Brand Stories
artificial intelligence (AI) for command and control and decision-making
Summary points:
- Military AI experts are asking Anthropic, Google Public Sector, and AIQ Phase to develop advanced AI prototypes for national defense.
- Projects focus on cutting-edge AI applications like command and control, situational awareness, cyber operations, and uncrewed systems.
- The initiative strengthens ties between the military and leading AI developers to align emerging technologies with critical warfighting and enterprise requirements. here
WASHINGTON – U.S. military computing experts are asking three companies to find ways of using artificial intelligence for warfighting and large-scale computing that supports decision-making, operations, and administration.
Officials of the Pentagon’s Chief Digital and Artificial Intelligence Office in Washington announced three separate potential $200 million contracts on Monday to develop prototype frontier AI capabilities to address critical national security challenges in across warfighting and enterprise domains.
Key military AI frontiers and capability areas include command and control; predictive analytics and situational awareness; autonomous and uncrewed systems; cyber and information operations; Enterprise transformation; health care delivery; and scenario planning.
Military-industry collaboration
The contracts seek to develop close collaboration between the U.S. Department of Defense (DOD) and pioneering AI companies to give the military access to AI capabilities, and to share the military’s needs with industry.
Frontier AI refers to the latest generation of advanced AI models that push the boundaries of what AI can achieve. These models involve general-purpose high-performance computing built on massive scales.
On these three contracts, the companies will do the work in and around Washington, and should be finished by July 2026. For more information contact Anthropic online at www.anthropic.com; Google Public Sector at https://publicsector.google/; or the Chief Digital and Artificial Intelligence Office at www.ai.mil. AIQ Phase does not have a website.
The market has returned to its highs, along with many top artificial intelligence (AI) names. However, another market dip could always be around the corner.
Let’s look at three top AI stocks that have made strong runs that would be good buys on a pullback.
Image source: Getty Images.
1. Palantir
With a high valuation but attractive growth opportunities, Palantir Technologies (PLTR -0.34%) is a stock that would be attractive on a dip. The company has emerged as one of the market’s most compelling AI growth stories, and its momentum has been gaining speed.
In the first quarter, Palantir posted its seventh consecutive period of accelerating growth, with revenue up 39%. The rise is being led by its U.S. commercial segments, which saw sales jump 71% and the value of future deals soar 127%.
While there is a lot of talk about which company is building the best AI model, Palantir is focused on something far more practical: making AI useful. Its Artificial Intelligence Platform (AIP) uses AI models to help solve real-world problems.
AIP does this by gathering data and then connecting it to physical assets and operational workflows, allowing companies to make AI more useful. As a result, the platform is being used for a growing list of purposes, including hospitals monitoring for sepsis, insurers using it in their underwriting, and energy companies optimizing their pipeline infrastructure.
The company’s largest customer is the U.S. government, which is starting to embrace AI to become more efficient. Last quarter, Palantir’s government revenue climbed 45%.
The company also recently landed a major deal with NATO, expanding into international defense just as Europe ramps up military spending. That gives it three potential growth engines: domestic commercial enterprises, the U.S. government, and now the international public sector.
Yes, the stock is expensive by traditional metrics, but Palantir looks like it’s laying the groundwork to become one of the next megacaps. As such, any pullback could be a great buying opportunity.
2. Nvidia
Nvidia (NVDA -0.42%) has once again been helping lead the market higher. The company recently got good news when the Trump administration said the U.S. would ease chip export controls, allowing the company to resume selling its H20 chips to China. This will add billions in revenue.
Nvidia remains the undisputed champion of AI infrastructure, with its graphics processing units (GPUs) the backbone of this build-out due to their fast processing speeds. And the company has sped up its development cycle to ensure it remains on top.
Over the past two years, data center revenue has exploded from $4.3 billion to more than $39 billion — incredible growth for a company the size of Nvidia. It held a 92% share in the GPU market in the first quarter.
Its chips drive sales, but its secret weapon is its CUDA software. The company created the free platform in 2006 as a way to expand the use of GPUs beyond their original purpose of speeding up graphics in video games.
While it was slow to play out in other end markets, Nvidia smartly pushed CUDA into academia and research labs, where early AI research was being done. That led developers to build directly on CUDA, leading to a growing collection of tools and libraries designed to maximize GPU performance for AI workloads.
If shares of Nvidia dip, be ready to pounce. Data center spending continues to ramp up, and the company has a big opportunity in the automotive market, too, as autonomous and smart vehicles start to become more prevalent.
3. Microsoft
Another company that has seen its stock run up in price is Microsoft (MSFT -0.32%). It dominates the enterprise software space with its Microsoft 365 suite of worker productivity products and has one of the leading cloud computing companies in Azure.
The cloud remains the company’s fastest-growing business, with the unit producing revenue growth of 30% or more each of the past seven quarters. Azure revenue jumped 33% last quarter (35% in constant currency), with nearly half of that coming from AI services. Growth could have been even higher, but Microsoft has been hitting capacity constraints.
As such, it plans to ramp up capital expenditures (capex) in fiscal 2026 with a focus on adding GPUs and servers. It said those assets are more directly tied to AI revenue than buying the buildings that house them. That’s a smart move that should support continued cloud computing momentum.
Microsoft’s $10 billion investment in OpenAI gave it an early AI lead, especially with Azure initially being granted exclusive access to its leading large language models (LLMs). Companies continue to be attracted to OpenAI’s popular AI models, and Azure gives its customers direct access to them.
It has also embedded OpenAI’s models throughout its ecosystem to run its Copilot, which is gaining popularity with businesses. At $30 per enterprise user per month, it offers a lot of strong upside.
That said, the OpenAI partnership is getting complicated. The exclusivity deal is over, and the two sides are reportedly negotiating new terms as the AI provider looks to restructure. Still, Microsoft remains entitled to 49% of OpenAI Global’s profits up to a tenfold return on its investment — potentially a huge payday.
Microsoft remains in a strong long-term position. The stock’s recent run-up makes it an attractive candidate to buy on any pullback.
Brand Stories
Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence
Lees, C. C. et al. ISUOG Practice Guidelines: diagnosis and management of small-for-gestational-age fetus and fetal growth restriction. Ultrasound Obstet. Gynecol. 56, 298–312 (2020).
Martin. Health—United Nations Sustainable Development. United Nations Sustainable Development https://www.un.org/sustainabledevelopment/health/#tab-3f22056b0e91266e8b2 (2023).
Krasevec, J. et al. Study protocol for UNICEF and WHO estimates of global, regional, and national low birthweight prevalence for 2000 to 2020. Gates Open Res. 6, 80 (2022).
Blencowe, H. et al. National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: a systematic analysis. Lancet Glob. Health 7, e849–e860 (2019).
Jia, C. H. et al. Short term outcomes of extremely low birth weight infants from a multicenter cohort study in Guangdong of China. Sci. Rep. 12, 11119 (2022).
Low birth weight. https://www.who.int/data/nutrition/nlis/info/low-birth-weight.
Okwaraji, Y. B. et al. National, regional, and global estimates of low birthweight in 2020, with trends from 2000: a systematic analysis. Lancet 403, 1071–1080 (2024).
International Institute for Population Sciences (IIPS) and Macro International. 2007. National Family Health Survey (NFHS-3), 2005–06: India: Volume I. Mumbai: IIPS.
Singh, D. et al. Prevalence and correlates of low birth weight in India: findings from National Family Health Survey 5. BMC Pregnancy Childbirth 23, 1–10 (2023).
Sabbaghchi, M., Jalali, R. & Mohammadi, M. A systematic review and meta-analysis on the prevalence of low birth weight infants in Iran. J. Pregnancy 2020, 1–7 (2020).
Institute of Medicine (US) Committee on Improving Birth Outcomes. In Improving Birth Outcomes: Meeting the Challenge in the Developing World (eds. Bale, J. R., Stoll, B. J., Lucas, A. O.) (National Academies Press, 2003). https://www.ncbi.nlm.nih.gov/books/NBK222095/.
Derakhshi, B., Esmailnasab, N., Ghaderi, E. & Hemmatpour, S. Risk factor of preterm labor in the west of Iran: a case-control study. DOAJ 43, 499–506 (2014).
Moradi, G., Zokaeii, M., Goodarzi, E. & Khazaei, Z. Maternal risk factors for low birth weight infants: a nested case-control study of rural areas in Kurdistan (western of Iran). J. Prev. Med. Hyg. (2021).
Katz, J. et al. Mortality risk in preterm and small-for-gestational-age infants in low-income and middle-income countries: a pooled country analysis. Lancet 382, 417–425 (2013).
Knop, M. R. et al. Birth weight and risk of Type 2 diabetes mellitus, cardiovascular disease, and hypertension in adults: a meta-analysis of 7,646,267 participants from 135 studies. J. Am. Heart Assoc. 7, e008870 (2018).
Wu, M. et al. Estimation of fetal weight by ultrasonic examination. Int. J. Clin. Exp. Med. 8, 540–545 (2015).
Dwivedi, Y. K. et al. Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manag. 57, 101994 (2021).
Alanazi, A. Using machine learning for healthcare challenges and opportunities. Inform. Med. Unlocked 30, 100924 (2022).
Reddy, S. Explainability and artificial intelligence in medicine. Lancet Digit. Health 4, e214–e215 (2022).
Reza, T. B. & Salma, N. Prediction and feature selection of low birth weight using machine learning algorithms. J. Health Popul. Nutr. 43, 1–10 (2024).
Ranjbar, A. et al. Machine learning-based approach for predicting low birth weight. BMC Pregnancy Childbirth 23, 1–9 (2023).
de Morais, F. L. et al. Utilization of tree-based machine learning models for predicting low birth weight cases. BMC Pregnancy Childbirth 25, 207 (2025).
Naimi, A. I., Platt, R. W. & Larkin, J. C. Machine learning for fetal growth prediction. Epidemiology 29, 290 (2018).
Tao, J., Yuan, Z., Sun, L., Yu, K. & Zhang, Z. Fetal birthweight prediction with measured data by a temporal machine learning method. BMC Med. Inform. Decis. Mak. 21, 1–10 (2021).
Rubaiya, N., Mansur, M., Alam, M. M. & Rayhan, M. I. Unraveling birth weight determinants: integrating machine learning, spatial analysis, and district-level mapping. Heliyon 10, e27341 (2024).
Sadeghi, Z. et al. A review of explainable artificial intelligence in healthcare. Comput. Electr. Eng. 118, 109370 (2024).
Gill, S. V., May-Benson, T. A., Teasdale, A. & Munsell, E. G. Birth and developmental correlates of birth weight in a sample of children with potential sensory processing disorder. BMC Pediatr. 13, 1–9 (2013).
Tarín, J. J., García-Pérez, M. A. & Cano, A. Potential risks to offspring of intrauterine exposure to maternal age-related obstetric complications. Reprod. Fertil. Dev. 29, 1468–1475 (2016).
Wang, S. et al. Changing trends of birth weight with maternal age: a cross-sectional study in Xi’an city of Northwestern China. BMC Pregnancy Childbirth 20, 1–7 (2020).
Softa, S. M. et al. The association of maternal height with mode of delivery and fetal birth weight at King Abdulaziz University Hosc, Jeddah,. Saudi Arabia. Cureus 14, e27493 (2022).
Rochow, N. et al. Maternal body height is a stronger predictor of birth weight than ethnicity: analysis of birth weight percentile charts. J. Perinat. Med. 47, 22–29 (2018).
Spada, E., Chiossi, G., Coscia, A., Monari, F. & Facchinetti, F. Effect of maternal age, height, BMI and ethnicity on birth weight: an Italian multicenter study. J. Perinat. Med. 46, 1016–1021 (2017).
Ludwig, D. S. & Currie, J. The association between pregnancy weight gain and birthweight: a within-family comparison. Lancet 376, 984–990 (2010).
Hussey, M. R. et al. Associations of placental lncRNA expression with maternal pre-pregnancy BMI and infant birthweight in two birth cohorts. J. Dev. Orig. Health Dis. 16, 1–10 (2025).
Garces, A. et al. Association of parity with birthweight and neonatal death in five sites: The Global Network’s Maternal Newborn Health Registry study. Reprod. Health 17, 1–8 (2020).
Shah, P. S. Parity and low birth weight and preterm birth: a systematic review and meta-analyses. Acta Obstet. Gynecol. Scand. 89, 862–875 (2010).
Wang, Y. A. et al. Preterm birth and low birth weight after assisted reproductive technology-related pregnancy in Australia between 1996 and 2000. Fertil. Steril. 83, 1650–1658 (2005).
Zhong, X. et al. Effect of parental physiological conditions and assisted reproductive technologies on the pregnancy and birth outcomes in infertile patients. Oncotarget 8, 18409–18416 (2016).
Rahmati, S., Azami, M., Badfar, G., Parizad, N. & Sayehmiri, K. The relationship between maternal anemia during pregnancy with preterm birth: a systematic review and meta-analysis. J. Matern. Fetal Neonatal Med. 33, 2679–2689 (2018).
Sherwani, S. I., Khan, H. A., Ekhzaimy, A., Masood, A. & Sakharkar, M. K. Significance of HbA1C test in diagnosis and prognosis of diabetic patients. Biomark. Insights 11, BMI.S38440 (2016).
Gabbe, S. Management of diabetes mellitus complicating pregnancy. Obstet. Gynecol. 102, 857–868 (2003).
Li, J. et al. Maternal TSH levels at first trimester and subsequent spontaneous miscarriage: a nested case–control study. Endocr. Connect. 8, 1288–1293 (2019).
Nishioka, E. et al. Relationship between maternal thyroid-stimulating hormone (TSH) elevation during pregnancy and low birth weight: A longitudinal study of apparently healthy urban Japanese women at very low risk. Early Hum. Dev. 91, 181–185 (2015).
Bilardo, C. M. et al. ISUOG Practice Guidelines (updated): performance of 11–14-week ultrasound scan. Ultrasound Obstet. Gynecol. 61, 127–143 (2023).
Kalousová, M., Muravská, A. & Zima, T. Pregnancy-associated plasma protein A (PAPP-A) and preeclampsia. Adv. Clin. Chem. 63, 169–209 (2014).
Shiefa, S., Amargandhi, M., Bhupendra, J., Moulali, S. & Kristine, T. First trimester maternal serum screening using biochemical markers PAPP-A and free Β-HCG for Down syndrome, Patau syndrome and Edward syndrome. Indian J. Clin. Biochem. 28, 3–12 (2012).
Salomon, L. J. et al. ISUOG Practice Guidelines (updated): performance of the routine mid-trimester fetal ultrasound scan. Ultrasound Obstet. Gynecol. 59, 840–856 (2022).
Ogonowski, J. & Miazgowski, T. Intergenerational transmission of macrosomia in women with gestational diabetes and normal glucose tolerance. Eur. J. Obstet. Gynecol. Reprod. Biol. 195, 113–116 (2015).
Yang, Y. et al. The association of gestational diabetes mellitus with fetal birth weight. J. Diabetes Complications 32, 635–642 (2018).
Ardissino, M. et al. Maternal hypertension increases risk of preeclampsia and low fetal birthweight: Genetic evidence from a Mendelian randomization study. Hypertension 79, 588–598 (2022).
Cutland, C. L. et al. Low birth weight: case definition & guidelines for data collection, analysis, and presentation of maternal immunization safety data. Vaccine 35, 6492–6500 (2017).
Aziz, F., Khan, M. F. & Moiz, A. Gestational diabetes mellitus, hypertension, and dyslipidemia as the risk factors of preeclampsia. Sci. Rep. 14, 1–11 (2024).
Celik, N. Welch’s ANOVA: Heteroskedastic skew-t error terms. Commun. Stat. Theory Methods 51, 3065–3076 (2022).
Mujahid, M. et al. Data oversampling and imbalanced datasets: an investigation of performance for machine learning and feature engineering. J Big Data 11, 87 (2024).
Cerda, P., Varoquaux, G. & Kégl, B. Similarity encoding for learning with dirty categorical variables. Mach. Learn. 107, 1477–1494 (2018).
Alkhawaldeh, I. M., Albalkhi, I. & Naswhan, A. J. Challenges and limitations of synthetic minority oversampling techniques in machine learning. World J. Methodol. 13, 373–378 (2023).
Abedin, M. Z., Guotai, C., Hajek, P. & Zhang, T. Combining weighted SMOTE with ensemble learning for the class-imbalanced prediction of small business credit risk. Complex Intell. Syst. 9, 3559–3579 (2022).
Kraskov, A., Stögbauer, H. & Grassberger, P. Estimating mutual information. Phys. Rev. E 69, 066138 (2004).
Shipe, M. E., Deppen, S. A., Farjah, F. & Grogan, E. L. Developing prediction models for clinical use using logistic regression: an overview. J. Thorac. Dis. 11, S574–S584 (2019).
Montomoli, J. et al. Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients. J. Intensive Med. 1, 110–116 (2021).
Jabbar, M. A., Deekshatulu, B. L. & Chandra, P. Classification of heart disease using K-nearest neighbor and genetic algorithm. arXiv preprint arXiv:1508.02061 (2015).
Zheng, J. et al. Clinical data based XGBoost algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012–2021) multicenter retrospective case-control study. BMC Gastroenterol. 23, 1–13 (2023).
Loef, B. et al. Using random forest to identify longitudinal predictors of health in a 30-year cohort study. Sci. Rep. 12, 1–12 (2022).
Wallace, M. L. et al. Use and misuse of random forest variable importance metrics in medicine: demonstrations through incident stroke prediction. BMC Med. Res. Methodol. 23, 1–12 (2023).
Singh, D. & Singh, B. Investigating the impact of data normalization on classification performance. Appl. Soft Comput. 97, 105524 (2019).
Kaliappan, J. et al. Impact of cross-validation on machine learning models for early detection of intrauterine fetal demise. Diagnostics (Basel) 13, 1692 (2023).
Elgeldawi, E., Sayed, A., Galal, A. R. & Zaki, A. M. Hyperparameter tuning for machine learning algorithms used for Arabic sentiment analysis. Informatics 8, 79 (2021).
Shekar, B. H. & Dagnew, G. Grid search-based hyperparameter tuning and classification of microarray cancer data. In Proc. Int. Conf. Adv. Comput. Commun. Paradigms (ICACCP) (2019). https://doi.org/10.1109/ICACCP.2019.8882943
Chadaga, K. et al. Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers. Sci. Rep. 14, 1–14 (2024).
Tougui, I., Jilbab, A. & Mhamdi, J. E. Impact of the choice of cross-validation techniques on the results of machine learning-based diagnostic applications. Healthc. Inform. Res. 27, 189–199 (2021).
Rodríguez-Pérez, R. & Bajorath, J. Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. J. Comput. Aided Mol. Des. 34, 1013–1026 (2020).
Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).
Abdullakutty, F. et al. Histopathology in focus: a review on explainable multi-modal approaches for breast cancer diagnosis. Front. Med. 11, 1450103 (2024).
Javaid, M., Haleem, A., Pratap Singh, R., Suman, R. & Rab, S. Significance of machine learning in healthcare: Features, pillars and applications. Int. J. Intell. Netw. 3, 58–73 (2022).
Arain, Z., Iliodromiti, S., Slabaugh, G., David, A. L. & Chowdhury, T. T. Machine learning and disease prediction in obstetrics. Curr. Res. Physiol. 6, 100099 (2023).
Alzubaidi, M. et al. Ensemble transfer learning for fetal head analysis: From segmentation to gestational age and weight prediction. Diagnostics 12, 2229 (2022).
Hughes, M. M., Black, R. E. & Katz, J. 2500-g low birth weight cutoff: history and implications for future research and policy. Matern. Child Health J. 21, 283 (2016).
Zeegers, B. et al. Birthweight charts customised for maternal height optimises the classification of small and large-for-gestational age newborns. Acta Paediatr. https://doi.org/10.1111/APA.17332 (2024).
Teoh, Z.H., Mariapun, J., Ko, V.S.Y., Dominic, N.A., Jeganathan, R., Karalasingam, S.D. & Thirunavuk Arasoo, V.J. Maternal height, and ethnicity and birth weight: A retrospective cohort study of uncomplicated term vaginal deliveries in Malaysia. Birth. 51, 620–628 (2024).
Sutan, R., Mohtar, M., Mahat, A. N. & Tamil, A. M. Determinant of low birth weight infants: a matched case control study. Open J. Prev. Med. 4, 91–99 (2014).
To, W. W. K., Cheung, W. & Kwok, J. S. Y. Paternal height and weight as determinants of birth weight in a Chinese population. Am. J. Perinatol. 15, 545–548 (1998).
Pölzlberger, E., Hartmann, B., Hafner, E., Stümpflein, I. & Kirchengast, S. Maternal height and pre-pregnancy weight status are associated with fetal growth patterns and newborn size. J. Biosoc. Sci. 49, 392–407 (2017).
Ay, L. et al. Maternal anthropometrics are associated with fetal size in different periods of pregnancy and at birth: the Generation R Study. BJOG 116, 953–963 (2009).
Mathai, M. et al. Ethnicity and fetal growth in Fiji. Aust. N. Z. J. Obstet. Gynaecol. 44, 318–321 (2004).
Figueras, F. et al. Customized birthweight standards for a Spanish population. Eur. J. Obstet. Gynecol. Reprod. Biol. 136, 20–24 (2008).
Britto, R. P. D. A. et al. Influence of maternal height and weight on low birth weight: a cross-sectional study in poor communities of northeastern Brazil. PLoS ONE 8, e000000 (2013).
Mongelli, M., Figueras, F., Francis, A. & Gardosi, J. A customised birthweight centile calculator developed for an Australian population. Aust. N. Z. J. Obstet. Gynaecol. 47, 128–131 (2007).
McCowan, L., Stewart, A. W., Francis, A. & Gardosi, J. A customised birthweight centile calculator developed for a New Zealand population. Aust. N. Z. J. Obstet. Gynaecol. 44, 428–431 (2004).
Zhang, G. et al. Assessing the causal relationship of maternal height on birth size and gestational age at birth: a Mendelian randomization analysis. PLoS Med. 12, e100000 (2015).
Kaur, S. et al. Risk factors for low birth weight among rural and urban Malaysian women. BMC Public Health 19, 539 (2019).
Inoue, S. et al. Association between short maternal height and low birth weight: a hospital-based study in Japan. J. Korean Med. Sci. 31, 353–359 (2016).
Retnakaran, R. et al. Association of timing of weight gain in pregnancy with infant birth weight. JAMA Pediatr. 172, 136–142 (2018).
Mohapatra, I., Harshini, N., Samantaray, S. R. & Naik, G. Association between early pregnancy body mass index and gestational weight gain in relation to neonatal birth weight. Cureus 14, e23000 (2022).
Arora, P. & Tamber Aeri, B. Gestational weight gain among healthy pregnant women from Asia in comparison with Institute of Medicine (IOM) Guidelines-2009: a systematic review. J. Pregnancy 2019, 3849596 (2019).
Hackmon, R. et al. Do early fetal measurements and nuchal translucency correlate with term birth weight?. J. Obstet. Gynaecol. Can. 39, 750–756 (2017).
Jackson, M. & Rose, N. C. Diagnosis and management of fetal nuchal translucency. Semin. Roentgenol. 33, 333–338 (1998).
Kalem, Z., Kaya, A. E., Bakırarar, B. & Kalem, M. N. Fetal nuchal translucency: is there an association with birthweight and neonatal wellbeing?. Turk. J. Obstet. Gynecol. 16, 35–40 (2019).
Napolitano, R. et al. Pregnancy dating by fetal crown–rump length: a systematic review of charts. BJOG 121, 556–565 (2014).
Fries, N. et al. The impact of optimal dating on the assessment of fetal growth. BMC Pregnancy Childbirth 21, 1–8 (2021).
Salomon, L. J. Early fetal growth: concepts and pitfalls. Ultrasound Obstet. Gynecol. 35, 385–389 (2010).
Papageorghiou, A. T. et al. International standards for early fetal size and pregnancy dating based on ultrasound measurement of crown–rump length in the first trimester of pregnancy. Ultrasound Obstet. Gynecol. 44, 641 (2014).
Kozuki, N. et al. The associations of parity and maternal age with small-for-gestational-age, preterm, and neonatal and infant mortality: A meta-analysis. BMC Public Health 13(Suppl 3), S2 (2013).
Hinkle, S. N. et al. The association between parity and birthweight in a longitudinal consecutive pregnancy cohort. Paediatr. Perinat. Epidemiol. 28, 106 (2013).
Krulewitch, C. J., Herman, A. A., Yu, K. F. & Johnson, Y. R. Does changing paternity contribute to the risk of intrauterine growth retardation?. Paediatr. Perinat. Epidemiol. 11, 41–47 (1997).
Luo, Z. C. et al. The effects and mechanisms of primiparity on the risk of pre-eclampsia: a systematic review. Paediatr. Perinat. Epidemiol. 21, 36–45 (2007).
Sørnes, T. & Bakke, T. Uterine size, parity and umbilical cord length. Acta Obstet. Gynecol. Scand. 68, 439–441 (1989).
Clapp, J. F. & Capeless, E. Cardiovascular function before, during, and after the first and subsequent pregnancies. Am. J. Cardiol. 80, 1469–1473 (1997).
Dasa, T. T., Okunlola, M. A. & Dessie, Y. Effect of grand multiparity on the adverse birth outcome: a hospital-based prospective cohort study in Sidama Region, Ethiopia. Int. J. Womens Health 14, 363 (2022).
Lin, L., Lu, C., Chen, W., Li, C. & Guo, V. Y. Parity and the risks of adverse birth outcomes: a retrospective study among Chinese. BMC Pregnancy Childbirth 21, 1–11 (2021).
Baer, R. J., Lyell, D. J., Norton, M. E., Currier, R. J. & Jelliffe-Pawlowski, L. L. First trimester pregnancy-associated plasma protein-A and birth weight. Eur. J. Obstet. Gynecol. Reprod. Biol. 198, 1–6 (2016).
Turrado Sánchez, E. M., De Miguel Sánchez, V. & Macía Cortiñas, M. Correlation between PAPP-A levels determined during the first trimester and birth weight at full-term. Reprod. Sci. 30, 3235 (2023).
Zhao, D. et al. Association between maternal blood glucose levels during pregnancy and birth outcomes: a birth cohort study. Int. J. Environ. Res. Public Health 20, 2102 (2023).
Everett, T. R. & Lees, C. C. Beyond the placental bed: placental and systemic determinants of the uterine artery Doppler waveform. Placenta 33, 893–901 (2012).
Liu, Y. et al. Impact of gestational hypertension and preeclampsia on low birthweight and small-for-gestational-age infants in China: a large prospective cohort study. J. Clin. Hypertens. 23, 835 (2021).
Gibbs, C. M., Wendt, A., Peters, S. & Hogue, C. J. The impact of early age at first childbirth on maternal and infant health. Paediatr. Perinat. Epidemiol. 26, 259–284 (2012).
Markovitz, B. P., Cook, R., Flick, L. H. & Leet, T. L. Socioeconomic factors and adolescent pregnancy outcomes: distinctions between neonatal and post-neonatal deaths?. BMC Public Health 5, 279 (2005).
Sharma, V. et al. Young maternal age and the risk of neonatal mortality in rural Nepal. Arch. Pediatr. Adolesc. Med. 162, 828–835 (2008).
Alam, N. Teenage motherhood and infant mortality in Bangladesh: maternal age-dependent effect of parity one. J. Biosoc. Sci. 32, 229–236 (2000).
Restrepo-Méndez, M. C. et al. Childbearing during adolescence and offspring mortality: findings from three population-based cohorts in southern Brazil. BMC Public Health 11, 781 (2011).
Paranjothy, S., Broughton, H., Adappa, R. & Fone, D. Teenage pregnancy: who suffers?. Arch. Dis. Child. 94, 239–245 (2009).
Lawlor, D. A., Mortensen, L. & Andersen, A. M. N. Mechanisms underlying the associations of maternal age with adverse perinatal outcomes: a sibling study of 264,695 Danish women and their firstborn offspring. Int. J. Epidemiol. 40, 1205–1214 (2011).
Fall, C. H. D. et al. Association between maternal age at childbirth and child and adult outcomes in the offspring: a prospective study in five low-income and middle-income countries (COHORTS collaboration). Lancet Glob. Health 3, e366–e377 (2015).
Alves, C., Jenkins, S. M. & Rapp, A. Early pregnancy loss (spontaneous abortion) (StatPearls, 2023).
Jackson, T. & Watkins, E. Early pregnancy loss. J. Am. Acad. Physician Assist. 34, 22–27 (2021).
Nicolaides, K. H. A model for a new pyramid of prenatal care based on the 11 to 13 weeks’ assessment. Prenat. Diagn. 31, 3–6 (2011).
Bekele, W. T. Machine learning algorithms for predicting low birth weight in Ethiopia. BMC Med. Inform. Decis. Mak. 22, 1–16 (2022).
Arayeshgari, M., Najafi-Ghobadi, S., Tarhsaz, H., Parami, S. & Tapak, L. Machine learning-based classifiers for the prediction of low birth weight. Healthc. Inform. Res. 29, 54 (2023).
Pollob, S. M. A. I., Abedin, M. M., Islam, M. T., Islam, M. M. & Maniruzzaman, M. Predicting risks of low birth weight in Bangladesh with machine learning. PLoS ONE 17, e000000 (2022).
Khan, W. et al. Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms. Sci. Rep. 12, 1–12 (2022).
Hussain, Z. & Borah, M. D. Birth weight prediction of newborn baby with application of machine learning techniques on features of mother. J. Stat. Manag. Syst. 23, 1079–1091 (2020).
Alabbad, D. A. et al. Birthweight range prediction and classification: a machine learning-based sustainable approach. Mach. Learn. Knowl. Extr. 6, 770–788 (2024).
Dola, S. S. & Valderrama, C. E. Exploring parental factors influencing low birth weight on the 2022 CDC natality dataset. BMC Med. Inform. Decis. Mak. 24, 367 (2024).
Liu, Q. et al. Machine learning approaches for predicting fetal macrosomia at different stages of pregnancy: a retrospective study in China. BMC Pregnancy Childbirth 25, 1–10 (2025).
Citizen Service AI Market: Transforming Public Sector Services with Intelligent Technology
The integration of Artificial Intelligence (AI) into public sector services has emerged as a powerful force for driving government innovation, improving operational efficiency, and delivering citizen-centric services. Governments around the world are increasingly embracing Citizen Service AI solutions to modernize how they engage with constituents. This market is not only evolving rapidly in scale but also in sophistication, with applications ranging from chatbots and virtual assistants to predictive analytics and facial recognition for public safety.
According to a recent analysis by Persistence Market Research, the global Citizen Service AI market is set for exponential growth, rising from a value of US$ 9.1 billion in 2023 to US$ 81.3 billion by 2030, representing an impressive CAGR of 36.7% over the forecast period. This surge reflects governments’ increasing need to provide efficient, transparent, and responsive services to an increasingly digital population.
Understanding Citizen Service AI: Market Introduction and Definition
Citizen Service AI refers to the application of AI-driven technologies to streamline and enhance public services provided by government institutions. It involves the use of intelligent tools such as machine learning, natural language processing (NLP), chatbots, image processing, and facial recognition to automate, personalize, and optimize interactions between citizens and public service departments.
By deploying AI-powered systems, governments aim to reduce administrative burdens, minimize bureaucratic inefficiencies, and deliver services in real-time. Whether it’s applying for permits, accessing healthcare information, reporting public grievances, or receiving traffic updates, Citizen Service AI ensures a seamless and user-friendly experience.
Key Drivers Accelerating Market Growth
1. Demand for Enhanced Citizen Engagement and Satisfaction
One of the primary growth drivers for the Citizen Service AI market is the need to improve citizen engagement and satisfaction. Citizens today expect services that are fast, responsive, and personalized. AI enables governments to deliver services that are proactive rather than reactive, empowering citizens to engage with agencies through intuitive digital interfaces.
For instance, AI-driven virtual assistants can handle thousands of queries simultaneously, 24/7, providing consistent and accurate responses. This not only reduces wait times but also ensures better access to information, particularly during emergencies or high-demand periods.
2. Rising Government Investments in Digital Transformation
Many national and regional governments are prioritizing digital transformation initiatives. These strategies aim to modernize public administration, increase transparency, and optimize service delivery through cutting-edge technologies. The allocation of public funds to AI-driven projects—including smart city initiatives and AI-enhanced public health systems—is contributing significantly to market expansion.
3. Efficiency Gains and Cost Savings
Another major advantage of Citizen Service AI is its ability to streamline government operations. By automating routine tasks such as form processing, appointment scheduling, and data entry, agencies can significantly cut down on operational costs while reallocating human resources to more complex, high-impact functions.
Machine learning algorithms also support data-driven decision-making, enabling governments to identify emerging needs, forecast public service demand, and allocate resources more effectively.
What Are the Most Promising Applications of AI in Government Services?
AI is being applied across various segments of public administration. Key use cases include:
• Traffic and Transportation Management: AI helps optimize traffic flow, manage congestion, and enhance public transit systems through real-time data.
• Healthcare: From predictive modeling to chatbot-based symptom checkers, AI is transforming how citizens access and receive medical care.
• Public Safety: Facial recognition, surveillance analytics, and emergency response systems use AI to improve security and reduce response times.
• Utilities and General Services: AI-driven monitoring systems support efficient resource usage and environmental conservation.
How is AI transforming public service delivery in government sectors?
AI is revolutionizing public service delivery by making government operations more efficient, responsive, and citizen-centric. Through AI-powered chatbots, virtual assistants, and predictive analytics, public agencies can automate routine interactions, deliver 24/7 support, and proactively address citizen needs. This not only reduces wait times and operational costs but also enhances user satisfaction. Moreover, AI enables personalized communication, better resource allocation, and smarter policy decisions—ultimately fostering trust, transparency, and inclusiveness in governance.
Barriers to Growth: Data Privacy and the Digital Divide
Despite its advantages, the Citizen Service AI market faces notable challenges that must be addressed to ensure sustainable growth.
1. Data Privacy and Security Concerns
The increased use of AI in public services necessitates the collection and processing of vast amounts of citizen data. This raises concerns regarding data misuse, unauthorized access, and potential privacy breaches. Governments must implement robust cybersecurity protocols and ethical AI frameworks to protect sensitive data and maintain public trust.
2. Digital Accessibility and Equity
While AI can streamline access to services, it can also exacerbate existing inequalities if certain populations—such as the elderly, low-income groups, or rural residents—lack access to digital devices or internet connectivity. Bridging the digital divide through education, infrastructure development, and inclusive design is essential for ensuring equitable benefits from AI-enabled governance.
Market Opportunities: Data-Driven Governance and Smart Cities
The potential for AI to improve cost-effectiveness and public service efficacy presents a compelling opportunity for market players. By leveraging real-time data, governments can adopt predictive analytics to anticipate citizen needs, respond to crises swiftly, and formulate policies based on behavioral trends.
Smart city initiatives further bolster this potential, as governments integrate AI into transportation networks, utilities, and emergency services. From traffic light automation to waste management, these applications are transforming urban governance into a smarter, greener, and more responsive ecosystem.
Analyst Perspective: Strategic Growth and Public-Private Collaboration
The Citizen Service AI market is in a phase of transformative expansion, powered by a clear recognition among governments and enterprises of AI’s game-changing capabilities. The future of this market lies in close collaboration between public sector bodies and technology providers.
Leading AI solution providers like Amazon Web Services, Microsoft, IBM, and Google are aligning their technologies to address the unique challenges of public governance. These collaborations not only ensure technological innovation but also create tailored, scalable solutions that address localized needs.
However, to fully harness the potential of AI, stakeholders must address ethical considerations and implement robust regulatory frameworks. This includes establishing transparency in AI decision-making, ensuring data accountability, and promoting digital literacy.
Competitive Landscape: Key Players and Innovation Leaders
Several major technology companies dominate the Citizen Service AI market. These include:
• IBM: Through its Watson AI platform, IBM delivers predictive analytics and personalized citizen engagement tools.
• Microsoft Azure: Offers comprehensive AI solutions for government departments through cloud-based cognitive services.
• Amazon Web Services (AWS): Powers AI applications with scalable infrastructure; notable partnership with the Canadian government on the Citizen Care Pod for public health.
• Google Cloud: Known for natural language processing and conversational AI tools that enhance public communication.
Other emerging and established players include NVIDIA, Accenture, Pegasystems, Tencent, ServiceNow, and Intel Corporation—each contributing to the AI-powered transformation of public services through research, innovation, and strategic alliances.
Regional Insights: Market Leaders and Emerging Regions
• North America leads the adoption of Citizen Service AI, driven by digital transformation in the U.S. and Canada.
• Europe follows closely, with smart governance initiatives in countries like the UK, Germany, and the Nordics.
• Asia-Pacific, particularly China and Singapore, is witnessing rapid growth due to government-backed smart city programs.
• Latin America, Middle East & Africa are emerging markets with significant potential, supported by increasing digitization and AI readiness.
Conclusion: A Future Shaped by AI-Enabled Governance
The Citizen Service AI market represents a groundbreaking shift in how governments interact with and serve their citizens. From reducing administrative overhead to offering personalized, real-time support, AI has the potential to redefine public service delivery on a global scale.
With the market projected to grow at a staggering 36.7% CAGR from 2023 to 2030, reaching US$ 81.3 billion, the opportunities are vast. However, for this promise to be fully realized, it is essential that governments and technology providers collaborate to ensure secure, ethical, and inclusive deployment.
The next decade will likely witness a transformation where AI not only makes governance smarter but also more humane, responsive, and aligned with the evolving needs of the public it serves.
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