The futureof AI inhealthcare
AI is helping prioritize the patient
experience, improve outcomes, andstreamline clinical workflows withsmarter healthcare solutions.

Table of Contents
04

Defining AI for healthcare

07

Applying AI in healthcare today and tomorrow		Diagnosis and treatment: Imaging and clinical decision support		Workflows: Clinical and administrative		Precision medicine: Individual and population

22

Better human health enabled by AI

02

AI transformation
The Brookings Institution’s Artificial Intelligence andEmerging Technology (AIET) initiative identifies fourareas where AI is expected to benefit healthcare1:• Pushing the boundaries of human performance• Democratizing medical knowledge and excellence• Automating drudgery in medical practice• Managing patients and medical resources
According to market analysts at Deloitte, tasks
performed by AI “…can range from simple to complex
and include everything from answering the phone to
medical record review, population health trends andanalytics, therapeutic drug and medical device design,reading radiology images, making clinical diagnoses
and treatment plans, and even talking with patients.”2

Reductions in healthcare spending
The National Bureau of Economic Research says
broader adoption of AI could save 5%–10% a
year in healthcare spending (up to $360 billion)
without sacrificing quality or access.3
While the goal is to deliver better-quality
healthcare less expensively, incorporating AI
capabilities into a healthcare organization requiresconsiderable upfront investment and planning.
Research suggests a variety of powerfulapplications that may revolutionize theprediction, prevention, and treatment of disease.

At Lenovo, we believe AI encompasses both the art of possible
and the art of practical, from enabling personalized medicine andtransforming research to practical efficiencies that can be usedthroughout the patient care and administrative environment.

“We need to make sure that we don’t
implement systems that are going tohave inherent bias. This is one of thereasons why, at least in my mind, we willneed a physician at the end of that chain
to make sure that whatever the AI robot
comes up with actually makes sense in
the context of this given patient and thisgiven set of circumstances.”
Dr. Robert Groves
Executive Vice President and Chief Medical Officer
Banner | Aetna

03

“AI is the ability formachines to mimic thecognitive functions
and decision makingof humans with limited
human intervention.”
Robert Daigle
Director, Global AI Business, Lenovo

Defining AI
in healthcare
Artificial intelligence (AI) has a growing range of applicationsin healthcare, including the use of computer-driven analysis
for clinical decision support, streamlining both clinical andadministrative workflows, and moving precision medicine forward.

04

Machine learning (ML)

Deep learning (DL)Federated learning (FL)

Machine learning gives systems theability to automatically learn andimprove from experience without
being explicitly programmed. ML
uses algorithms to analyze data,detect patterns, form conclusions,and make predictions. Most
traditional ML algorithms needstructured (labeled) data.

Deep learning is a subset of machinelearning. A deep learning model
analyzes data not unlike the way
a person would. It uses a layered
structure of algorithms called an
artificial neural network that imitates
the way the human brain filters
information and learns to classify andpredict. In this model, an algorithm
can determine on its own if aprediction is accurate or not. WhileML requires the features used forclassification to be provided (labeleddata), DL can automatically figureout the features. It allows machines
to solve complex problems even withdiverse, unstructured data. DL requires
huge amounts of data and therefore
powerful computing capability.

Federated learning allows AI
algorithms to use and learn from data
located at different sites. This meansorganizations can collaborate on
the development of models without
needing to directly share sensitive
clinical data with each other.

Natural language
processing (NLP)

Anatomyof AI
The broad term “artificial intelligence”
includes subsets of related technologies,
which often work together to enable
healthcare capabilities and breakthroughs.

Natural language processing is
a subset of machine learning
used to convert human language
into computable data formats
that can be compared and analyzed.

Computer vision (CV)Computer vision is the use of
AI to compare and analyze
images — training computers to
see and interpret them the same
way human vision does. Computer
vision models take information fromthousands of images and outcomes,
then apply deep learning to analyze
and reach conclusions.

Imaging analytics
Accelerate your workflows with the Lenovo
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Medical image analysis applies deeplearning and computer vision toprovide clinical decision support toclinician specialists including oncology,
cardiology, and pathology.

05

Applications of
AI in healthcare*
Diagnosis
and treatment

Predictive modelingrisk stratification

Matching engines• Clinical decision support• Readmission rate

• Symptoms analyzer• Hospital-acquired infections• Treatments with similar
cost-benefit ratios• Treatment efficacy vs.
effectiveness• Emergence of complications• Pathology

Computer vision• Radiological image analysis

Virtual assistants
Discovery• Voice assistant/medical scribes• Clinical trials• Electronic healthrecord (EHR) integration• Hypothesis generation• Proofs of concept• ECG and EEG analysis

Workflows

Precision medicine• Genomics• Patient flow optimization• Detect process inefficiencies• Electronic healthrecord (EHR) integration• Prior authorization• Patients with similar profiles

Patient engagement• Augmented reality (AR)• Virtual reality (VR)• Generative AI reference materials

Mobile apps
• Wellness• Chronic disease management• Billing and coding*Adapted from Cognity AE.

06

Applying AI in healthcare
today and tomorrow
Diagnosis and treatment | Workflows | Precision medicine

07

Diagnosis and treatment:

Imaging and clinicaldecision support
AI uses computer-driven analysis to support
clinician decision-making in diagnosis andtreatment and to provide deeper insights tohealthcare researchers, providers, and patients.

08

Diagnosis and treatment:

Diagnosis and treatment:

Diagnosis and treatment:

Reducing invasive
proceduresRadiology as
early adopters of AI

Pathology’s in-housesecond opinion

Deep learning is increasingly being applied inoncology and holds promise for better accuracy
in detecting an image’s clinically relevant
features than computer-aided detection (CAD).

An early application of AI in healthcare
is medical image analysis in radiology,
where understaffing, increasing demand,and rising costs are significant concerns.

Another advantage of AI in imaging is thepotential to reduce the need for tissue samples
and their risk of infection in favor of far lessinvasive MRIs, CT scans, and X-rays.

Globally, 30% of radiologists say they
are already using AI, according to a survey
conducted by the European Society ofRadiology.4 Although mostly used to aid
in screenings, AI is also being used tocreate synthetic images for training
purposes and to help create the reportingneeded for routine imaging processes.

Pathologists are using AI to improve accuracy
and increase efficiency with pattern detection
capabilities that serve as a digital “thought
assistant.”5 In essence, the technology has
given pathologists a second opinion to refer to,
but without the wait.

In addition to diagnostics, AI in imaging may
also aid treatment by speeding the discovery of
new drugs and vaccines. Machine vision image
analytics is an emerging technology that canidentify which molecules might be effective forwhich biological targets.

AI has the potential to reduce costs and savetime in breast cancer screenings. Millions of
women have routine screening mammograms
every year. The majority of them show normal,
healthy breast tissue. Yet reading the images
needed to confirm breast health represents a
substantial burden on radiologists.

As stated in the journal Diagnostics, “Using
AI approaches that computationally evaluate
the entire slide image, researchers can detectfeatures that are difficult to detect by eye
alone, which is now the state of the art indigital pathology.”6
According to a survey published by The Lancet,“Experts agreed that AI would be routinely andimpactfully used within AP laboratory andpathologist clinical workflows by 2030.”7

AI transforms the reading workflow —making worklists intelligent andhighlighting and prioritizing studies thatneed deeper review and evaluation.

The Lenovo ThinkPad® P1 and ThinkPad® P16 mobile
workstations, powered by Intel vPro® with up toIntel® Core™ i9 processors, blaze through heavy
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09

Diagnosis and treatment:

Diagnosis and treatment:

Detecting hypoglycemia
in diabetes patients

Earlier mental healthpattern detection

Tracking blood sugar levels isessential in diagnosing diabetes
and even more vital for patients
undergoing treatment. A sudden
drop in blood sugar resulting in
hypoglycemia can have serious
consequences, including death.

AI has proven particularly adept at
detecting the presence of, or precursors
to, certain mental health issues. In fact,a study published by Nature stated,“AI technologies offer great promise
in diagnosing mental health disorders.
Healthcare professionals in the fieldshould cautiously and consciously begin
to explore the opportunities of AI-based
tools for their daily routine.”11

A study published in the Journal of
Diabetes Science and Technology
on using machine learning (ML) for
real-time hyperglycemia detection
concluded, “Machine learning enables
more precise and timely predictions
in comparison with baseline models.”8

A review of studies of ML and AI for
predicting blood glucose levels fromwearable devices (WD) was positive
as well, stating, “WDs in general havethe potential to remove the needfor invasive devices completely for
glucose monitoring in the not-toodistant future.”9
It doesn’t stop at detection, though.
According to a study in Diabetes
Care, AI can be used to help guidethe management of glucose levels.
The researchers concluded, “Among
adults with type 2 diabetes, use of an
integrated digital healthcare platform
with AI-driven dietary management
resulted in better glycemia and moreweight loss.”10

Mental illness often goes undetected
and accounts for 7% of all total
disability-adjusted life years. Having
AI as a detection or prevention tool
could improve the quality andquantity of life for countless people.11
Detection isn’t limited to pre-existing
mental illness, either — as AI can use data
to predict where impacts on mental healthmay occur in the future. In fact, Stanford
University used AI to help predict when
dependency or addiction was likely tooccur from certain opioid prescriptions.12

10

Diagnosis and treatment:

Early and effective
prostate cancer detection
Prostate cancer affects one in eightmen.13 The quality of life for millionsof men could be improved withearlier and more effective means of
prostate cancer detection, as could theprevention of unnecessary biopsies.
AI has already been shown to be effective
in helping pathologists detect thepresence and extent of prostate cancer.
In fact, research shows pathologists
who used AI to detect prostate cancer
performed better than those who didn’t,
reducing detection errors by 70%.14

Before that study, the FDA had alreadyreported that AI improved detectionof prostate cancer on individual slidesof biopsies by an average of 7.3%.15
AI can also help determine the extent
of prostate cancer that is present.
Researchers at University of California,
Los Angeles (UCLA) Jonsson
Comprehensive Cancer Center and theDepartment of Urology at UCLA havedeveloped an AI model for just thispurpose and tested it with patients at
Stanford University School of Medicine.
The team’s model was found to be
more accurate at predicting tumor
margins than conventional methods.16
Researchers in Canada have reported
that, after patients’ first oncology
consult and with an AI-based reviewof the patient’s full medical history,
their algorithm could predict thesurvival rates of patients with a varietyof cancers with 80%–90% accuracy.17

Diagnosis and treatment:

Heart attack and stroke prevention
Researchers at the Iowa Institute for
Biomedical Imaging of the Universityof Iowa are studying the use of AI and
optical coherence tomography (OCT)
imaging to detect changes in hearttissue. They are using AI to help predictthe likelihood of a patient having aheart attack.
The project uses AI to classify coronary
tissues and predict coronary plaquevulnerability over the year following
an initial physician visit. Successful
AI-based prediction of coronary
plaque vulnerability allows physicians
to take preventive action at the first
visit, making future heart attacks lesslikely to occur.18

Florida’s Memorial Healthcare System
Comprehensive Stroke Centers is using
AI right now to help detect strokes
and take immediate action. AI detects
stroke identifiers in the neuroimaging
performed on all stroke patients. A
stroke intervention team is notified
immediately, and expedited treatment
is provided.
Stroke neurologist and neurointerventional surgeon Dr. Haris
Kamal of Memorial Healthcare System
Comprehensive Stroke Centers told
CBS News, “AI allows us to shave off
all the time that it would have taken
for a radiologist to read the picture
and then call the ER doctor. This direct
pathway provides patients access toneuro-endovascular specialists directly,
without even anyone having to make aphone call.”19
11

Diagnosis and treatment:

Elder care for an aging planet
By 2050, there will be 2.1 billion
people over the age of 60.20 AI
will be needed to supplement thehealthcare of so many by so few.
AI systems via wearables andother devices are already inplace to detect falls and changes
in indicators of heart health or
cognitive issues. A new systemdeveloped at the University ofWaterloo goes a step further, using
very low-power, millimeter-wave
radio systems with machine learningand artificial intelligence to alert
healthcare workers to sudden falls
without the need to install cameras.21

Researchers at Massachusetts
General Hospital are using an AI
model based on deep learningof MRI imaging to help detect
Alzheimer’s disease. Analyzing thedata from over 10,000 patients
across all risk levels, the researchers
were able to detect Alzheimer’s
disease with a 90.2% accuracy rate.22
As many as 40%–60% of adults
with probable dementia are notbeing diagnosed. The researchersconcluded that AI could help
diagnose patients earlier, providing
more time for treatment.22

12

Workflows:

Clinical andadministrative
AI has undeniable potential to transform many aspectsof healthcare. One big category is the streamlining of
workflows by building efficiency into each role and eachtask, whether clinical or administrative. The ultimate goal isdelivering better quality of experience for patients, families,
and providers — from first contact through every encounter,
whether in the clinic, the hospital, online, or at home.

The Lenovo ThinkPad P16 is powered by Intel vPro® withIntel® Core™ i9 processors for a world-class healthcare solution.

13

Workflows:

Clinical workflows
On the clinical side, AI can significantly impact
efficiency by reducing the time to develop treatment
plans. A good example is in oncology. These plans
are complex and time-consuming, with consideration
for available treatment modalities and targetingcancer cells while preserving healthy tissue. With
thousands of ongoing clinical trials, there is moredata than one oncologist could possibly synthesize.
Yet this information could be critical to the patient.
ML and NLP can interpret volumes of study results
and compare the data to a specific case — informing
not only the treatment, but the best way to deliverit (another painstaking process that takes significant
time). Microsoft Research reports that specialists
using AI can plan radiotherapy treatments about
2.5 times faster with AI than without.23

14

Workflows:

Administrative workflows
The administrative side of healthcare
is one of the most recognized areasof AI value. According to McKinsey,
administrative overhead currently
accounts for 25% of total US healthcare
spending.24
Many of these tasks are ripe for
automation, including prior authorization,
following up on unpaid bills, andmaintaining records. In fact, McKinsey
estimates that AI-enabled priorauthorization can automate 50%–75% ofmanual tasks.24
NLP can be used to parse data fromelectronic health records (EHRs) and toconvert unstructured data into

computable form. This is valuable
in consolidating handwritten notes,
voice dictation, and computer-enteredinformation in a patient’s EHR.
Process automation can be applied
to a wide range of administrative
activities including patient scheduling,claims processing, and medical records
management. AI could also help processroutine inbox requests like medication
refills and test result notifications.
All these AI use cases let front office
staff spend more time addressing patientconcerns and less time checking people
in. Physicians and nurses can spend moretime on patient interaction and less on
paperwork, electronic or otherwise.
Data analytics has also become a key
component of organizational operations
and efforts to increase efficiency andprofitability — from reception to backend offices, even to supplies inventory.

Workflows:

Streamlining clinical documentation
The average nurse in the UnitedStates spends 15% of work time on
documentation activities alone.25 This
includes dealing with more than 10,000
Current Procedural Terminology (CPT®)
codes, as well as more than 71,000
procedure and 69,000 diagnosis codesin the International Classification of
Diseases, Tenth Revision (ICD-10).26,27
McKinsey estimates that 10%–20% of
a nurse’s shift is spent on activities
that could be optimized with the righttechnology.25

For doctors, the news is even better.
New AI tools, including solutions fromMicrosoft, are able remove hours of typing
from a doctor’s day — summarizing,organizing, tagging, and transcribing
interactions. Transcription summaries caneven be leveled to the understanding of
the patient.
For patients, it’s empowering. For
doctors, it’s freeing. According to family
physician Dr. Michelle Thompson, wheninterviewed in The New York Times about
AI documentation, “AI has allowed me,
as a physician, to be 100% present for
my patients.”2815

Workflows:

Patient engagement
and convenience
Industries are increasingly adopting self-service
models that allow customers to complete tasks
when and where they want, on their own devices.
AI can help healthcare do the same. Self-service
benefits could help reduce costs, patient waitingtimes, and administrative errors while increasing
patient satisfaction. Some provider organizations
are also offering interactive online portals with
NLP-driven chatbots to help with medication
refills and other simple administrative tasks for a
personalized, always-available experience.

16

Workflows:

Easing workloads
Radiology workloads are overwhelming, with 36%
of radiologists reporting they feel burned out.29 AI
helps ease radiology workloads by conducting initial
image screening for indicators of disease. It can alsooffer radiologists additional insights, confirming adiagnosis or flagging a possible human error. Basedon these deeper data-based insights, radiologists
can more confidently make a final diagnosis.

17

Precision medicine:

Individual
and population
One of the most exciting and fastest-growing areas in healthcare is
precision medicine. Precision medicine personalizes the diagnosis,
treatment, and prognosis of an individual patient — informed byanalysis of data not only from that patient but also from population
health data, now and over time. This level of insight is what many
believe will allow scientists and clinicians to predict and begin toprevent today’s chronic diseases and much more.

18

Precision medicine:

Making it personal
In precision medicine, insights from AI tools can improve accuracy in diagnosis andefficacy of treatment by tailoring solutions to the patient’s unique manifestation of theirillness and receptivity to therapies. Genetics and life experience, including the social
determinants of health (SDOH) like birthplace, housing, diet, workplace, and income,factor into these personalized treatments.30• ML provides physicians with morediagnostic information from sources
like wearable devices than has everbeen available.

• The data from ML and NLP canidentify disease trends andpredict which treatments aremost successful for individuals.

• ML can compare data collected on an
individual patient to data from a groupof patients with the help of NLP, which
standardizes patient records into data
sets from large numbers of patients.

• This data can also forecast thelikelihood of a specific patient
contracting a disease.

Lenovo AI Innovative Solutions
enable you to find tailored solutions
including computer vision, audio
recognition, prediction, security,and virtual assistants. The Lenovo
AI Discover Center of Excellence
provides access to AI experts,
workshops, and best practices.

• “Digital twins,” or representative
data models, can be used for eachpatient’s individual body, physiology,
and medical history, enabling
more precise treatment testing.

19

Precision medicine:

Accelerating
population-level
genomics
Achieving population-scale precision medicine requires
unprecedented levels of high-performance computing.
In fact, two out of three analysis stages in genomics-based
biomedical analyses require supercomputer power.31
Scaling up genomics production largely depends on
scaling high-performance computing technologies.
Performing the scope of analytics needed to gain timely,
meaningful insight across populations means speed aswell as processing capacity. Technology companies areworking to develop hardware and software that will
optimize existing systems so they can perform ordersof magnitude more analytics in shorter timeframes.

Take genomics to the next levelwith the Lenovo ThinkStation®
PX, powered by up to Intel® Xeon®
Platinum 8490H processors andWindows 11 Pro for Workstations.

20Precision medicine:

Lenovo accelerates
genomic analytics
Lenovo has extensively tested the performance of the
Broad Institute’s Genome Analysis Tool Kit (GATK)
Germline Variant Calling Workflow — tools used byresearchers to perform genomics analytics. The projectexamined variants in hardware, system tunings, datatypes, execution modes, and software implementations.
The goal was to find the optimum configuration toaccelerate the speeds at which genomes are assembled
and analyzed. As a result, Lenovo developed its
Genomics Optimization and Scalability Tool (GOAST).

For data centers not using optimized hardware
and software, processing a single genome takes
150 to 160 hours. Work by Intel® in 2017 reduced
that processing time to 10.8 hours.
Now, with Lenovo GOAST solutions, researchers
can analyze an entire human genome in 47 minutes
and whole exomes in a matter of seconds. In astandard cloud or on-premises environment, thesame analysis takes 40–150 hours.

With Lenovo ThinkEdge solutions, your organization
can run AI applications in the cloud or on-premises,
depending on your needs, with purpose-built server
compute power closer to the source of the data.

The Lenovo GOAST, based on a Lenovo ThinkSystem® SR630 server powered by
two Intel® Xeon® Platinum 8480+ processors — and the GOAST Plus, based on a
ThinkSystem® SR950 powered by eight Intel® Xeon® Platinum 8280L processors —are validated, preconfigured, and built for high-performance reliability.

“Improving predictive modeling —I think that can definitely save a
lot of time and resources. You’re
wasting less with faster insights,
less money, and more accurate
results. I think that’s very exciting
and has a lot of promise.”
Dana Alegre
Solutions Architect–HPC Life Sciences
Lenovo21

Better human healthenabled by AI
Developing secure and responsible
AI solutions to improve healthcaredelivery around the world is the work ofmany organizations and thousands ofindividuals. As research and development
continue, new possibilities and potential
healthcare applications will emerge,
expanding the horizon still further.

• For clinicians, that means easing
their workload — streamlining patient
screening and expanding the diagnosis
and treatment of disease.

Even as scientists work to fulfill thepromise of AI for moving healthcare fromtreating disease to preventing it, early
applications are making their way into
clinical settings.

• For patients, it means a moreconvenient healthcare system —physicians able to make more informed
diagnoses and treatments tailored tothe individual.

• For health systems, it means greater
efficiency, enhanced security forpatient data, and improved patient
outcomes.

AI research and application add up
to better human health — the goal of
everyone involved in the quest.
The Lenovo AI Discover Center of
Excellence can help with the technicalknowledge to discover optimized
solutions and valuable insights fromyour data quickly, responsibly, andethically. Lenovo makes adopting
AI faster with access to AI experts,
workshops, and best practices.

“Technology should be a tool toserve the primary relationshipbetween caregiver and patient.”
Dr. Robert Groves
Executive Vice President and Chief Medical Officer
Banner | Aetna• For researchers and pharmaceuticalcompanies, it means greatly
accelerating drug screening, drug
discovery, and vaccine delivery.

22

Generative AI
in healthcare
Generative AI offers unprecedented
promise for time and cost savings
because it touches every area of
healthcare, offering relief from mundane,
repetitive tasks while providing improved
experiences.

Generative AI has made it possible toautomate the delivery of information topatients, in their native language, at theirlearning level, and offer summaries oflab results, notes, and clinical orders forclinicians in real time.

McKinsey states that generative AI
can help healthcare by “…automating
tedious and error-prone operational
work, bringing years of clinical data
to a clinician’s fingertips in seconds,
and by modernizing health systems
infrastructure.”32

According to Accenture, generative AI
could potentially automate or augment
37% of healthcare practitioner andtechnical tasks, as well as 35% of
healthcare support tasks.33 Imagine
how much time that will free up for
direct patient care. And imagine theimprovement in employee experience forhealthcare workers everywhere.

23

Contact your Lenovo Health accountrepresentative or local business partner.
Visit www.lenovo.com/Health

Sources1

The Brookings Institution’s Artificial Intelligence and Emerging Technology (AIET) Initiative, November 20192

Deloitte Development LLC, “The future of artificial intelligence in health care: How AI will impact patients, clinicians, and the pharmaceutical industry,” 20193

National Bureau of Economic Research, “The Potential Impact of Artificial Intelligence on Healthcare Spending,” January 2023

4 European Society of Radiology (ESR), “Current practical experience with artificial intelligence in clinical radiology: A survey of the European Society of Radiology,” June 2022
5

The Pathologist, “A Newer Frontier,” April 2022

6

Diagnostics (Basel), “Application of Artificial Intelligence in Pathology: Trends and Challenges,” June 2022

7

eBioMedicine, Part of The Lancet Discovery Science, “Computational pathology in 2030: A Delphi study forecasting the role of AI in pathology within the next decade,” January 2023

8

Journal of Diabetes Science and Technology, “Explainable Machine Learning for Real-Time Hypoglycemia andHyperglycemia Prediction and Personalized Control Recommendations,” June 2022

9

Journal of Medical Internet Research, “The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review,” January 2023

10 Diabetes Care, “An Integrated Digital Health Care Platform for Diabetes Management With AI-Based Dietary Management,” February 2023
11 Nature, “The performance of artificial intelligence-driven technologies in diagnosing mental disorders: An umbrella review,” July 2022
12 Healthcare IT News, “AI helps Stanford researchers show link between first opioid Rx and dependency,” November 2022
13 American Cancer Society, “Key Statistics for Prostate Cancer,” 2023
14 Archives of Pathology and Laboratory Medicine, “Clinical Validation of Artificial Intelligence — Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in
Prostate Cancer Detection,” December 2022
15 US Food and Drug Administration, “DA Authorizes Software that Can Help Identify Prostate Cancer,” September 2021
16 UCLA Health, “AI model could help improve outcomes of prostate cancer focal therapy,” June 2023
17 JAMA Network Open, “Predicting the Survival of Patients with Cancer from Their Initial Oncology Consultation Document Using Natural Language Processing,” February 2023
18 Handbook of Medical Image Computing and Computer Assisted Intervention, “Segmentation using optimal graph search and just-enough interaction,” 2020
19 CBS News Miami, “Artificial Intelligence Is Leading to Faster Stroke Diagnosis, Treatment and Better Outcomes,” April 2023
20 World Health Organization, ”Ageing and health,” October 2022
21 Science Daily, “New in-home AI tool monitors the health of elderly residents,” March 2023
22 Heath IT Analytics, “AI Model Enables Alzheimer’s Disease Detection from Brain MRIs,” March 2023
23 Microsoft, “How AI is helping to shrink waiting times for NHS cancer patients,” June 2023
24 McKinsey & Company, “AI ushers in next-gen prior authorization in healthcare,” April 2022
25 McKinsey & Company, “Reimagining the nursing workload: Finding time to close the workforce gap,” May 2023
26 American Medical Association, “CPT®: The shared language of health care,” November 2022
27 Centers for Disease Control and Prevention, “International Classification of Diseases (ICD-10-CM/PCS) Transition — Background,” August 2023
28 The New York Times, “A.I. May Someday Work Medical Miracles. For Now, It Helps Do Paperwork.” June 2023
29 Medscape, “Radiologist Lifestyle, Happiness and Burnout Report,” February 2023
30 World Health Organization, “Social Determinants of Health,” 2023
31 HPC Wire, “Accelerating High Performance Computing (HPC) for Population-Level Genomics,” September 201932 McKinsey & Company, “Tackling healthcare’s biggest burdens with generative AI,” July 2023
33 Accenture, “A new era of generative AI for everyone,” 2023

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