Editorial Commentary: Personalized Hip Arthroscopy Outcome Prediction Using Machine Learning—The Future Is Here

Harris, Joshua D.

doi:10.1016/j.arthro.2021.02.032

Cited by 10 publications

(2 citation statements)

References 23 publications

(30 reference statements)

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“…This was the strongest theme of the literature and conveyed manifold concerns about AI ethics and regulation ( Zelmer et al, 2018 ; Abdullah et al, 2021 ); ethical design and use of AI technologies in healthcare contexts ( Sikstrom et al, 2022 ); concerns about data privacy, data biases and data collection ( Harris, 2021 ; Ostherr, 2022 ); as well as concerns about trust, care quality, and liability ( Davenport and Kalakota, 2019 ; Sanal et al, 2019 ). There is a strong anticipation perspective relating to concerns about role replacement ( Johnston, 2018 ; Blease et al, 2019 ; Bridge and Bridge, 2019 ; Powell, 2019 ; Blease et al, 2020 ; Doraiswamy et al, 2020 ; Alrassi et al, 2021 ) and which parts of healthcare practice, can and should be entrusted to AI technologies ( Loftus et al, 2020 ; Nadin, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

et al. 2023

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BackgroundAdvances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored.ObjectivesThe aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare?Materials and methodsA systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011–2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice.ResultsSearches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan–Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships.ConclusionThere is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships.ImplicationsIn a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

et al. 2023

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“…Computer‐assisted surgeries allow surgeons not only to create a precise pre‐operative plan, but also increase accuracy of their implementation during surgery via complex navigation/robotic systems [30]. Likewise, the ability to predict individual patient outcomes using artificial intelligence and patient stratification preoperatively leading to personalised medicine holds great promise [10].…”

Section: Future Perspectives Of Hip Preservationmentioning

confidence: 99%

The growth of hip preservation as a speciality

Sienko

Ekhtiari

Khanduja

2023

Knee Surg Sports Traumatol Arthrosc

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Patient‐Reported Outcomes (PROM, PROMIS)

Pel‐Littel¹,

Hofman²

2022

The International Encyclopedia of Health Communication

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This entry provides an introduction to patient‐reported outcomes (PROs) and their significance. Different types of patient‐reported outcome measures (PROMs) are introduced, such as generic and specific PROMs, as well as the progression, evaluation, and future directions for their use. Even though there are various challenges with the implementation and use of PROMs (such as patients' motivation to complete a PROM and the shift from a disease/problem‐oriented approach in the healthcare conversations to an outcome/goal‐based approach), they can be overcome. PROMs are well established in their use for screening, monitoring, and improving communication at the individual (micro) level, and to aid in decision making, monitoring populations, and assessing quality at the aggregated (meso) level.

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Editorial Commentary: Personalized Hip Arthroscopy Outcome Prediction Using Machine Learning—The Future Is Here

Cited by 10 publications

References 23 publications

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

The growth of hip preservation as a speciality

Patient‐Reported Outcomes (PROM, PROMIS)

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