陆军医学与人工智能改变未来战场Army Medicine and Artificial Intelligence Transforming the Future Battlefield

**Army Medicine and Artificial Intelligence**

**Transforming the Future Battlefield**

Col. Vanessa Worsham, U.S. Army Nurse Corps

Lt. Col. Elvis Gonzalez, U.S. Army Medical Service Corps Lt. Col. Margaret Kucia, U.S. Army Specialist Corps

Lt. Col. Megan Matters, U.S. Army Nurse Corps

Maj. Thomas Hansen, U.S. Army Medical Service Corps Maj. David Preczewski, U.S. Army Medical Service Corps

Maj. Martin Smallidge, U.S. Army Dental Corps Dr. Edward Michaud, U.S. Army Civilian Corps

rtificial intelligence (AI),machine learning

**A**

(ML), and robotics mutually enhance human

performance and military readiness through the development of intelligent software and machines.1 The ongoing evolution of AI and related technologies continue to influence popular culture, science, robotics, finance, marketing, supply chain, and healthcare.2

AI mimics human intellect and processing func-

tions such as language, learning, and problem-solv-

ing, through repetitive pattern recognition.3 ML and neural network processes ingest large datasets, which “train” algorithms and develop adaptive “intelligence.”4 Quantitative studies and applied research demonstrate the efficacy of AI, ML, and human-machine integra- tion, and in military settings, human-machine inte-

gration increases lethality through range, endurance, payload, survivability, and adaptability, significantly changing employment strategies.5

In 2022,AI revolutionized modern culture and social dynamics through the release of ChatGPT by OpenAI.

The generative AI platform established this once distant, science-fiction concept within the public domain, with

widespread availability for anyone with a network con-

nection. In the wake of such rapid developments, future

applications provide unlimited potential, and in particular, this technology is assessed to support human cognition

through enhanced military leaders’decision-making,

improved situational awareness, and optimized resource replenishment to fight and win on current and future

battlefields. AI provides military decision-makers with valuable insights and analysis based on algorithms that process vast amounts of high-velocity, high-volume data, identifying trends and patterns that outpace human cog- nition, especially on a mass scale and at echelon.

Of significant impact,AI could provide solutions for the extreme challenges of providing medical care

![](/media/202412//1733384081.7393177.png)

A patient is loaded into a UH-60A Black Hawk medevac aircraft. AI will play an integral role in increasing the accuracy and efficiency of patient movement by reducing the number of pilots and crew required for medevac missions as well as by performing administrative func- tions and monitoring patients en route. (Photo by Maj. David Preczewski, U.S. Army)

during large-scale combat operations (LSCO). Army Futures Command has described the challenges facing the Army Health System (AHS) in the future oper-

ational environment, including high casualty rates,

delayed evacuation and replenishment, and the lethal- ity of multidomain and novel complex terrain envi-

ronments.6 To mitigate the challenges, the AHS will support forces contested in all operational domains. Our enemies’ antiaccess and aerial denial capabilities will limit U.S. air, land, and sea force projection. In the cyber domain, AHS assets are threatened by enemy access to force structure and patient data, weapon-

ized misinformation, and cyberattacks that interrupt network access. During conflict, the AHS will face a hyperactive, lethal, and kinetic fight preventing stable and stationary medical treatment operations. These challenges of the future operational environment will strain the AHS’s ability to complete its enduring mis- sion to conserve the fighting strength.

Recent experiments indicate overwhelming casual- ty rates, which have not been suffered since WorldWar II: in future LSCO, the Army could lose twenty-one

thousand soldiers, a corps-size element, in seven days.7 These experiments establish the need for the AHS

to increase evacuation and treatment holding on the future battlefield while simultaneously conserving

the force’s lethality by returning a high percentage of servicemembers with illness or injury to the front line as swiftly as possible.

The AHS has three operational imperatives to

address the challenges of the future operational en-

vironment, each of which can be supported through

the application of AI. First, the AHS must clear the

battlefield of casualties as a means to unencumber the combat force to move and operate; mobility is para-

mount in future fights. To conserve fighting strength

after clearing the battlefield, the AHS must maximize return to duty as far forward as possible, which starts in garrison with the AHS force health protection (FHP) task. Home station care provides medical readiness

to reduce service member risk for complications

from disease, exposure to infection, and injury, which ensures servicemembers perform effectively even

before deployment. In forward environments, the AHS

**ARMY MEDICINE**

incorporates a sustainment capability by treating those sick or injured through health service support (HSS) tasks performed by operational medical organizations. Finally, the AHS must overcome the expected threat of contested logistics. Military medical resupply will

not outweigh resupply needs of the maneuver force, so the AHS system must remain agile and flexible in its management of medical logistics, especially in mitigat- ing expected mass casualty events.8 Only by addressing all three interwoven imperatives will the AHS fulfill its role in conserving the fighting strength, and AI will be a revolutionary instrument for the AHS to solve infor- mation, risk, and decision-making challenges of LSCO.

**Command and Control**

Just as technology growth has rapidly accelerated

over the last few decades, the Army increasingly relies on data to inform decisions and gain information ad- vantage. From a command-and-control (C2) perspec- tive, there are opportunities to leverage AI to process data rapidly. This is seen today through deep learning, pattern recognition, decision-making through repeti- tion, and AI ability to distinguish unique attributes and elements of an image.9 Follow-on applications of deep learning lead to an ability to automate, incorporate un- manned systems, and reduce human error by offload- ing and accelerating information across a battlefield.

AI accelerates the provision of data to warfighters via rendering faster computers, algorithmic improvements, and access to large amounts of highly-accurate and

well-established data-enabled advances in ML and deep learning.10

The U.S. military’s ability to regain and maintain information and decision advantage relies on the

Combined Joint All-Domain Command and Control (CJADC2) concept. CJADC2 provides a coherent

approach for shaping joint force C2 and producing

the warfighting capability to sense, make sense, and

act at all levels and phases of war, across all domains, and with partners and allies to deliver the information advantage at the speed of relevance while acting inside an adversary’s decision cycle.11

Sense is the ability to discover, collect, correlate,

aggregate, process, and exploit data from all domains

and sources—friendly, adversary, and neutral—and

then share the information to support decision-making. Sensemaking refers to analyzing data to understand

better and predict the operational environment, the actions and intentions of an adversary, and the actions of our friendly forces. Sensemaking data transforms into information, which transforms into knowledge

that enhances the decisions of the joint force and part- ners.12 When human assessment is combined with the technical means to perceive and understand, leaders are better able to take action against adversaries.

The intersection of AI andAHS can potential-

ly revolutionize how military medical professionals

operate on the battlefield.13 With advancements inAI technology, health-care systems hold the potential to become more efficient, accurate, and responsive, saving lives and improving the overall outcomes for wounded soldiers. AI supports the AHS to meet the future chal- lenges by improving surveillance, rapidly treating pa- tients, moving wounded more efficiently, and enabling accurate resupply.

**Maximize Return to Duty**

Early, precise intervention maximizes a warrior’s ability to functionally return to duty and increases

survivability on and off the battlefield. Health-care

technology constantly evolves: systems are more

accurate, less expensive, faster, smaller, and can reach further than humans alone. Health care leverages

data to support clinical decision-making acutely,

routinely, and emergently; incorporating AI will add limitless value. Like most health systems, the AHS are trained professionals whose mission requires aggre-

gating data quickly, often under immense pressure,

to treat and save people and animals—our military

warriors. These overarching functions rely on humans to capture, consolidate, interpret, analyze, and utilize data to inform care. The cornerstone of military

health care remains its people, the most complex

and expensive assets in the military organizations,

however, given constrained resources and feasible

data saturation expected in the future fight,AI will

enhance manpower with machine power. AI across

AHS will function as a health-care multiplier, en-

hancing cost-efficiency, bridging personnel shortages, expanding limited medical capabilities, reducing error, optimizing workflows, improving data processing and analysis, and facilitating precision medicine. AI has

the clinical and operational potential to transform AHS health care across surveillance and delivery

![](/media/202412//1733384081.7666047.png)

**BATDOK**—Battlefield assisted trauma distributed observation kit **MEDLOG**—Medical logistics

**C2**—Command and control **MSTC**—Medical Simulation Training Center

**CBRN**—Chemical, biological, radiological, and nuclear **MTF**—Medical treatment facility

**CASEVAC**—Casualty evacuation **NextGen**—Next generation

**FRSD**—Forward resuscitative surgical detachment **OMIS-A**—Operational medical and information system

**GMFL**—Global medical field laboratory **PCAD**—Prolonged care augmentation detachment

**LSCO**—Large-scale combat operations **SBU-E**—Sensitive but unclassified-encrypted

**MEDCOP**—Medical common operating picture **SFAC**—Security Force Assistance Command

**MEDEVAC**—Medical evacuation

(Figure by authors)

**Medical Capability Development Integration**

**Directorate Concept to Support Army of 2030/2040**

areas. AI will enhance productivity and efficacy of care across FHP at home and through HSS abroad while in the fight.

**Force Health Protection: Surveillance**

AI will enhance FHP through retrospective and

prospective health surveillance and disease prevention programming. By analyzing data, AI informs medical planning and identifies potential health risks topopula- tions and individuals.

AI applications range from predictive analytics to

data consolidation to prediction of disease progression for

those at high risk and prevention measure recommenda- tions. These predictions will allow clinical and nonclinical leaders to intervene early and potentially reduce progres- sion or prevent adverse outcomes altogether.

AI can also look at biometric data aggregation

to inform disease processes, drug administration

reactions, and the need to perform interventions or diagnostics early. One element that will increase data to provide treatment rests within wearable biometric sensor technology, like an individually worn wrist or ring monitor. This live-data source will give providers real-time and cumulative information and educa-

tion to patients on how best to conduct their health,

**ARMY MEDICINE**

![](/media/202412//1733384081.7849822.png)

**BATDOK**—Battlefield assisted trauma distributed observation kit **RAC**—Routine ambulatory care

**CS**—Composite health care system **TC2**—Tactical command and control

**DNBI**—Disease nonbattle injury **TAC**—Trauma acute care

**HALO**—Health assessment lite operation **TMDS**—Theater medical data store

**OMDS**—Operational medicine data store

(Figure by authors)

**Operational Health Information Technology at a Glance**

rehabilitation, and in battle inform treatment. More robustly, this wearable data source input within

the military will provide physical and occupational therapists, nutritionists, behavioral health specialists, primary care providers, and emergency providers

information to improve their treatment plans and

help guide optimal interventions and therapies while automatically populating the patient’s longitudinal health record.

Real-time medical surveillance with trends will

accompany actionable recommendations for leaders

by echelon. Clusters of abnormal vital signs may signal a man-made or natural environmental exposure and

will alert appropriate leaders with suggested causes and related recommendations. These records, informed by aggregate data from all countries and all sources, in-

cluding national ministries of health, the World Health Organization, and nongovernmental organizations,

can provide a more comprehensive health intelligence preparation for the battlefield.

Clinical data from medical sensors will enable

medical leaders across the battlespace to access clinical data and assess the performance of theirrespective

organizations. AI will enable clinical data interpreta- tion, identify patterns linked withoutcomes, develop predictive analytics, and propose solutions to clinical and nonclinical leaders.

In military medical care, to include capabilities out- side a hospital such as veterinary and dentistry capa- bilities, generating anAHS provider necessitates initial schooling, which is an intensive, manpower-heavy task, but AI can help ease this burden. Instead of individual public health and preventative screening exams such as monthly examinations conducted by AHS provid- ers, screenings performed by nonmedical technicians assisted by AI could provide a cost-effective health

risk assessment. Screenings area pu lic health a iv- ity that cannot give the individual patient a diagnosis for intervention but provides a risk assessment for

how likely the person is to have a particular disease. A population-level oral disease survei lance system could enhance the screening process by eliminating human screening decision-making for tens of thousands of sol- diers annua ly. Leveraging AI to inform this and other systems like it can expand the reach of a single trained dentist or provider across the AHS. A ditiona ly,

AI wi l utilize patient data to identify a soldiers risk

factors throughout their career and a ditional health factors area ded to the calculation. When a soldier is prepared for deployment,AI-enhanced risk assessment wi l again become helpful in identifying those most at risk of disease and refer ing them to a treatment facil- ity for diagnosis and treatment. Within the behavioral health footprint alone, AI has the potential to identify those at risk early and o en.

AI enhances FHP through retro ective and pro-

ective health survei lance and disease prevention pro- gramming. AI in FHP informs medical planning.

**Health Service Support: Treatment**

Like nonmilitary healthcare and advances in med- ical uses for AI employment, AHS can leverage AI to improve treatment and hospital management. AI, deep learning, natural lan uage processing, and ML capabil- ities have proven e cacyin nonoperational health-care se ings through improved diagnostic accuracy, treat- ment plans, risk factor assessment, health communica- tion, and health-care administration.14 AI algorithms quickly identify trends, pa erns, and insights. is is

ap lica leat the point of injury, at the bedside, and for C2 oversights to inform survivability and future AHS improvements. e AHS and military medicine can bene t signi cantly from tailoring these analysis tools to operational medicine mission sets.

Fu ly expanding and incorporating the cur ent lev- els of AI infrastructure availa le within healthcare wi l directly ap ly to treatment and care at war. Cur ent in- tegratede orts include using ML algorithms formed- ical diagnostics, predictive modeling, and personalized treatment plans digesting inputs into electronic health records. Employing these tools and others wi l facili-

tate expanded clinical decisionsup ort systems, more widely incorporate evidence-based care, and rapi ly

nar owdi erential diagnoses while providing precision treatment plans at scale. Cha lenges of the future op- erational environment of warfare require the provision of prolonged care in a limited resource environment

and incorporate evolving objective mass casualty plans. AI as a tool wi l empower AHS providers at any level of care to tailor carerapi ly and adaptively to disease diagnostics and patient management and immediately advise within the triage process. For providers from

combat medic to neurosurgeon, AI has the potential to synchronize and standardize care plans and systemati- ca ly inform the most signi cant survivability potential with limited resources.

Today, the adoption of AI into military medicine

is slow yet present, but incorporating AI in AHS wi l maximize return to duty and save lives, while also

informing future clinical pra ice recommendations

AI ena les health-care teams at a l levels to work at the top of their scope, saving lives by mitigating the austeri- ty of future ba le elds.

**Clear the Battlefield**

eunencumbering of maneuver forces relies

signi cantly upon the ability to clear the ba le eld and re ulate patient ow throughout the AHS. AI wi l play an integral role in increasing the accuracy ande cien- cy of processing patient movement requests (PMR) . AI can drastica ly shi the movement of AHS patients, from initiating patient careenroute through providing de nitive care.

When initiating aPMR or 9-Line, data input

technologies such as a biometric weara le wi l transmit data into the Army data fabric. is early noti cation of PMRa lows health-care teams to be er prepare for patient needs, and data can be shared in near-real time through near- eld communication devices or low-orbit sate lites such as drones and ba loons to ensure a com- mon operating picture is availa le for decision-making. As the medic is alerted of a potential casualty, AI au- tomatica lycompletes triage and populates the PMR, a lowing the medic to focus on patient care versus

processing or dispatching administrative data. eAI algorithms consolidate and summarize injury pa erns, survivability triage, transport times, medical sup ly

accessibility, and availa le hospital bed spaces to inform the best evacuation movement decisions. is informa- tion wi l help the medic determine the ap ropriatecare

**ARMY MEDICINE**

![](/media/202412//1733384081.8174355.png)

A Sikorsky UH-60A Black Hawk flies over Fort Campbell, Kentucky, on 5 February 2022 during its first unmanned flight as part of the De- fense Advanced Research Projects Agency’s Aircrew Labor In-Cockpit Automation System program. (Photo courtesy of DARPA)

provision at the point of injury as well as the best pa- tient packaging for transport. This is further supported by AI intelligently tasking a transport platform to the PMRand alerting all roles of care.

AI integration into evacuation platforms can take many forms. Fully autonomous vehicles can operate

without requiring oversight from a human-in-the-loop. Semiautonomous platforms can reduce the number of drivers or pilots required for the platform by reducing the workload for the crew. Incorporating human ma- chine teaming will unload the physical burden required of our medical forces.

**Overcome Contested Logistics**

Quality health care, no matter the environment,

requires medical supplies (Class VIII). The issue of

contested logistics encompasses the dichotomy of

an obstructed supply chain with an inability to gain

dominance in one or more domains on the battlefield. For medical logistics, there is a requirement to move blood rapidly and Class VIII across vast distances

with a high probability that the equipment will not

be flown due to a contested air domain thus increas- ing the delivery time between resupplies. Delivering health care in multidomain operations is further

complicated by weather, road conditions, and water

crossings, increasing the lag time. In this unforgiving battlefield landscape, integrating AI into the delivery of Class VIII becomes a key driver of innovation and efficiency. It is imperative to explore how AI and ML can revolutionize supply chain management in austere

environments and reshape traditional processes that must be replicated in such conditions.15

To overcome contested logistics, AHS will require a robust and integrated medical C2 system that lever- ages data inputs from biometric wearable sensors,

electronic health record systems, and sustainment sys- tems to turn reactive logistics into predictive logistics when coupled with ML and AI.

AI has redefined how the civilian sector predicts and demands plans for decades. Like in health-care

treatment,AI has enhanced supply chain manage-

ment in non-war environments for years. AI algo-

rithms provide significantly more precise demand

forecasts by examining external factors, market

trends, and historical data.16 ML models, such as those used in shipping container yards, constantly learn and adapt to improve predictive logistics.17 Incorporating AI and ML will help reduce excess inventory while

enhancing overall supply chain output.

AI facilitates precise and effective inventory

management for sustainers at the echelon. Through real-time data analysis and predictive analytics, AI can enable C2 and precision sustainment to optimize inventory levels, reduce processing times and costs, improve just-in-time distribution, and save lives.

Planners will have the tools to detect patterns and

variances, providing timely information for com-

manders to aid in proactive decision-making.18 Units on the ground will limit the need to pull when it

comes to resupply when AI will consolidate data and push anticipated needs easier.

ML, a branch of AI, can be leveraged to redefine forecasting and demand planning due to its ability to analyze enormous amounts of data in real-time. This will result in extraordinary accuracy, enabling sus- tainment organizations to adapt quickly at all levels.

ML can be crucial in autonomous decision-mak- ing ina contested environment where supply chains become increasingly complex. Algorithms will sift

through intricate datasets and provide an operating picture that will guide strategic decisions, improving risk management and flexibility.19 Leaders will possess the tools to identify potential risks and implement

preemptive measures that strengthen supply chains against unforeseen threats.

Optimizing logistics challenges using AI, AHS, and the military can maximize the return to duty rate,

increase survivability, and ultimately save warriors’ lives on the battlefield.

**Barriers to Artificial Intelligence in the Army Health System**

Though the potential benefits of AI in supporting the AHS in LSCO are straightforward, several chal- lenges remain. Communications are expected to be denied, degraded, intermittent, and of limited band- width; maneuver force data often retains bandwidth priority over health data transmission. Operational medicine generates significant amounts of data, but current AI systems may still need to be adequately

capable of prioritizing high volumes and velocities

of such health data. Similarly, AI and related appli- cations crucially rely on large amounts of “training

data” to discern patterns and relationships. Modern health-care databasedon casualty flow, injury pat- terns, and health resources in LSCO and MDO do

not yet exist to inform this pattern learning.20 As a

modern technology, AI tools and systems are not yet routinely available in all military medical training

or clinical settings. The need for AI understanding

in medical care and education is evident. However,

military health-care teams will also require realistic training, updated instruction plans, and deliberate, sustained exposure to be ready to adopt AI processes and systems across all operational medicine function- al areas.21

Electronic health-care data also requires additional safety and security protocols to ensure data security,

as the HIPAA Security Rule describes, current-

ly limiting the open transfer of health data across

interoperable battle management command and

control information systems.22 Medical data gener-

ated in operational settings will be compiled within a service member’s longitudinal service health record. While AI supports tools that improve operational

health-care practice, evacuation, logistics, and return to duty, AI does not surmount the need for health

data to be secure, private, unbiased, authentic, and

accurate, together with being fed high-quality, reliable data.23 Protection of operational health data is even

more important than in garrison, as operational data may create risk by revealing troop locations, combat effectiveness, and even the identities of clandestine or high-value personnel.

Additional barriers to AI implementation in the AHS mirror the challenges of AI use in the civilian

sector. AI relies on high-volume data storage systems that are accessible, secure, and extremely expensive.24 In addition to the need for data stewardship and

governance, AI in the AHS must comply with addi- tional military-specific regulations while remaining

valuable and relevant to operational needs.25 Further, the development of AI tools and processes must still be well-integrated into Army operations as well as in austere health-care settings during conflict. Teams

and professionals, such as graduates of the Army

Software Factory, and the development of corps-level chief data officers promote the Army’s data-centric

culture.26 Though the recognition of the need for

technical specialists is present, this remains in nascent stages within AHS and without robust, dedicated

technology teams to iteratively develop, deploy, and maintain such dynamic tools. While the benefits of

AI are apparent in the provision of care, using such

systems to support military medicine broadly requires significant investment in a larger military medical

data strategy.

**Ethics of Using Artificial Intelligence**

Firms within the defense industrial base tend to coordinate with the U.S. government customers’ legal and policy compliance experts to help ensure that AI systems comply. The defense industry’sAI systems

comply with effective utilization of traceable and

**ARMY MEDICINE**

reliable systems and continue to produce equitable, unbiased results throughout their mission life.

ML systems train on collected datasets that in-

clude bias and may exhibit these biases upon creating algorithmic bias. Human language is known as biased; machines trained on the human language are highly likely to be personal, which can lead to prejudices.

Bias and prejudices are preventable using policies and datasets that do not contain discriminatory language. Despite uncertainties and unknowns in developing AI systems. the U.S. government has a work-in-progress to establish policies to determine if an AI model is

sufficiently safe, secure, and ethical for Department of Defense use.27

Trusting the data allows warfighters to exercise

their informed decision-making space to act and

defeat adversaries during the mission. However, AI/ ML algorithms will not fully compensate for gaps in physical understanding. While AI offers numerous

benefits, there are ethical considerations to address.

Questions arise regarding the moral implications of using AI in health care and the potential impact on

human elements such as empathy and compassion. AI is away to augment, rather than replace, the compas- sionate and dedicated provision of military operation- al health care.28

The consideration of using AI in the delivery of

healthcare and health-care decision-making should

require apolicy update within the joint force. Army

Medical Concepts for the Future articulates the ne-

cessity of incorporating automation into healthcare to ensure we do the greatest good for the greatest number of patients. The moral argument remains regarding how “involved” AI and humans remain in decision-making. On one hand, there is an ethical obligation to leverage emerging technologies to create the highest level of care for our soldiers.29 Suppose emerging AI technologies

provide greater good than harm, and the principle of

immediacy deems algorithmic care triage and autono- mous evacuation as a justified means in LSCO. In that case, one can argue that it is ethicalso long that humans remain in the loop.30

**Conclusion**

Artificial intelligence, machine learning, and hu-

man-machine integration can provide information dom- inance on the battlefield and enable warfighters to make

informed decisions at the speed of relevance while pro- jecting power. AI facilitates a common operating picture across the battlefield; AI and ML are critical catalysts for fusing data. AI/ML significantly improve decision-mak- ing and the kill-chain timeline. Human machine teaming allows humans and machines to train together, ensuring the warfighter’s trust in the machine. AI and human

machine teaming reduce the warfighter’s cognitive load, directly optimizing an informed decision-making process while enabling joint force overmatch.

The intersection of AI andAHS can revolutionize military medical support on the battlefield. AI holds

immense potential for enhancing military operations and ensuring the well-being of every soldier. AI tech- nologies such as algorithmic care, medical surveillance, evacuation, and predictive analytics can transform how medical support is provided to the frontlines. By de-

ployingAI systems, the AHS can significantly improve the speed and accuracy of diagnostics, treatment, and decision-making processes. AI algorithms quickly ana- lyze vast amounts of medical data, extending care and evacuation reach, reducing human error, mitigating the risks associated with high-stress combat environments, and enabling health-care professionals to improve out- comes for wounded soldiers and save lives.

AI can enhance the efficiency of logistics and supply chains in the AHS. By leveraging predictive analytics, AI algorithms can anticipate medical supply needs,

optimize inventory management, and streamline the delivery of critical resources across the battlefield. This proactive approach ensures medical personnel have

access to the necessary equipment and medications, enabling them to provide adequate and timely care to wounded soldiers.

AI continues to evolve, making it crucial to

address the ethical implications and potential risks

associated with its use on the battlefield. Safeguards and professional technical oversight must be imple- mented to ensure that AI systems are developed and used responsibly. Transparency, accountability, and a robust governance framework are essential in main- taining the ethical integrity of an AI-enabledAHS. By harnessing the immense power of AI technologies, the AHS has the potential to completely transform military medical support, leading to the preservation of countless lives and playing a critical role in achiev- ing victory in future conflicts. ![](/media/202412//1733384081.8669474.png)

**Notes**

1. Laszlo Monostori, “Artificial Intelligence,” in *CIRP Encyclopedia of Production Engineering*, ed. Sami Chatti et al. (Berlin: Springer,

2019), 73–76, [https://doi.org/10.1007/978-3-662-53120-4\_16703](https://doi.org/10.1007/978-3-662-53120-4_16703).

2. “Homage to John McCarthy, the Father of Artificial Intelligence (AI),” Teneo.ai, accessed 21 March 2024, [https://www.teneo.ai/blog/](https://www.teneo.ai/blog/homage-to-john-mccarthy-the-father-of-artificial-intelligence-ai) [homage-to-john-mccarthy-the-father-of-artificial-intelligence-ai](https://www.teneo.ai/blog/homage-to-john-mccarthy-the-father-of-artificial-intelligence-ai).

3. Christopher Collins et al. “Artificial Intelligence in Infor- mation Systems Research: A Systematic Literature Review and Research Agenda,” *International Journal of Information Man- agement* 60 (2021): Article 102383, [https://doi.org/10.1016/j.](https://doi.org/10.1016/j.ijinfomgt.2021.102383) [ijinfomgt.2021.102383](https://doi.org/10.1016/j.ijinfomgt.2021.102383).

4. Greg Allen, *Understanding AI Technology* (Washington, DC: Department of Defense Joint Artificial Intelligence Center, April 2020), [https://www.ai.mil/docs/Understanding%20AI%20Technol-](https://www.ai.mil/docs/Understanding%20AI%20Technology.pdf) [ogy.pdf.](https://www.ai.mil/docs/Understanding%20AI%20Technology.pdf)

5. Thomas Davenport and Ravi Kalakota, “The Potential for Ar- tificial Intelligence in Healthcare,” *Future Healthcare Journal*, 6 no. 2 (June 2019): 94–98, <https://doi.org/10.7861/futurehosp.6-2-94>.

6. Army Futures Command Pamphlet 71-20-12,*Army Fu-*

*tures Command Concept for Medical 2028* (Austin, TX: U.S. Army

Futures Command, 4 March 2022), [https://api.army.mil/e2/c/down-](https://api.army.mil/e2/c/downloads/2022/04/25/ac4ef855/medical-concept-2028-final-unclas.pdf) [loads/2022/04/25/ac4ef855/medical-concept-2028-final-unclas.pdf.](https://api.army.mil/e2/c/downloads/2022/04/25/ac4ef855/medical-concept-2028-final-unclas.pdf)

7. Todd South, “21,000 Casualties in Seven Days: The Push to Update Medic Training,” Army Times, 6 October 2023, [https://](https://www.armytimes.com/news/your-army/2023/10/06/21000-casualties-in-seven-days-the-push-to-update-medic-training/) [www.armytimes.com/news/your-army/2023/10/06/21000-casual-](https://www.armytimes.com/news/your-army/2023/10/06/21000-casualties-in-seven-days-the-push-to-update-medic-training/) [ties-in-seven-days-the-push-to-update-medic-training/](https://www.armytimes.com/news/your-army/2023/10/06/21000-casualties-in-seven-days-the-push-to-update-medic-training/).

8. James Jones, “Mass Casualty Evacuation and Treatment 18th MDO Gap Decision Brief,” October 2023, Joint Base San Antonio – Fort Sam Houston, Texas.

9. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, “Deep Learning,” *Nature* 521 (2015): 436–44, [https://doi.org/10.1038/](https://doi.org/10.1038/nature14539) [nature14539](https://doi.org/10.1038/nature14539).

10. Simon London, “Ask the AI Experts: What’s Driv-

ing Today’s Progress in AI?,” McKinsey, 7 July 2017, [https://](https://www.mckinsey.com/capabilities/quantumblack/our-insights/ask-the-ai-experts-whats-driving-todays-progress-in-ai) [www.mckinsey.com/capabilities/quantumblack/our-insights/](https://www.mckinsey.com/capabilities/quantumblack/our-insights/ask-the-ai-experts-whats-driving-todays-progress-in-ai) [ask-the-ai-experts-whats-driving-todays-progress-in-ai](https://www.mckinsey.com/capabilities/quantumblack/our-insights/ask-the-ai-experts-whats-driving-todays-progress-in-ai).

11. U.S. Department of Defense, *Summary of the Joint*

*All-Domain Command and Control Strategy* (Washington, DC: U.S. Department of Defense, 17 March 2022), 2, [https://media.defense.](https://media.defense.gov/2022/Mar/17/2002958406/-1/-1/1/SUMMARY-OF-THE-JOINT-ALL-DOMAIN-COMMAND-AND-CONTROL-STRATEGY.PDF) [gov/2022/Mar/17/2002958406/-1/-1/1/SUMMARY-OF-THE-](https://media.defense.gov/2022/Mar/17/2002958406/-1/-1/1/SUMMARY-OF-THE-JOINT-ALL-DOMAIN-COMMAND-AND-CONTROL-STRATEGY.PDF)

[JOINT-ALL-DOMAIN-COMMAND-AND-CONTROL-STRATEGY.](https://media.defense.gov/2022/Mar/17/2002958406/-1/-1/1/SUMMARY-OF-THE-JOINT-ALL-DOMAIN-COMMAND-AND-CONTROL-STRATEGY.PDF) [PDF](https://media.defense.gov/2022/Mar/17/2002958406/-1/-1/1/SUMMARY-OF-THE-JOINT-ALL-DOMAIN-COMMAND-AND-CONTROL-STRATEGY.PDF); J. Stuckey, “Artificial Intelligence Design Engineering for

People and Technology (ADEPT): Open Framework for Imple-

mentingAI Engineering” (AI Engineering Symposium: AAAI Spring Symposium Series 2022, Pittsburgh, PA, 23 March 2022).

12. U.S. Department of Defense, *Summary of the Joint All-Do- main Command and Control Strategy*, 7.

13. Benjamin P. Donham, “It’s Not Just About the Algorithm: Development of a Joint Medical Artificial Intelligence Capability” *Joint Force Quarterly* 111 (4th Quarter, October 2023), [https://](https://ndupress.ndu.edu/JFQ/Joint-Force-Quarterly-111/Article/Article/3569597/its-not-just-about-the-algorithm-development-of-a-joint-medical-artificial-inte/15)

[ndupress.ndu.edu/JFQ/Joint-Force-Quarterly-111/Article/Arti-](https://ndupress.ndu.edu/JFQ/Joint-Force-Quarterly-111/Article/Article/3569597/its-not-just-about-the-algorithm-development-of-a-joint-medical-artificial-inte/15)

[cle/3569597/its-not-just-about-the-algorithm-development-of-a-](https://ndupress.ndu.edu/JFQ/Joint-Force-Quarterly-111/Article/Article/3569597/its-not-just-about-the-algorithm-development-of-a-joint-medical-artificial-inte/15) [joint-medical-artificial-inte/.](https://ndupress.ndu.edu/JFQ/Joint-Force-Quarterly-111/Article/Article/3569597/its-not-just-about-the-algorithm-development-of-a-joint-medical-artificial-inte/15)

[1](https://ndupress.ndu.edu/JFQ/Joint-Force-Quarterly-111/Article/Article/3569597/its-not-just-about-the-algorithm-development-of-a-joint-medical-artificial-inte/15)4. Thomas Davenport and Ravi Kalakota, “The Poten- tial for Artificial Intelligence in Healthcare,” *Future Healthcare Journal* 6, no. 2 (June 2019): 94–98, [https://doi.org/10.7861/](https://doi.org/10.7861/futurehosp.6-2-94) [futurehosp.6-2-94](https://doi.org/10.7861/futurehosp.6-2-94).

15. C.J. Lovelace, “Army Medical Logistics Participates in DOD AI Research and Development,” [Army.mil](http://Army.mil), 19 April 2022, [https://](https://www.army.mil/article/255821) [www.army.mil/article/255821](https://www.army.mil/article/255821).

16. Ben Unglesbee, “AI Opens New Frontier

in Supply Chain Planning,” Supply Chain Dive, 23

May 2023, [https://www.supplychaindive.com/news/](https://www.supplychaindive.com/news/inventory-demand-forecasting-ai-machine-learning/650781/)

[inventory-demand-forecasting-ai-machine-learning/650781/](https://www.supplychaindive.com/news/inventory-demand-forecasting-ai-machine-learning/650781/).

17. Frankie Youd, “Artificial Intelligence to the Rescue: Assisting the Shipping Container Crisis,” Ship Technology, 18 November

2021, [https://www.ship-technology.com/features/artificial-in-](https://www.ship-technology.com/features/artificial-intelligence-to-the-rescue-assisting-the-shipping-container-crisis/?cf-view) [telligence-to-the-rescue-assisting-the-shipping-container-cri-](https://www.ship-technology.com/features/artificial-intelligence-to-the-rescue-assisting-the-shipping-container-crisis/?cf-view) [sis/?cf-view](https://www.ship-technology.com/features/artificial-intelligence-to-the-rescue-assisting-the-shipping-container-crisis/?cf-view).

18. C.J. Lovelace, “Army Medical Logistics Participates in DOD AI Research and Development.”

19. Clint Reiser, Chris Cunnane, and Steve Banker, “The Rise of Machine Learning,” Supply Chain Exchange, 21 August 2019, [https://](https://www.thescxchange.com/articles/1996-the-rise-of-machine-learning) [www.thescxchange.com/articles/1996-the-rise-of-machine-learning](https://www.thescxchange.com/articles/1996-the-rise-of-machine-learning).

20. Jonathan Spirnak and Sameer Antani, “The Need for Artifi- cial Intelligence Curriculum in Military Medical Education,” *Military Medicine* (20 October 2023): usad412, [https://doi.org/10.1093/](https://doi.org/10.1093/milmed/usad412) [milmed/usad412](https://doi.org/10.1093/milmed/usad412).

21. Ibid.

22. “Health Information Privacy,” U.S. Department of Health and Human Services, accessed 25 March, 2024, [https://www.hhs.](https://www.hhs.gov/hipaa/for-professionals/security/index.html) [gov/hipaa/for-professionals/security/index.html](https://www.hhs.gov/hipaa/for-professionals/security/index.html).

23. Robbie Hammer, “Defense Health Agency Paving the Way for Digital-First Health Care Delivery,” Health.mil, 12 October

2023, [https://www.health.mil/News/Dvids-Articles/2023/10/12/](https://www.health.mil/News/Dvids-Articles/2023/10/12/news455622) [news455622](https://www.health.mil/News/Dvids-Articles/2023/10/12/news455622).

24. Damco Solutions, “7 Biggest Barriers to AI Adoption & Their Solutions,” IoT for All, 30 June 2022, [https://www.iotforall.](https://www.iotforall.com/barriers-to-ai-adoption-and-solutions) [com/barriers-to-ai-adoption-and-solutions](https://www.iotforall.com/barriers-to-ai-adoption-and-solutions).

25. Ibid.

26. Army Software Factory, accessed 21 March 2024, [https://](https://soldiersolutions.swf.army.mil/) [soldiersolutions.swf.army.mil/](https://soldiersolutions.swf.army.mil/).

27. Rick Robinson, “AI Ethics: Building Security and Responsi- bility into Intelligent Systems,” Northrop Grumman, accessed 21 March 2024, [https://www.northropgrumman.com/what-we-do/](https://www.northropgrumman.com/what-we-do/ai-ethics-building-security-and-responsibility-into-intelligent-systems/) [ai-ethics-building-security-and-responsibility-into-intelligent-sys-](https://www.northropgrumman.com/what-we-do/ai-ethics-building-security-and-responsibility-into-intelligent-systems/) [tems/](https://www.northropgrumman.com/what-we-do/ai-ethics-building-security-and-responsibility-into-intelligent-systems/).

28. Hammer, “Defense Health Agency Paving the Way for Digital-First Health Care Delivery.”

29. Army Futures Command, *Army Medical Modernization Strategy 2022* (Austin, TX: U.S. Army Futures Command, 1 May 2022).

30. John Ramiccio, “The Ethics of Robotic, Autonomous, and Unmanned Systems in Life-Saving Roles” (Newport, RI: Naval

War College, 12 June 2017), 1–35, [https://apps.dtic.mil/sti/pdfs/](https://apps.dtic.mil/sti/pdfs/AD1041802.pdf) [AD1041802.pdf.](https://apps.dtic.mil/sti/pdfs/AD1041802.pdf)

**Col. Vanessa Worsham, U.S Army Nurse Corps,** is the director of the Army Capability Manager—Army Health Systems

Medical Capability Development Integration Directorate, Futures Concept Center, Army Futures Command, at Joint Base San Antonio, Texas. She holds a Bachelor of Science in Nursing from Howard University, a Business of Nursing graduate certificate from Johns Hopkins University, a master’s degree in national security and resource strategy and a graduate certificate in national security interagency leadership from the Dwight D. Eisenhower School at the National Defense University.

**Lt. Col. Elvis Gonzalez, U.S. Army,** is the Army capability manager for the Army Health System for Medical Logistics at Joint Base San Antonio, Texas.

**Lt. Col. Margaret Kucia, U.S. Army,** is the Army capability manager for the Army Health System for Treatment at Joint Base San Antonio, Texas.

**Lt. Col. Megan Matters, U.S. Army,** is the Army capability manager for the Army Health System for Hospitalization at Joint Base San Antonio, Texas.

**Maj. Thomas Hansen, U.S. Army,** is the Army capability manager for the Army Health System for Medical Command and Control at Joint Base San Antonio, Texas.

**Maj. David Preczewski, U.S. Army,** is the Army capability manager for the Army Health System for Medical Evacuation at Joint Base San Antonio, Texas.

**Maj. Martin Smallidge, U.S. Army,** is the Army capability manager for the Army Health System for Dental at Joint Base San Antonio, Texas.

**Dr. Edward Michaud** is a senior clinical consultant to Army capability managers for the Army Health System at Joint Base San Antonio, Texas.