Use of artificial intelligence in critical care: opportunities and obstacles
Background Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-bas...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2024
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| Online Access: | https://hdl.handle.net/1721.1/154170 |
| _version_ | 1811072961887600640 |
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| author | Pinsky, Michael R. Bedoya, Armando Bihorac, Azra Celi, Leo Churpek, Matthew Economou-Zavlanos, Nicoleta J. Elbers, Paul Saria, Suchi Liu, Vincent Lyons, Patrick G. Shickel, Benjamin Toral, Patrick Tscholl, David Clermont, Gilles |
| author_facet | Pinsky, Michael R. Bedoya, Armando Bihorac, Azra Celi, Leo Churpek, Matthew Economou-Zavlanos, Nicoleta J. Elbers, Paul Saria, Suchi Liu, Vincent Lyons, Patrick G. Shickel, Benjamin Toral, Patrick Tscholl, David Clermont, Gilles |
| author_sort | Pinsky, Michael R. |
| collection | MIT |
| description | Background
Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed.
Main body
Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent “black-box” nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools.
Conclusions
AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development. |
| first_indexed | 2024-09-23T09:23:04Z |
| format | Article |
| id | mit-1721.1/154170 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:23:04Z |
| publishDate | 2024 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1541702024-04-18T03:57:25Z Use of artificial intelligence in critical care: opportunities and obstacles Pinsky, Michael R. Bedoya, Armando Bihorac, Azra Celi, Leo Churpek, Matthew Economou-Zavlanos, Nicoleta J. Elbers, Paul Saria, Suchi Liu, Vincent Lyons, Patrick G. Shickel, Benjamin Toral, Patrick Tscholl, David Clermont, Gilles Critical Care and Intensive Care Medicine Background Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed. Main body Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent “black-box” nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools. Conclusions AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development. 2024-04-17T12:35:20Z 2024-04-17T12:35:20Z 2024-04-08 2024-04-14T03:12:32Z Article http://purl.org/eprint/type/JournalArticle 1364-8535 https://hdl.handle.net/1721.1/154170 Pinsky, M.R., Bedoya, A., Bihorac, A. et al. Use of artificial intelligence in critical care: opportunities and obstacles. Crit Care 28, 113 (2024). PUBLISHER_CC en 10.1186/s13054-024-04860-z Critical Care Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer Science and Business Media LLC BioMed Central |
| spellingShingle | Critical Care and Intensive Care Medicine Pinsky, Michael R. Bedoya, Armando Bihorac, Azra Celi, Leo Churpek, Matthew Economou-Zavlanos, Nicoleta J. Elbers, Paul Saria, Suchi Liu, Vincent Lyons, Patrick G. Shickel, Benjamin Toral, Patrick Tscholl, David Clermont, Gilles Use of artificial intelligence in critical care: opportunities and obstacles |
| title | Use of artificial intelligence in critical care: opportunities and obstacles |
| title_full | Use of artificial intelligence in critical care: opportunities and obstacles |
| title_fullStr | Use of artificial intelligence in critical care: opportunities and obstacles |
| title_full_unstemmed | Use of artificial intelligence in critical care: opportunities and obstacles |
| title_short | Use of artificial intelligence in critical care: opportunities and obstacles |
| title_sort | use of artificial intelligence in critical care opportunities and obstacles |
| topic | Critical Care and Intensive Care Medicine |
| url | https://hdl.handle.net/1721.1/154170 |
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