Expert consensus on feasibility and application of automatic pain assessment in routine clinical use
Abstract Background Pain is often difficult to assess, particularly in non-communicative patients. While artificial intelligence (AI)-based objective Automatic Pain Assessment (APA) systems are a promising solution, their clinical implementation raises essential questions, primarily regarding clinic...
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2025-06-01
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| Series: | Journal of Anesthesia, Analgesia and Critical Care |
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| Online Access: | https://doi.org/10.1186/s44158-025-00249-8 |
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| author | Marco Cascella Alfonso Maria Ponsiglione Vittorio Santoriello Maria Romano Valentina Cerrone Dalila Esposito Mario Montedoro Roberta Pellecchia Gennaro Savoia Giuliano Lo Bianco Massimo Innamorato Silvia Natoli Jonathan Montomoli Federico Semeraro Elena Giovanna Bignami Valentina Bellini Matteo Luigi Giuseppe Leoni Felice Occhigrossi Alessandro Vittori Maria Caterina Pace Pasquale Buonanno Mauro Forte Elisabetta Chinè Roberta Carpenedo Alessandro De Cassai Alfonso Papa Maurizio Marchesini Gaetano Terranova Fabrizio Micheli Laura Demartini Franco Marinangeli William Raffaeli Flaminia Coluzzi Andrea Tinnirello Roberto Arcioni Angelo Marra Mohammed Naveed Shariff Federica Monaco Gabriele Finco Alessia Bramanti Ornella Piazza |
| author_facet | Marco Cascella Alfonso Maria Ponsiglione Vittorio Santoriello Maria Romano Valentina Cerrone Dalila Esposito Mario Montedoro Roberta Pellecchia Gennaro Savoia Giuliano Lo Bianco Massimo Innamorato Silvia Natoli Jonathan Montomoli Federico Semeraro Elena Giovanna Bignami Valentina Bellini Matteo Luigi Giuseppe Leoni Felice Occhigrossi Alessandro Vittori Maria Caterina Pace Pasquale Buonanno Mauro Forte Elisabetta Chinè Roberta Carpenedo Alessandro De Cassai Alfonso Papa Maurizio Marchesini Gaetano Terranova Fabrizio Micheli Laura Demartini Franco Marinangeli William Raffaeli Flaminia Coluzzi Andrea Tinnirello Roberto Arcioni Angelo Marra Mohammed Naveed Shariff Federica Monaco Gabriele Finco Alessia Bramanti Ornella Piazza |
| author_sort | Marco Cascella |
| collection | DOAJ |
| description | Abstract Background Pain is often difficult to assess, particularly in non-communicative patients. While artificial intelligence (AI)-based objective Automatic Pain Assessment (APA) systems are a promising solution, their clinical implementation raises essential questions, primarily regarding clinician acceptance. Methods We conducted a survey-to-consensus investigation on the feasibility and application of APA for clinical use. Firstly, the steering committee implemented the CHERRIES guidelines and designed a questionnaire for healthcare professionals. Given the survey results, 26 experts in pain medicine were asked to participate in a two-round consensus by rating 10 statements through a 7-point Likert scale. Consensus was defined as ≥ 75% agreement (“agree” or “completely agree”). For both phases, data was collected through online questionnaires and analyzed quantitatively. Results For the survey, we collected responses from 628 healthcare professionals. The output highlighted excellent acceptance of the technology and a preference for multidimensional techniques. After two rounds, consensus was achieved on 8 out of 10 statements. Experts agreed on APA utility in supporting healthcare professionals and real-time pain monitoring. A strong consensus (96.2%) supported the need to inform patients about the use and limitations of AI systems. Adequate staff training is mandatory. Moreover, 92.3% agreed on the importance of implementing risk management, data quality control, and AI governance throughout the APA lifecycle. The experts stressed the need for internal and external validation processes and periodic updates, even for research purposes. Consensus was also reached about the importance of involving interdisciplinary stakeholders and addressing regulatory, ethical, and social implications. Multimodal inputs (e.g., physiological signals, facial expressions, speech, and clinical data) in APA systems are recommended. Additionally, APA systems should be capable of grading pain levels (e.g., via NRS), not just detecting the presence of pain. On the other hand, two statements did not reach consensus: the applicability of APA systems for acute and chronic pain conditions and their potential to improve therapeutic strategies. Conclusion APA is viewed as a promising and potentially feasible technology for clinical pain assessment, particularly in vulnerable populations. Further research is needed to validate the dedicated tools, define applications in different clinical conditions (e.g., acute and chronic pain), and demonstrate their impact on routine clinical practice for pain management. |
| format | Article |
| id | doaj-art-46b8b0d985fe4080b1935bf3343e6dbc |
| institution | OA Journals |
| issn | 2731-3786 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Anesthesia, Analgesia and Critical Care |
| spelling | doaj-art-46b8b0d985fe4080b1935bf3343e6dbc2025-08-20T02:31:00ZengBMCJournal of Anesthesia, Analgesia and Critical Care2731-37862025-06-015111210.1186/s44158-025-00249-8Expert consensus on feasibility and application of automatic pain assessment in routine clinical useMarco Cascella0Alfonso Maria Ponsiglione1Vittorio Santoriello2Maria Romano3Valentina Cerrone4Dalila Esposito5Mario Montedoro6Roberta Pellecchia7Gennaro Savoia8Giuliano Lo Bianco9Massimo Innamorato10Silvia Natoli11Jonathan Montomoli12Federico Semeraro13Elena Giovanna Bignami14Valentina Bellini15Matteo Luigi Giuseppe Leoni16Felice Occhigrossi17Alessandro Vittori18Maria Caterina Pace19Pasquale Buonanno20Mauro Forte21Elisabetta Chinè22Roberta Carpenedo23Alessandro De Cassai24Alfonso Papa25Maurizio Marchesini26Gaetano Terranova27Fabrizio Micheli28Laura Demartini29Franco Marinangeli30William Raffaeli31Flaminia Coluzzi32Andrea Tinnirello33Roberto Arcioni34Angelo Marra35Mohammed Naveed Shariff36Federica Monaco37Gabriele Finco38Alessia Bramanti39Ornella Piazza40Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, Anesthesia and Pain Medicine, University of SalernoDepartment of Electrical Engineering and Information Technology, University of Naples Federico IIDepartment of Electrical Engineering and Information Technology, University of Naples Federico IIDepartment of Electrical Engineering and Information Technology, University of Naples Federico IIDepartment of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, Anesthesia and Pain Medicine, University of SalernoDepartment of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, Anesthesia and Pain Medicine, University of SalernoDepartment of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, Anesthesia and Pain Medicine, University of SalernoDepartment of Electrical Engineering and Information Technology, University of Naples Federico IIIndependent ScholarAnesthesiology and Pain Department, Foundation G. Giglio CefalùDepartment of Neuroscience, AUSL Romagna, Pain Unit, Santa Maria Delle Croci HospitalDepartment of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of PaviaDivision of Anesthesiology and Intensive Care, Infermi Hospital, AUSL Romagna Department of Anesthesia, Intensive Care and Prehospital Emergency Maggiore Hospital Carlo Alberto PizzardiDepartment of Medicine and Surgery, Anesthesiology, Critical Care and Pain Medicine Division, University of ParmaDepartment of Medicine and Surgery, Anesthesiology, Critical Care and Pain Medicine Division, University of ParmaDepartment of Medical and Surgical Sciences and Translational Medicine, La Sapienza” University of RomePain Therapy Unit, San Giovanni-Addolorata HospitalDepartment of Anesthesia, Critical Care and Pain Medicine, ARCO, Ospedale Pediatrico Bambino Gesù IRCCSDepartment of Woman, Child and General and Specialized Surgery, University of Campania “Luigi Vanvitelli”Independent ScholarDepartment of Woman, Child and General and Specialized Surgery, University of Campania “Luigi Vanvitelli”Unit of Pain Therapy, Polyclinic of Tor VergataUnit of Pain Therapy, Polyclinic of Tor VergataDepartment of Medicine (DIMED), University of PaduaDepartment of Pain Management, AO “Ospedale Dei Colli”, Monaldi HospitalDepartment of Anesthesia and Pain Medicine, Mater Olbia HospitalAnaesthesia and Intensive Care Department, Asst Gaetano PiniUnit of Interventional and Surgical Pain Management, Guglielmo da Saliceto HospitalPain Unit, IRCCS MaugeriDepartment of Anesthesiology, Pain Treatment, Intensive and Palliative Care, University of L’AquilaInstitute for Research On Pain, ISAL FoundationDepartment of Medical and Surgical Sciences and Biotechnologies, Unit of Anesthesia, Intensive Care and Pain Medicine, Sapienza University of RomeAnesthesiology and Pain Medicine Department, ASST Franciacorta, Ospedale Di IseoSultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCR)Clinical Engineering, AOU San Giovanni Di Dio e Ruggi d’AragonaDepartment of AI&DS, Rajalakshmi Institute of TechnologyDepartment of AnesthesiaDepartment of Medical Science and Public Health, University of CagliariDepartment of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of SalernoDepartment of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, Anesthesia and Pain Medicine, University of SalernoAbstract Background Pain is often difficult to assess, particularly in non-communicative patients. While artificial intelligence (AI)-based objective Automatic Pain Assessment (APA) systems are a promising solution, their clinical implementation raises essential questions, primarily regarding clinician acceptance. Methods We conducted a survey-to-consensus investigation on the feasibility and application of APA for clinical use. Firstly, the steering committee implemented the CHERRIES guidelines and designed a questionnaire for healthcare professionals. Given the survey results, 26 experts in pain medicine were asked to participate in a two-round consensus by rating 10 statements through a 7-point Likert scale. Consensus was defined as ≥ 75% agreement (“agree” or “completely agree”). For both phases, data was collected through online questionnaires and analyzed quantitatively. Results For the survey, we collected responses from 628 healthcare professionals. The output highlighted excellent acceptance of the technology and a preference for multidimensional techniques. After two rounds, consensus was achieved on 8 out of 10 statements. Experts agreed on APA utility in supporting healthcare professionals and real-time pain monitoring. A strong consensus (96.2%) supported the need to inform patients about the use and limitations of AI systems. Adequate staff training is mandatory. Moreover, 92.3% agreed on the importance of implementing risk management, data quality control, and AI governance throughout the APA lifecycle. The experts stressed the need for internal and external validation processes and periodic updates, even for research purposes. Consensus was also reached about the importance of involving interdisciplinary stakeholders and addressing regulatory, ethical, and social implications. Multimodal inputs (e.g., physiological signals, facial expressions, speech, and clinical data) in APA systems are recommended. Additionally, APA systems should be capable of grading pain levels (e.g., via NRS), not just detecting the presence of pain. On the other hand, two statements did not reach consensus: the applicability of APA systems for acute and chronic pain conditions and their potential to improve therapeutic strategies. Conclusion APA is viewed as a promising and potentially feasible technology for clinical pain assessment, particularly in vulnerable populations. Further research is needed to validate the dedicated tools, define applications in different clinical conditions (e.g., acute and chronic pain), and demonstrate their impact on routine clinical practice for pain management.https://doi.org/10.1186/s44158-025-00249-8Artificial intelligenceAutomatic pain assessmentPainPediatric painOpioidPain therapy |
| spellingShingle | Marco Cascella Alfonso Maria Ponsiglione Vittorio Santoriello Maria Romano Valentina Cerrone Dalila Esposito Mario Montedoro Roberta Pellecchia Gennaro Savoia Giuliano Lo Bianco Massimo Innamorato Silvia Natoli Jonathan Montomoli Federico Semeraro Elena Giovanna Bignami Valentina Bellini Matteo Luigi Giuseppe Leoni Felice Occhigrossi Alessandro Vittori Maria Caterina Pace Pasquale Buonanno Mauro Forte Elisabetta Chinè Roberta Carpenedo Alessandro De Cassai Alfonso Papa Maurizio Marchesini Gaetano Terranova Fabrizio Micheli Laura Demartini Franco Marinangeli William Raffaeli Flaminia Coluzzi Andrea Tinnirello Roberto Arcioni Angelo Marra Mohammed Naveed Shariff Federica Monaco Gabriele Finco Alessia Bramanti Ornella Piazza Expert consensus on feasibility and application of automatic pain assessment in routine clinical use Journal of Anesthesia, Analgesia and Critical Care Artificial intelligence Automatic pain assessment Pain Pediatric pain Opioid Pain therapy |
| title | Expert consensus on feasibility and application of automatic pain assessment in routine clinical use |
| title_full | Expert consensus on feasibility and application of automatic pain assessment in routine clinical use |
| title_fullStr | Expert consensus on feasibility and application of automatic pain assessment in routine clinical use |
| title_full_unstemmed | Expert consensus on feasibility and application of automatic pain assessment in routine clinical use |
| title_short | Expert consensus on feasibility and application of automatic pain assessment in routine clinical use |
| title_sort | expert consensus on feasibility and application of automatic pain assessment in routine clinical use |
| topic | Artificial intelligence Automatic pain assessment Pain Pediatric pain Opioid Pain therapy |
| url | https://doi.org/10.1186/s44158-025-00249-8 |
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