentation techniques have been implemented in many SW packages developed by the research group members. The integration on the SW repository has to be completed | 40% → ??? |
March 31, 2025 | M3.1a | Initialization of the shared SW repository and release of guidelines for contributors | 90% → ??? | |
December 31, 2025 | M3.1b | Release of an internal note reporting the guidelines to set up the data platform (definition of the requirements, user roles, allowed data types) | Set-up information have been collected (including requirements, user roles, allowed data types); they have to be organized in a harmonic report → https://confluence.infn.it/spaces/XNAT/pages/695107644/Getting+started | 80% → 100% |
March 31, 2025 | M4.1 | Kick-off meeting organization and release of a plan of collaboration with external parties | kick off meeting (Febr 2025) https://agenda.infn.it/event/45571/ Plan of collaboration not released | 80% → ??? |
Activity 2026: WP and task coordinators
Updated Gantt and Milestones 2026 with coordination roles and tasks of all groups:
Drive shared folder/
AIM_MIA_2025-2027/WORK/
ToDo sede per sede - Gantt e Milestones.xlsx
Report 2025
Plans 2026
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WP1. Mining multi-modal information
Chair: ….
Task1.1 Feature-based approach to multi-input analysis
Task leader: P. Oliva (PI)
Task1.2 Integration of multi-parametric and multi-modal imaging data
Task leader: C. Talamonti (FI)
Task1.3 AI solutions for heterogeneous data analysis
Task leader: M. Marrale (CT)
WP2. Handling incomplete/missing/limited datasets
Chair: C. Testa (BO)
Task2.1 Traditional approaches for data curation and augmentation
Task leader: G. De Nunzio (LE)
Task2.2 Medical Image Data Generation
Task leader: C. Testa (BO)
Task2.3 Data inpainting with CNN
Task leader: G. Di Domenico (FE)
WP3. Data platform and computing infrastructure
Chair: F. Lizzi (PI)
Task3.1 Definition of requirements and user roles
Task leader: C. Scapicchio (PI)
Task3.2 Realization and maintenance of the data platform prototype
Task leader: A. Formuso (PI)
Task3.3 Integration of data processing pipelines and output storing
Task leader: F. Laruina (PI)
Task3.4 SW organization and repository
Task leader: I. Postuma (PV)
Task3.5 Data collection
Task leader: N. Curti (BO)
WP4. Project management, outreach and networking
Chair: A. Retico (PI)
Task4.1. Project management and networking
Task leader: A. Retico (PI)
Task4.2. Collaboration with AIFM and other associations
Task leaders: C. Talamonti (FI), A. Chincarini (GE);
Task4.3. Outreach
Task leader: ME Fantacci
Plans 2026: WP1 and WP2
Several methods developed in 2025 can be generalized to work on datasets identified as of common scientific interests by different research groups
Report 2025
Plans 2026
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Sept. 12, 2025
WP1. Mining multi-modal information
Chair: …..
Task1.1 Feature-based approach to multi-input analysis
Task leader: P. Oliva (PI)
Task1.2 Integration of multi-parametric and multi-modal imaging data
Task leader: C. Talamonti (FI)
Task1.3 AI solutions for heterogeneous data analysis
Task leader: M. Marrale (CT)
| WP1 | | WP2 | | | |
(Multi-)input data | Method | Goal | Method | Goal | Clinical area | Group |
CT, PET | Complex networks | patients' stratification | | | | BA |
3D PET | | | GenAI | Improve spatial resolution, case-control classification | synthetic data | BO |
MRI, fMRI | Radiomics + ML, XAI | case-control classification | | | Clinical areas TBD | CA |
MRI, fMRI, clinical data | Radiomics + ML, XAI; CNN | case-control classification | | | Clinical areas TBD | CT |
CT sinogram | | | VAE | data curation (e.g., missing angles) | | FE |
CT, PET, MRI, dose | Radiomics + ML, XAI | metastasis prediction | | | soft tissue sarcoma | FI |
DCE-MRI, clinical data | Radiomics + ML, XAI; federated learning | detection of TNBC | GenAI | curation of missing clinical data | breast cancer | LE |
PET/CT, clinica data | Correlation between imaging and clinical data | predicton of patient's outcome; classification | | | breast cancer | LNS |
CT, clinical data | Radiomics + ML, Lasso, XAI | response to therapy | standard techniques | data augmentation | hepatocellular carcinoma | MI |
CT, MRI | Unet; Radiomics + ML | segmentation, prediction patient's outcome | | | pancreas tumor | PI |
micro-CT, CT | Radiomics | virtual biopsy | | | lung tumor | PV |
WP2. Handling incomplete/missing/limited datasets
Chair: C. Testa (BO)
Task2.1 Traditional approaches for data curation and augmentation
Task leader: G. De Nunzio (LE)
Task2.2 Medical Image Data Generation
Task leader: C. Testa (BO)
Task2.3 Data inpainting with CNN
Task leader: G. Di Domenico (FE)
Plans 2026: WP3
[T3.2] Definition of the final structure of the IT platform.
[T3.3] Development of a first strategy to integrate data processing pipelines (e.g. image pre-processing) to be executed directly from the platform and to store back the output.
[T3.4] Improve the quality of the SW repository and increment its population.
[T3.5] Upload of the public datasets of interest by the different participants.
Report 2025
Plans 2026
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Sept. 12, 2025
Plans 2026: WP4
Project management
Collaborations
Outreach
New projects
Report 2025
Plans 2026
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Sept. 12, 2025
Plans 2026: Milestones
Report 2025
Plans 2026
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Sept. 12, 2025
Deadline | Milestones | |
June 30, 2026 | M1.2.a | Identification of at least two scientific use cases requiring AI-based multi-input analysis approaches and assignment of tasks among developers from different sites |
June 30, 2026 | M2.2.a | Integration on the SW repository of prototype versions of the proposed data curation techniques |
June 30, 2026 | M3.2.a | Instantiation of a XNAT platform and integration of the main datasets of interest for collaborative analyses |
December 31, 2026 | M1.2.b | Report on the preliminary results obtained by the multi-input analysis approaches implemented in the two scientific cases of common interest |
December 31, 2026 | M2.2.b | Evaluation of the performance of the DL algorithms for sinogram curation and for increasing the DWI angular resolution |
December 31, 2026 | M3.2.b | Release of the user manual for the integration of analysis pipelines into the XNAT platform |
December 31, 2026 | M4.2 | Organization of a webinar series on AI impact in Medicine in collaboration with AIFM |
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Report 2025
Plans 2026
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Artificial Intelligence in Medicine: focus on Multi-Input Analysis
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To wrap up:
Report 2025
Plans 2026
Operative program for today (focus on 2026 activity):
To complete the milestones 2025 (to be completed before March 25th 2026 → https://consuntivi.dsi.infn.it/#/priv/landingPage):
A. Retico
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Artificial Intelligence in Medicine: focus on Multi-Input Analysis
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To wrap up:
Operative program for today:
To complete 2025 milestones:
Long-standing tasks to be completed:
Report 2025
Plans 2026
23
Expected impact of AIM_MIA
Expected impact
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