Versions
- 2024-08-02 (2)
- 2024-08-02 (1)
Keywords
AI
Machine Learning
perspective
Abstract
The first annual meeting of the Italian Society for Artificial Intelligence in Medicine (Società Italiana Intelligenza Artificiale in Medicina, SIIAM) on December 7, 2023, marked a significant milestone in integrating artificial intelligence (AI) into Italy's healthcare framework. This paper reports on the collaborative workshop conducted during this event, highlighting the collective efforts of 51 professionals from diverse fields including medicine, engineering, data science, and law. The interdisciplinary nature of the participants played a crucial role in generating ideas for innovative AI solutions tailored to healthcare challenges.
Central to the discussions were several AI applications aimed at improving patient care and streamlining healthcare processes. Notably, the use of Large Language Models (LLMs) in remote monitoring of chronic patients emerged as an area of focus. These models promise enhanced patient monitoring through detailed symptom checking and anomaly detection, thereby facilitating timely medical interventions. Another significant proposal involved employing LLMs to improve empathy in medical communication, addressing the challenges posed by cultural diversity and high-stress levels among healthcare professionals.
Additionally, the development of Machine Learning algorithms for standardizing treatment in pediatric emergency departments was discussed, along with the need for educational initiatives to enhance AI adoption in rural healthcare settings. The workshop also explored using LLMs for efficient data extraction and analysis in scientific literature, interpreting healthcare norms, and streamlining Hospital Discharge Records.
These discussions underscored SIIAM's commitment to leveraging AI for healthcare innovation, setting a precedent for the future of healthcare in Italy. This paper provides a comprehensive overview of the ideas and solutions proposed at the workshop, reflecting the participants' forward-thinking vision and the potential of AI to revolutionize healthcare.
References
Moen A, Chronaki C, Petelos E, Voulgaraki D, Turk E, Névéol A. Diversity in Health Informatics: Mentoring and Leadership. Public Health and Informatics: Proceedings of MIE 2021 [Internet]. 2021 Jul 1 [cited 2024 Apr 19];1031–5. Available from: https://ebooks.iospress.nl/doi/10.3233/SHTI210341
Mitchell R, Boyle B, O’Brien R, Malik A, Tian K, Parker V, et al. Balancing cognitive diversity and mutual understanding in multidisciplinary teams. Health Care Manage Rev [Internet]. 2017 Jan 1 [cited 2024 Apr 19];42(1):42–52. Available from: https://journals.lww.com/hcmrjournal/fulltext/2017/01000/balancing_cognitive_diversity_and_mutual.5.aspx
Roberge MÉ, van Dick R. Recognizing the benefits of diversity: When and how does diversity increase group performance? Human Resource Management Review. 2010 Dec;20(4):295–308.
Chi WN, Reamer C, Gordon R, Sarswat N, Gupta C, White Vangompel E, et al. Continuous Remote Patient Monitoring: Evaluation of the Heart Failure Cascade Soft Launch. Appl Clin Inform [Internet]. 2021 Oct 1 [cited 2024 Apr 19];12(5):1161. Available from: /pmc/articles/PMC8716190/
Cascini F, Gentili A, Melnyk A, Beccia F, Causio FA, Solimene V, et al. A new digital model for the Italian Integrated Home Care: strengths, barriers, and future implications. Front Public Health [Internet]. 2023 [cited 2024 Apr 19];11. Available from: https://pubmed.ncbi.nlm.nih.gov/38035284/
Mohammed KI, Zaidan AA, Zaidan BB, Albahri OS, Alsalem MA, Albahri AS, et al. Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure. J Med Syst [Internet]. 2019 Jul 1 [cited 2024 Apr 19];43(7):1–21. Available from: https://link.springer.com/article/10.1007/s10916-019-1362-x
Montemayor C, Halpern J, Fairweather A. In principle obstacles for empathic AI: why we can’t replace human empathy in healthcare. AI Soc [Internet]. 2022 Dec 1 [cited 2024 Apr 19];37(4):1353–9. Available from: https://publichealth.berkeley.edu/news-media/research-highlights/can-we-replace-human-empathy-in-healthcare
Kerasidou A. Artificial intelligence and the ongoing need for empathy, compassion and trust in healthcare. Bull World Health Organ [Internet]. 2020 Apr 4 [cited 2024 Apr 19];98(4):245. Available from: /pmc/articles/PMC7133472/
Sauerbrei A, Kerasidou A, Lucivero F, Hallowell N. The impact of artificial intelligence on the person-centred, doctor-patient relationship: some problems and solutions. BMC Med Inform Decis Mak [Internet]. 2023 Dec 1 [cited 2024 Apr 19];23(1). Available from: /pmc/articles/PMC10116477/
West CP, Dyrbye LN, Shanafelt TD. Physician burnout: contributors, consequences and solutions. J Intern Med [Internet]. 2018 Jun 1 [cited 2024 Apr 19];283(6):516–29. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/joim.12752
Ayers JW, Poliak A, Dredze M, Leas EC, Zhu Z, Kelley JB, et al. Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. JAMA Intern Med [Internet]. 2023 Jun 1 [cited 2024 Apr 19];183(6):589–96. Available from: https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2804309
Zhan C, Miller MR. Administrative data based patient safety research: a critical review. BMJ Qual Saf [Internet]. 2003 Dec 1 [cited 2024 Apr 19];12(suppl 2):ii58–63. Available from: https://qualitysafety.bmj.com/content/12/suppl_2/ii58
Liévin V, Hother CE, Motzfeldt AG, Winther O. Can large language models reason about medical questions? Patterns [Internet]. 2022 Jul 17 [cited 2024 Apr 19];5(3). Available from: https://arxiv.org/abs/2207.08143v4
Alzahrani SA, Alzahrani AA, Al-Shamrani A. Artificial Intelligence in Paediatric Emergencies: A Narrative Review. American Journal of Pediatrics 2022, Volume 8, Page 51 [Internet]. 2022 Apr 9 [cited 2024 Apr 19];8(2):51–5. Available from: https://www.sciencepg.com/article/10.11648/j.ajp.20220802.11
Singh D, Nagaraj S, Mashouri P, Drysdale E, Fischer J, Goldenberg A, et al. Assessment of Machine Learning–Based Medical Directives to Expedite Care in Pediatric Emergency Medicine. JAMA Netw Open [Internet]. 2022 Mar 1 [cited 2024 Apr 19];5(3):e222599–e222599. Available from: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2790164
Leegon J, Jones I, Lanaghan K, Aronsky D. Predicting Hospital Admission in a Pediatric Emergency Department using an Artificial Neural Network. American Medical Informatics Association Annual Symposium. 2006;
Ramgopal S, Sanchez-Pinto LN, Horvat CM, Carroll MS, Luo Y, Florin TA. Artificial intelligence-based clinical decision support in pediatrics. Pediatr Res [Internet]. 2023 Jan 1 [cited 2024 Apr 19];93(2):334. Available from: /pmc/articles/PMC9668209/
Rafay MF, Pontigon AM, Chiang J, Adams M, Jarvis DA, Silver F, et al. Delay to Diagnosis in Acute Pediatric Arterial Ischemic Stroke. Stroke [Internet]. 2009 Jan 1 [cited 2024 Apr 19];40(1):58–64. Available from: https://www.ahajournals.org/doi/abs/10.1161/STROKEAHA.108.519066
Hosny A, Aerts HJWL. Artificial intelligence for global health. Science (1979) [Internet]. 2019 Nov 22 [cited 2024 Apr 19];366(6468):955–6. Available from: https://www.science.org/doi/10.1126/science.aay5189
Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput [Internet]. 2023 Jul 1 [cited 2024 Apr 19];14(7):8459. Available from: /pmc/articles/PMC8754556/
De Angelis L, Baglivo F, Arzilli G, Privitera GP, Ferragina P, Tozzi AE, et al. ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health. Front Public Health [Internet]. 2023 Apr 25 [cited 2024 Apr 19];11:1166120. Available from: https://api.semanticscholar.org/
Greenstein S. Preserving the rule of law in the era of artificial intelligence (AI). Artif Intell Law (Dordr). 2022 Sep 1;30(3):291–323.
Surden H. The Ethics of Artificial Intelligence in Law: Basic Questions [Internet]. 2019 [cited 2024 Apr 19]. Available from: https://papers.ssrn.com/abstract=3441303
Using health care audit to improve quality of clinical records: the preliminary experience of an Italian Cancer Institute | Annali dell’Istituto Superiore di Sanità [Internet]. [cited 2024 Apr 19]. Available from: https://annali.iss.it/index.php/anna/article/view/339
Weis JM, Levy PC. Copy, paste, and cloned notes in electronic health records;prevalence, benefi ts, risks, and best practice recommendations. Chest [Internet]. 2014 Mar 1 [cited 2024 Apr 19];145(3):632–8. Available from: http://journal.chestnet.org/article/S0012369215343786/fulltext