Summary of findings about implementation: How AI was implemented varied; local service context influenced how AI was used. Implementation requires resources, time and collaboration between NHS staff & AI suppliers. Need to ensure that AI tools selected address challenges faced by services.

Summary of findings about impact: AI is effective at improving x-ray reporting times for likely cancer cases. The impact on reporting times may depend on other changes brought in by trusts. AI’s impact on the number of follow up referrals varied by trust, but not by ethnicity.

Summary of findings about cost: Staff time was not reduced in current implementation. Project management, IT and monitoring were the main implementation costs. AI was cost-effective overall but had negligible health benefits.

Our verdict: AI has potential to be implemented at scale, with the right resourcing. But more needs to be understood about the impact on care and patient outcomes.

With the right resourcing, AI has the potential to be implemented at scale in NHS chest diagnostics.

We've summarised findings from the #RSET evaluation of the AI Diagnostics Fund.

Explore the infographic in full: buff.ly/wvohcAp

Read the report: buff.ly/MPkSorm

Evaluation of AI in chest diagnostics: a Q&A with Angus Ramsay, Chris Sherlaw-Johnson and Kevin Herbert As the NIHR Rapid Service Evaluation Team (RSET) today publish their new mixed-methods evaluation of artificial intelligence in chest diagnostics, we spoke to Angus Ramsay, Chris Sherlaw-Johnson and…

With the potential uses for AI tools being explored across the NHS, analysts are working hard to build the evidence base needed to inform decision‑making. 🔍️

Read this week’s Q&A with #RSET researchers to learn about their work evaluating the use of AI tools in chest diagnostics.

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Ambient voice technology in health care: what’s the evidence so far? With findings from the first phase of a rapid evaluation of ambient voice technology in the NHS published today, co-authors Jenny Shand and Steve Morris describe the most notable insights so far and…

With AI notetaking tools being rolled out at pace to free up capacity in clinical settings, further research is needed to understand the implications.

#RSET have been exploring how we can measure the benefits offered by ambient voice technology.

Read the blog. 👇

Ambient voice technology in health care: what’s the evidence so far? With findings from the first phase of a rapid evaluation of ambient voice technology in the NHS published today, co-authors Jenny Shand and Steve Morris describe the most notable insights so far and o...

New Blog: Ambient voice technology is helping to reduce the time clinicians are spending on their notetaking, across a range of health care settings. 🗒️

But how can we know whether time savings are being translated into better health care?

Read the #RSET blog for insights from our research so far.

NEW STUDY: Patient-initiated follow-up, an innovative approach to help meet demand for elective care in England's NHS, has been studied by #RSET to understand the experience and perspective of the staff implementing it. 🔍️

Read the paper: buff.ly/2gGDGup

The latest study by the NIHR #RSET team explores how to best monitor and measure the effectiveness of peer-support schemes for social care in prisons in England and Wales.

Read the paper and watch our video overview 👉 buff.ly/YaoIdCO

VR技術を駆使した新たな災害訓練ソフト「RSET」の魅力と創業者の来日 VRを用いてリアルな災害訓練を可能にする「RSET」。創業者のビル・グレゴリー氏が来日し、次世代技術について語ります。

VR技術を駆使した新たな災害訓練ソフト「RSET」の魅力と創業者の来日 #大阪府 #大阪市 #災害訓練 #RSET #ビル・グレゴリー

VRを用いてリアルな災害訓練を可能にする「RSET」。創業者のビル・グレゴリー氏が来日し、次世代技術について語ります。

Procurement and early deployment of artificial intelligence tools for chest diagnostics in NHS services in England: a rapid, mixed method evaluation Skip to main content

There is much to consider when implementing #AI in health care 🔍

Read the recent paper from the NIHR #RSET team for insights and lessons from the implementation of AI tools for chest diagnostics in the #NHS 👇

Procurement and early deployment of artificial intelligence tools for chest diagnostics in NHS services in England: a rapid, mixed method evaluation Skip to main content

🆕 #RSET paper finds #AI diagnostic tools might not be a silver bullet for health care service pressures.

AI tools can offer valuable support, but can be hard to implement.

The paper identifies important lessons to help future implementation.

@nihr.bsky.social funded with @ucl.ac.uk @cam.ac.uk

Mixed-methods evaluation of the Maternity and Neonatal Independent Senior Advocate (MNISA) pilot in England The Maternity and Neonatal Independent Senior Advocate (MNISA) role is being piloted across England following a series of high-profile maternity reviews. The role is designed to support families who h...

Together with #Sands, the #RSET team evaluated the Maternity and Neonatal Independent Senior Advocate role that's being piloted to support families after adverse outcomes in NHS maternity and neonatal care.

Families overwhelmingly valued the service, but there are areas for improvement.

Curb your enthusiasm: what does the evidence tell us about using AI in radiology diagnostics? With hopes for the far-reaching impact of artificial intelligence (AI) on health care remaining as strong as ever, the NIHR Rapid Service Evaluation Team have conducted a review of the literature on…

The early signs for #AI in health care are promising, but tools need to be problem-driven, with specific challenges in mind.

Our blog from Emma Dodsworth and @racheljlawrence.bsky.social looks at AI in #radiology diagnostics, summarising the #RSET team's work.

Read the blog 👇
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There are high hopes for #AI and its potential to help the #NHS address productivity challenges.

The #RSET team have reviewed the evidence for AI in #radiology diagnostics - read the blog from Emma Dodsworth and @racheljlawrence.bsky.social for the key insights from the research 👇

buff.ly/dMwTGyA

Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping review The aim of this review was to evaluate evidence on the use of Artificial Intelligence (AI) to support diagnostics in radiology, including implementati…

Find out more about the #RSET work on AI in radiology in this paper. 👇

www.sciencedirect.com/science/arti...

Curb your enthusiasm: what does the evidence tell us about using AI in radiology diagnostics? With hopes for the far-reaching impact of artificial intelligence (AI) on health care remaining as strong as ever, the NIHR Rapid Service Evaluation Team have conducted a review of the literature on…

NEW BLOG: With hopes for the impact of #AI on health care remaining as strong as ever, the NIHR #RSET team have been exploring the role of AI in #radiology diagnostics.

Emma Dodsworth and Rachel Lawrence describe the three findings from the work that stood out the most. 👇
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Our #RSET team have been looking at services in prisons where prisoner ‘buddies’ are trained to provide social care support for other prisoners.

The final report from this study is out now: https://buff.ly/4aN0anw

Watch this video for a quick overview 👇 https://buff.ly/4hJhqME

Muhammad Ghayas Khan on LinkedIn: 🔥 Understanding ASET vs RSET: Key to Safe and Efficient… 🔥 Understanding ASET vs RSET: Key to Safe and Efficient Evacuations Imagine a building catching fire during a busy workday. How long do the occupants have to…

Mooie plaat om aan te geven dat beschikbare #vluchttijd altijd minimaal zo groot moet zijn als benodigde vluchttijd. Liefst 2x zo groot. #ASET is 2x #RSET www.linkedin.com/posts/muhamm...