The use of Artificial Intelligence (AI) for clinical pathway management and decision making is believed to improve clinical care and has been used to improve pathways for treatment in most medical disciplines. Methods...The use of Artificial Intelligence (AI) for clinical pathway management and decision making is believed to improve clinical care and has been used to improve pathways for treatment in most medical disciplines. Methods: A literature review was undertaken to identify the hurdles and steps required to introduce supported clinical decision-making using AI within hospitals. This was supported by a survey of local hospital practice within the Midlands of the United Kingdom to see what systems had been introduced and were functioning effectively. Results: It is unclear how to practically implement systems using AI within medicine easily. Algorithmic medicine based on a set of rules calculated from data only takes a clinician so far to deliver patient centred optimal treatment. AI facilitates a clinician’s ability to assimilate data from disparate sources and can help with some of the analysis and decision making. However, learning remains organic and the subtleties of difference between patients, care providers who exhibit non-verbal communication for instance make it difficult for an AI to capture all the pertinent information required to make the correct clinical decision for any given individual. Hence it assists rather than controls any process in clinical practice. It also must continually renew and adapt considering changes in practise and trends as the goalposts change to meet fluctuations in resources and workload. Precision surgery is benefiting from robotic-assisted surgery in parts driven by AI and being used in 80% of trusts locally. Conclusion: The use of AI in clinical practice remains patchy with it being adopted where research groups have studied a more effective method of monitoring or treatment. The use of robotic-assisted surgery on the other hand has been more rapid as the precision of treatment that this provides appears attractive in improving clinical care.展开更多
ChatGPT has obvious benefits in the way it can interrogate vast amounts of reference information and utilise metadata generation to answer questions posed to it and is freely available having been developed through hu...ChatGPT has obvious benefits in the way it can interrogate vast amounts of reference information and utilise metadata generation to answer questions posed to it and is freely available having been developed through human feedback. Already there are ethical and practical implications on its impact on learning and research. Artificial Intelligence (AI) has been seen as a way of improving healthcare provision by delivering more robust outcomes but measuring these and implementing AI within this setting is at present limited and disjointed. Methods: ChatGPT was interrogated to see what it felt were the barriers to its implementation within healthcare and in particular orthopaedic practice. The evidence for this determination was then examined for validity and applicability for a practical roll out at a Trust, Regional or National level. Results: AI can synthesise a vast amount of information to help it answer specific questions. The context and structure of any question will determine the usefulness of the answer which can then be used to develop practical solutions based on experience and resource limitations. Conclusions: AI has a role in service development and can quickly focus a working group to areas to consider when practically implementing change.展开更多
文摘The use of Artificial Intelligence (AI) for clinical pathway management and decision making is believed to improve clinical care and has been used to improve pathways for treatment in most medical disciplines. Methods: A literature review was undertaken to identify the hurdles and steps required to introduce supported clinical decision-making using AI within hospitals. This was supported by a survey of local hospital practice within the Midlands of the United Kingdom to see what systems had been introduced and were functioning effectively. Results: It is unclear how to practically implement systems using AI within medicine easily. Algorithmic medicine based on a set of rules calculated from data only takes a clinician so far to deliver patient centred optimal treatment. AI facilitates a clinician’s ability to assimilate data from disparate sources and can help with some of the analysis and decision making. However, learning remains organic and the subtleties of difference between patients, care providers who exhibit non-verbal communication for instance make it difficult for an AI to capture all the pertinent information required to make the correct clinical decision for any given individual. Hence it assists rather than controls any process in clinical practice. It also must continually renew and adapt considering changes in practise and trends as the goalposts change to meet fluctuations in resources and workload. Precision surgery is benefiting from robotic-assisted surgery in parts driven by AI and being used in 80% of trusts locally. Conclusion: The use of AI in clinical practice remains patchy with it being adopted where research groups have studied a more effective method of monitoring or treatment. The use of robotic-assisted surgery on the other hand has been more rapid as the precision of treatment that this provides appears attractive in improving clinical care.
文摘ChatGPT has obvious benefits in the way it can interrogate vast amounts of reference information and utilise metadata generation to answer questions posed to it and is freely available having been developed through human feedback. Already there are ethical and practical implications on its impact on learning and research. Artificial Intelligence (AI) has been seen as a way of improving healthcare provision by delivering more robust outcomes but measuring these and implementing AI within this setting is at present limited and disjointed. Methods: ChatGPT was interrogated to see what it felt were the barriers to its implementation within healthcare and in particular orthopaedic practice. The evidence for this determination was then examined for validity and applicability for a practical roll out at a Trust, Regional or National level. Results: AI can synthesise a vast amount of information to help it answer specific questions. The context and structure of any question will determine the usefulness of the answer which can then be used to develop practical solutions based on experience and resource limitations. Conclusions: AI has a role in service development and can quickly focus a working group to areas to consider when practically implementing change.
