The DOI matches its Crossref record, with a small difference in title or year; 31 of the 31 references it cites were found in a registry or an index; 1 of the 27 sources its claims cite were checked for support.
Artificial intelligence: opportunities and implications for the health workforce
Open accessAbstract
Healthcare involves cyclic data processing to derive meaningful, actionable decisions. Rapid increases in clinical data have added to the occupational stress of healthcare workers, affecting their ability to provide quality and effective services. Health systems have to radically rethink strategies to ensure that staff are satisfied and actively supported in their jobs. Artificial intelligence (AI) has the potential to augment provider performance. This article reviews the available literature to identify AI opportunities that can potentially transform the role of healthcare providers. To leverage AI’s full potential, policymakers, industry, healthcare providers and patients have to address a new set of challenges. Optimizing the benefits of AI will require a balanced approach that enhances accountability and transparency while facilitating innovation.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review argues that AI can augment the health workforce by improving productivity, reducing administrative workload, supporting diagnosis and teamwork, and potentially increasing satisfaction. It also identifies new challenges in liability, labour markets, competencies, ethics, regulation, and the provider–patient relationship, calling for balanced governance that enhances accountability and transparency while fostering innovation.
Key findings
- AI may augment provider performance: nursing AI-enabled tools are reported to increase productivity by 30–50%, voice-to-text transcription is estimated to save 17% of doctors’ work time and 51% of registered nurses’ work time, and physicians in ambulatory settings already spend 49% of time on electronic health records and desk work versus 33% on direct clinical face time.
- The AI healthcare market is expected to grow from US$2.1 billion in 2018 to US$36.1 billion by 2025, at a compound annual growth rate of 50.2%, with applications including administrative tasks, clinical decision aids, imaging, drug design, surgical robots, chatbots, and medication monitoring.
- Workforce pressures are substantial: the needs-based shortage of healthcare workers was estimated at about 18 million in 2013; burnout rates reach between 25% and 75% in some clinical specialties; medical knowledge doubling time is projected at 73 days in 2020; and in the UK one in six patients have more than one chronic condition, accounting for approximately one-third of general practice consultations.
- Newer challenges include ambiguous professional liability for AI-supported decisions, labour market shifts and demand for informatics skills, need for new provider competencies, ethical concerns about biased or nonrepresentative datasets and “black box” accountability, ill-defined regulatory evidence standards, and AI’s inability to possess human characteristics such as compassion and empathy.
Methods
- Narrative literature review; the paper states it “reviews the available literature” to identify AI opportunities and implications for healthcare providers.
- No primary data collection or sample is reported; evidence comes from cited studies, reports, and examples of healthcare AI applications.
- Main analysis is a narrative synthesis of AI’s potential roles in productivity, workload, performance, teamwork, and satisfaction, followed by discussion of professional, labour market, competency, ethical, regulatory, and relational challenges.
Limitations
- The paper states limitations of AI itself: dependence on availability and quality of health data, inherent biases and nonrepresentativeness in training datasets, difficulty interpreting “black box” systems, inability to display compassion, empathy, and the human touch, and ambiguous liability and regulatory standards.
- Not stated in the paper; evident from the text: as a narrative review, it reports no systematic search strategy, databases, inclusion/exclusion criteria, or formal quality appraisal, so its conclusions may reflect selective evidence.
- Not stated in the paper; evident from the text: several quantitative claims rely on reports, industry sources, or examples that may not be peer-reviewed or generalizable across health systems.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
22 Not checked yet · 18 Own interpretation · 1 Couldn’t confirm · accuracy not yet measured
In the paper
“Globally, the needs-based shortage of healthcare workers was estimated to be about 18 million in 2013.1”
§ Artificial intelligence: opportunities and implications for the health workforce > Introduction
What the sources say
- Not checked yetcites [1] World Health Organization, 2016
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Studies on the adoption of proven innovations in healthcare confirm that there is unjustifiable slowness and incomplete implementation of evidence-based practices, even in the best academic health centres.12”
§ Artificial intelligence: opportunities and implications for the health workforce > Current healthcare challenges: the role of technology
What the sources say
- Not checked yetcites [12] Institute of Medicine, 2001
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The sheer volume of information, coupled with time constraints and cognitive limitations, has outstripped the capacity of healthcare providers to apply this new knowledge.”
§ Artificial intelligence: opportunities and implications for the health workforce > Current healthcare challenges: the role of technology
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Functioning largely as ‘black boxes’, the behaviour of AI systems is difficult to interpret and explain.”
§ Artificial intelligence: opportunities and implications for the health workforce > Newer challenges > Ethical considerations
plaindirect- Asserted: passes — The authors put it forward as true.
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- Based: unclear — It is unclear why the paper says it holds.
In the paper
“Patients and their families now have more knowledge of, competence with and engagement in their health decision-making.15”
§ Artificial intelligence: opportunities and implications for the health workforce > Current healthcare challenges: the role of technology
What the sources say
- Not checked yetcites [15] Tonsaker, 2014, Health information on the internet: gold mine or minefield?, Can Fam Physician.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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In the paper
“The difficulties in validating the outputs of AI systems have raised concerns about accountability, transparency and human control.36”
§ Artificial intelligence: opportunities and implications for the health workforce > Newer challenges > Ethical considerations
What the sources say
- Not checked yetcites [36] Kortz
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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In the paper
“For instance, only about one-fifth of the available trial results are used when diagnosing and treating cancer patients.18”
§ Artificial intelligence: opportunities and implications for the health workforce > AI to the rescue > Satisfaction
What the sources say
- Not checked yetcites [18] Jiang, 2017, Artificial intelligence in healthcare: past, present and future, Stroke Vasc Neurol. source
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“For instance, AI has been reported to improve the assessment of medical imaging to detect cases such as malignancy and diabetic retinopathy.30,31”
§ Artificial intelligence: opportunities and implications for the health workforce > AI to the rescue > Performance
What the sources say
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Considering the potential for AI to make erroneous decisions, the legal liability of AI-supported decisions often remains ambiguous.”
§ Artificial intelligence: opportunities and implications for the health workforce > Newer challenges > Professional liability
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
In the paper
“For instance, it has previously been reported that, in ambulatory settings, physicians spend 49% of their time on electronic health records and desk work versus 33% on direct clinical face time with patients and staff.27”
§ Artificial intelligence: opportunities and implications for the health workforce > AI to the rescue > Workload
What the sources say
- Not checked yetcites [27] Sinsky, 2016, Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties, Ann Intern Med. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By auto-populating structured data fields from open-ended clinician notes, querying relevant data from prior clinical records and transcribing recorded patient encounters, AI has the potential to substantially reduce the administrative burden.”
§ Artificial intelligence: opportunities and implications for the health workforce > AI to the rescue > Workload
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“Empathy skills of healthcare providers have been shown to positively influence patient outcomes.38”
§ Artificial intelligence: opportunities and implications for the health workforce > Newer challenges > Provider–patient relationship
What the sources say
- Not checked yetcites [38] Del Canale, 2012, The relationship between physician empathy and disease complications, Acad Med. source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“A related concern is that AI could make healthcare providers complacent and less likely to check results and challenge errors.34”
§ Artificial intelligence: opportunities and implications for the health workforce > Newer challenges > Professional liability
What the sources say
- Not checked yetcites [34] Wartman, 2018, Medical education must move from the information age to the age of artificial intelligence, Acad Med. source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“In nursing, for instance, it has been reported that the use of AI-enabled tools increases productivity by 30–50%.22”
§ Artificial intelligence: opportunities and implications for the health workforce > AI to the rescue > Productivity
What the sources say
- Not checked yetcites [22] McKinsey Global Institute, 2017
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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In the paper
“Prior reviews26 have demonstrated that administrative tasks contribute substantially to staff workload and time pressure.”
§ Artificial intelligence: opportunities and implications for the health workforce > AI to the rescue > Workload
What the sources say
- Not checked yetcites [26] Erickson, 2017, Medical Practice and Quality Committee of the American College of Physicians. Putting patients first by reducing administrative tasks in health care: a position paper of the American College of Physicians, Ann Intern Med. source
boosteddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Against the background of steady advances in clinical knowledge and changing patterns of health needs, the traditional model of decision-making by solo providers relying on their memory and personal experience is inadequate to effectively address twenty-first-century health challenges.”
§ Artificial intelligence: opportunities and implications for the health workforce > Current healthcare challenges: the role of technology
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The shortage and distribution challenges have led to increases in the workload and stress-related burnout of remaining staff, creating unsatisfactory work environments.
- Probably not a substantial claim. 16
- It may state two separate claims as one. 18
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“In the UK, for example, recent estimates suggest that one in six patients have more than one chronic condition, and these patients account for approximately one-third of all general practice consultations.13”
§ Artificial intelligence: opportunities and implications for the health workforce > Current healthcare challenges: the role of technology
What the sources say
- Could not verifycites [13] Fortin, 2005, Prevalence of multimorbidity among adults seen in family practice, Ann Fam Med. sourceExperimental — accuracy not yet measured
The Methods identify the study population as family-practice patients in Québec, Canada, and the Results report that nine in ten of those patients had more than one chronic condition. Our copy does not give a UK one-in-six estimate or the proportion of UK general-practice consultations attributable to these patients (searched: UK prevalence and consultation share). The Canadian estimate does not establish or contradict the UK claim. (LLM, conf=0.76)
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Could not verify
In the paper
“Among others, burnout of healthcare providers, especially clinical staff, has become an occupational hazard, with its rate reaching between 25% and 75% in some clinical specialties.7”
§ Artificial intelligence: opportunities and implications for the health workforce > Introduction
What the sources say
- Not checked yetcites [7] Laschinger, 2006, The impact of staff nurse empowerment on person-job fit and work engagement/burnout, Nurs Adm Q. source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The doubling time of medical knowledge in 1950 was estimated at 50 y, in 1980 it was 7 y and in 2010 it was 3.5 y. In 2020 it is projected to be only 73 d.11”
§ Artificial intelligence: opportunities and implications for the health workforce > Current healthcare challenges: the role of technology
What the sources say
- Not checked yetcites [11] Densen, 2011, Challenges and opportunities facing medical education, Trans Am Clin Climatol Assoc.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
In the paper
“Rapid, disruptive technological change has the potential to drive healthcare reforms to improve efficiency and augment provider satisfaction, thus improving patient experiences and outcomes.8”
§ Artificial intelligence: opportunities and implications for the health workforce > Introduction
What the sources say
- Not checked yetcites [8] Meskó, 2018, Will artificial intelligence solve the human resource crisis in healthcare?, BMC Health Serv Res. source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Recent studies3,4 have shown that the ongoing staffing shortages will not only increase patient waiting times, but also dramatically impact the overall quality of healthcare.5”
§ Artificial intelligence: opportunities and implications for the health workforce > Introduction
What the sources say
- Not checked yetcites [3] Hassmiller, 2006, Addressing the nurse shortage to improve the quality of patient care, Health Aff (Millwood) source
- Not checked yetcites [4] Dall, 2018
- Not checked yetcites [5] Rogers, 2004, The working hours of hospital staff nurses and patient safety, Health Aff (Millwood). source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As with many new technologies, the introduction of AI is likely to mean the skills and expertise required of healthcare providers will change.”
§ Artificial intelligence: opportunities and implications for the health workforce > Newer challenges > Labour market implications
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI has enormous potential to enhance productivity, improve quality and efficiency and contribute towards higher provider and patient satisfaction.”
§ Artificial intelligence: opportunities and implications for the health workforce > Conclusions
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although there is still much uncertainty about how adoption of AI will proceed, there are indications that AI has the potential to augment provider performance to deliver efficient, effective and quality care.”
§ Artificial intelligence: opportunities and implications for the health workforce > AI to the rescue
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As AI becomes increasingly used to assist with clinical activities, the professional obligations of healthcare providers towards individual patients might be affected by the use of AI decision support systems.”
§ Artificial intelligence: opportunities and implications for the health workforce > Newer challenges > Professional liability
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“A recent report estimates that voice-to-text transcription will result in work time savings of 17% for doctors and 51% for registered nurses.28”
§ Artificial intelligence: opportunities and implications for the health workforce > AI to the rescue > Workload
What the sources say
- Not checked yetcites [28] Accenture
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, as AI continues to evolve in healthcare, there will likely be a growth in the demand for new skill sets, such as informatics.”
§ Artificial intelligence: opportunities and implications for the health workforce > Newer challenges > Labour market implications
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“High workload is a recognized occupational stressor that has implications for the quality of care and patient outcomes.24,25”
§ Artificial intelligence: opportunities and implications for the health workforce > AI to the rescue > Workload
What the sources say
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI has the potential to integrate data from a large number of sources, both structured and unstructured, to provide more cohesive, faster, more consistent access to patient information across different settings and disciplines.”
§ Artificial intelligence: opportunities and implications for the health workforce > AI to the rescue > Teamwork
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Although multimorbidity is common and costly, clinical practice guidelines and models of care delivery are still largely built on vertical monomorbid approaches.14”
§ Artificial intelligence: opportunities and implications for the health workforce > Current healthcare challenges: the role of technology
What the sources say
- Not checked yetcites [14] Fortin, 2005, Multimorbidity is common to family practice: is it commonly researched?, Can Fam Physician.
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
In the paper
“Technologies such as AI have the potential to analyse and identify patterns in complex data and support healthcare providers in the delivery of care.19”
§ Artificial intelligence: opportunities and implications for the health workforce > Current healthcare challenges: the role of technology
What the sources say
- Not checked yetcites [19] Dilsizian, 2014, Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment, Curr Cardiol Rep. source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI can enable patients with chronic conditions to become better informed about their health and stay connected with health caregivers.”
§ Artificial intelligence: opportunities and implications for the health workforce > AI to the rescue > Satisfaction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Concerns have been raised that datasets used to train AI systems have inherent biases and are often not representative of the wider population.34”
§ Artificial intelligence: opportunities and implications for the health workforce > Newer challenges > Ethical considerations
What the sources say
- Not checked yetcites [34] Wartman, 2018, Medical education must move from the information age to the age of artificial intelligence, Acad Med. source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI technologies have the potential to process data, discover new knowledge and create novel methods to improve the quality of healthcare.”
§ Artificial intelligence: opportunities and implications for the health workforce > AI to the rescue > Satisfaction
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI has vast potential to deliver better quality and more efficient healthcare, thereby enhancing productivity, provider satisfaction and user experience and dramatically improving outcomes.”
§ Artificial intelligence: opportunities and implications for the health workforce > Newer challenges
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI tools have the potential to improve diagnostic decisions and treatment outcomes and reduce medical errors.”
§ Artificial intelligence: opportunities and implications for the health workforce > AI to the rescue > Performance
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI can assist in improving efficiency and quality, but is limited by its inability to possess some human characteristics, such as compassion, empathy and the human touch.”
§ Artificial intelligence: opportunities and implications for the health workforce > Newer challenges > Provider–patient relationship
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“Looking into the future—taking cues from the history of automation—AI is unlikely to displace humans, but will definitely redefine their roles and establish itself as an indispensable cognitive assistant.39”
§ Artificial intelligence: opportunities and implications for the health workforce > Conclusions
What the sources say
- Not checked yetcites [39]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI is likely to transform the role of healthcare providers, and may even dramatically change the provider–patient relationship.”
§ Artificial intelligence: opportunities and implications for the health workforce > AI to the rescue
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For now, the role of healthcare providers is unchanged, but AI can be a very useful cognitive assistant.”
§ Artificial intelligence: opportunities and implications for the health workforce > Newer challenges > Provider–patient relationship
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. It may state two separate claims as one.
In the paper
“According to a recent report,20 AI in the healthcare market is expected to grow from US$2.1 billion in 2018 to US$36.1 billion by 2025, at a compound annual growth rate of 50.2% during the forecast period.”
§ Artificial intelligence: opportunities and implications for the health workforce > The rise of AI
What the sources say
- Not checked yetcites [20] Research and Markets
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
The shortage and distribution challenges have led to increases in the workload and stress-related burnout of remaining staff, creating unsatisfactory work environments.
“The shortage and distribution challenges have led to increases in the workload and stress-related burnout of remaining staff, creating unsatisfactory work environments.”
The paper relies on this without support.
The growth of consumerism and the proliferation of internet-accessible sources of health-related information will continue to modify the provider–patient relationship.
“In addition, the growth of consumerism and the proliferation of internet-accessible sources of health-related information will continue to modify the provider–patient relationship.”
The paper relies on this without support.
Future medical practice will be characterized by the use of a growing array of data from multiple sources and AI applications.
“Future medical practice will be characterized by the use of a growing array of data from multiple sources and AI applications.”
The paper relies on this without support.
As the use of AI in the clinical space increases and evolves, legal and regulatory risk will increase, particularly since traditional regulatory principles are not yet attuned to AI.
“As the use of AI in the clinical space increases and evolves, legal and regulatory risk will increase, particularly since traditional regulatory principles are not yet attuned to AI.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Research has linked AI’s effectiveness in resource optimization to data quality, AI performance, and workforce adaptation [174–176].”
From Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review (2025) · cites this paper as [175]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“improving patient adherence in the care process [15, 70, 79, 82, 89, 97, 112, 113, 129, 135, 142, 145, 146, 156, 158]”
From Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review (2025) · cites this paper as [79]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“supporting healthcare providers in making more informed decisions”
From Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review (2025) · cites this paper as [79]
In the citing paper
“For instance, the integration of AI in healthcare improves the quality and efficiency of services, making it a useful and effective assistant in related activities (Hazarika, 2020).”
From Impacts of generative artificial intelligence on the future of labor market: a systematic review (2025) · cites this paper as Hazarika, 2020
Citations
Cites
Crossing the Quality Chasm: A New Health System for the 21st Century
Baker, A. (2001) · BMJ · doi:10.1136/bmj.323.7322.1192 · Cited by 10,779
Crossing the Quality Chasm: A New Health System for the 21st Century
Committee on Quality of Health Care in America (2002) · Journal for Healthcare Quality · doi:10.1111/j.1945-1474.2002.tb00463.x · Cited by 8,508
Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837
A model for types and levels of human interaction with automation
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/3468.844354 · Cited by 4,195
Artificial intelligence in radiology
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803
How the machine “thinks”: Understanding opacity in machine learning algorithms
Burrell, J. (2016) · Big Data & Society · doi:10.1177/2053951715622512 · Cited by 3,196
Leavitt, M. (2001) · PubMed · Cited by 1,835
Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties
Sinsky, C., Colligan, L., Li, L., Prgomet, M., Reynolds, S., Goeders, L., Westbrook, J., Tutty, M., & Blike, G. (2016) · Annals of Internal Medicine · doi:10.7326/m16-0961 · Cited by 1,494
Improving Diagnosis in Health Care
Committee on Diagnostic Error in Health Care, Board on Health Care Services, Institute of Medicine, & The National Academies of Sciences, E. (2015) · doi:10.17226/21794 · Cited by 1,376
Prevalence of multimorbidity among adults seen in family practice
Fortin, M. (2005) · The Annals of Family Medicine · doi:10.1370/afm.272 · Cited by 987
Research works printed but not linked: 4
- World Health Organization, 2016, Global strategy on human resources for health: workforce 2030
- Dall, 2018, 2018 update. The complexities of physician supply and demand: projections from 2016 to 2030
- World Health Organization, 2016, Report of the High-Level Commission on Health Employment and Economic Growth
- Bresnick, Amazon takes on unstructured EHR data with machine learning, NLP.
Cited by
Sorted by citations
Shaping the future of healthcare: ethical clinical challenges and pathways to trustworthy AI
Goktas, P., & Grzybowski, A. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14051605 · Cited by 336
Artificial intelligence technologies and compassion in healthcare: A systematic scoping review
Morrow, E. M., Zidaru, T., Ross, F., Mason, C. L., Patel, K. D., Ream, M., & Stockley, R. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.971044 · Cited by 308
Wubineh, B. Z., Deriba, F. G., & Woldeyohannis, M. M. (2024) · Urologic Oncology: Seminars and Original Investigations · doi:10.1016/j.urolonc.2023.11.019 · Cited by 141
Alami, H., Lehoux, P., Denis, J., Motulsky, A., Petitgand, C., Savoldelli, M., Rouquet, R., Gagnon, M., Roy, D. A., & Fortin, J. (2020) · Journal of Health Organization and Management · doi:10.1108/jhom-03-2020-0074 · Cited by 156
Arab, R. A. E., Moosa, O. A. A., Sagbakken, M., Ghannam, A., Abuadas, F. H. A., Somerville, J. G., & Mutair, A. S. A. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1619378 · Cited by 122
Metadata
- Authors
- Hazarika, I.
- Affiliations
- World Health Organization (CH)
- Corresponding author
- Hazarika, Indrajit
- ORCID
- Hazarika, Indrajit
- Venue
- International Health
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-04-17
- Volume
- 12
- Issue
- 4
- Pages
- 241-245
- ISSN
- 1876-3405 (ISSN-L)
- 1876-3413
- PMC
- PMC7322190
- OpenAlex
- W3016592756
- Semantic Scholar
- a74a03549d373238819df29729a833b098eaf9fa
- Cited by
- 203
- References
- 31
- Citations by year
- 2026: 46 · 2025: 52 · 2024: 54 · 2023: 31 · 2022: 15 · 2021: 3 · 2020: 2
- FWCI
- 0.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Health (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- World Health Organization
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence in healthcare, healthcare workers, occupational stress, clinical decision support, AI adoption barriers, transparency and accountability, AI policy
- MeSH terms
- Artificial Intelligence, Delivery of Health Care, Health Workforce, Humans, Medical Informatics, Primary Health Care
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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