Artificial intelligence is starting to change how doctors find diseases, choose treatments, and care for patients. AI tools are already helping read scans, spot cancer earlier, and support medical decisions.
As these AI-driven treatments move closer to everyday use, many people are asking the same question: where will patients get access to these new tools first?
New medical technology rarely spreads evenly across the country. Some states tend to see new treatments years before others. This usually happens in places with large hospitals, strong research centers, and a high number of doctors.
To better understand where AI-driven treatments are most likely to appear first, we analyzed public healthcare and research data and created a state-by-state ranking.
This analysis was created by Kivo, a cloud-based, Part 11-compliant document, content, and process management platform designed to help life sciences teams — including regulatory, clinical, and quality functions — collaborate efficiently and stay inspection-ready throughout drug and device development.
Ranking Premise
Access to emerging treatments is typically connected to both general healthcare infrastructure and the presence of major research centers.
People are most likely to have early access when their state has:
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More doctors per capita than other states.
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More major research centers that test new medical technology.
This is why The District of Columbia ranks first overall in our study. It has far more doctors per person than any state and sits near many national research and policy institutions. Several Northeastern states also rank near the top, reflecting the region’s long history of medical research and teaching hospitals.
Large states like California and New York still rank highly, but their large populations reduce per-person access compared with smaller, research-heavy states.
Methodology
To identify which states are most likely to see early access to AI-driven medical treatments, we created a Healthcare AI Readiness Index using publicly available data.
Early access was defined using measurable factors that reflect where advanced medical technologies are usually introduced first.
Physician workforce capacity was measured using the number of active physicians per 100,000 residents in each state. These data were sourced from Becker’s Hospital Review, which publishes state-by-state rankings of active physicians per capita based on federal workforce data.
Physician totals were cross-checked using the Kaiser Family Foundation’s State Health Facts tool, which reports total active physicians by state.
Research infrastructure was measured using the number of National Cancer Institute-designated cancer centers in each state. These centers are among the most advanced research hospitals in the country and often serve as early sites for new medical technologies. Cancer center data were sourced from the National Cancer Institute’s official directory and verified using the NCI Cancer Centers Program directory.
Population data used for per-capita calculations came from the Federal Reserve Bank of St. Louis, which publishes state population estimates based on U.S. Census data, provided through the FRED database.
Physician density and cancer center density were converted into per-capita values and standardized so states could be compared fairly. Each state’s standardized scores were combined with equal weight to produce a single Healthcare AI Readiness Score. States were then ranked from highest to lowest based on this score.
This analysis does not predict specific treatment approvals or commercial launch plans. It also does not include private pilot programs that are not connected to major research hospitals. Instead, it reflects how healthcare capacity and research presence have historically shaped where new medical technologies appear first.
Chart: AI Access Scores & Ranking By State
The chart below shows how each state ranks based on healthcare workforce capacity and advanced research infrastructure. Higher scores suggest states that are better positioned to see early access to AI-driven treatments.
| Rank | State | Population | Physicians per 100k | Cancer Centers | Centers per 1M | Score |
|---|---|---|---|---|---|---|
| 1 | District of Columbia | 702250 | 925.7 | 1 | 1.424 | 10.735 |
| 2 | Maine | 1405012 | 330.7 | 1 | 0.712 | 2.243 |
| 3 | New Hampshire | 1409032 | 324.1 | 1 | 0.710 | 2.173 |
| 4 | Hawaii | 1446146 | 318.8 | 1 | 0.691 | 2.049 |
| 5 | Massachusetts | 7136171 | 480.2 | 2 | 0.280 | 1.927 |
| 6 | New York | 19867248 | 398.2 | 8 | 0.403 | 1.642 |
| 7 | Maryland | 6263220 | 397.7 | 2 | 0.319 | 1.303 |
| 8 | Pennsylvania | 13078751 | 337.2 | 5 | 0.382 | 0.983 |
| 9 | Connecticut | 3675069 | 378.6 | 1 | 0.272 | 0.932 |
| 10 | Minnesota | 5793151 | 323.2 | 2 | 0.345 | 0.701 |
| 11 | Rhode Island | 1112308 | 375.1 | 0 | 0.000 | 0.631 |
| 12 | Vermont | 648493 | 389.9 | 0 | 0.000 | 0.629 |
| 13 | New Jersey | 9500851 | 305.5 | 1 | 0.105 | 0.059 |
| 14 | Colorado | 5957493 | 306.4 | 1 | 0.168 | 0.032 |
| 15 | Michigan | 10140459 | 307.8 | 2 | 0.197 | -0.004 |
| 16 | Wisconsin | 5960975 | 284.4 | 1 | 0.168 | -0.091 |
| 17 | California | 39431263 | 303.6 | 10 | 0.254 | -0.113 |
| 18 | Illinois | 12710158 | 304.0 | 2 | 0.157 | -0.216 |
| 19 | Washington | 7958180 | 292.5 | 1 | 0.126 | -0.323 |
| 20 | Ohio | 11883304 | 312.7 | 2 | 0.168 | -0.325 |
| 21 | Virginia | 8811195 | 275.4 | 2 | 0.227 | -0.357 |
| 22 | Oregon | 4272371 | 316.5 | 1 | 0.234 | -0.370 |
| 23 | Missouri | 6245466 | 311.2 | 1 | 0.160 | -0.392 |
| 24 | Florida | 23372215 | 279.6 | 4 | 0.171 | -0.451 |
| 25 | Alaska | 740133 | 293.4 | 0 | 0.000 | -0.456 |
| 26 | New Mexico | 2130256 | 257.0 | 1 | 0.469 | -0.463 |
| 27 | North Carolina | 11046024 | 271.4 | 3 | 0.272 | -0.470 |
| 28 | Tennessee | 7227750 | 261.1 | 2 | 0.277 | -0.504 |
| 29 | Delaware | 1051917 | 299.4 | 0 | 0.000 | -0.514 |
| 30 | Louisiana | 4597740 | 284.0 | 0 | 0.000 | -0.522 |
| 31 | Arizona | 7582384 | 257.4 | 2 | 0.264 | -0.554 |
| 32 | South Carolina | 5478831 | 244.5 | 1 | 0.183 | -0.564 |
| 33 | Indiana | 6924275 | 240.1 | 0 | 0.000 | -0.586 |
| 34 | Texas | 31290831 | 235.1 | 4 | 0.128 | -0.625 |
| 35 | Utah | 3503613 | 231.1 | 1 | 0.285 | -0.648 |
| 36 | Kansas | 2970606 | 240.8 | 1 | 0.337 | -0.652 |
| 37 | Georgia | 11180878 | 241.1 | 1 | 0.089 | -0.684 |
| 38 | Kentucky | 4588372 | 242.6 | 1 | 0.218 | -0.691 |
| 39 | Iowa | 3241488 | 227.5 | 1 | 0.309 | -0.736 |
| 40 | Nebraska | 2005465 | 253.8 | 1 | 0.499 | -0.742 |
| 41 | Montana | 1137233 | 262.5 | 0 | 0.000 | -0.755 |
| 42 | North Dakota | 796568 | 246.5 | 0 | 0.000 | -0.802 |
| 43 | Alabama | 5157699 | 224.4 | 1 | 0.194 | -0.810 |
| 44 | Arkansas | 3088354 | 224.6 | 0 | 0.000 | -0.823 |
| 45 | Oklahoma | 4095393 | 210.4 | 1 | 0.244 | -0.889 |
| 46 | South Dakota | 924669 | 251.5 | 0 | 0.000 | -0.891 |
| 47 | Mississippi | 2943045 | 203.7 | 0 | 0.000 | -1.032 |
| 48 | Nevada | 3267467 | 217.8 | 0 | 0.000 | -0.990 |
| 49 | Wyoming | 587618 | 217.6 | 0 | 0.000 | -0.991 |
| 50 | Idaho | 2001619 | 192.0 | 0 | 0.000 | -1.066 |
| 51 | West Virginia | 1769979 | 286.7 | 0 | 0.000 | -0.513 |
Why Healthcare Infrastructure Matters for AI
AI-driven treatments often need more support than traditional care. Many rely on large data systems, trained specialists, and ongoing testing after they are introduced. Because of this, new AI tools are usually first used in large health systems that already handle complex care.
Academic medical centers and federally recognized cancer centers often lead this work. These hospitals take part in clinical trials and early testing programs. States with more doctors per person also have an easier time adding new tools without placing extra stress on the healthcare system.
Why Geography Still Shapes Access
The rankings highlight something healthcare leaders have known for decades. Geography still plays a major role in who sees new medical technology first. States in the Northeast and Mid-Atlantic consistently rise to the top because they concentrate academic hospitals, research funding, and specialty physicians within a relatively small population base. This creates a dense testing ground where AI tools can be piloted, evaluated, and refined before broader rollout.
In contrast, many Southern and Mountain West states face a different reality. Even when population growth is strong, research hospitals and specialist capacity often lag behind demand. That makes it harder to introduce AI-driven diagnostics or treatment tools without straining existing systems. These regions are not falling behind because of a lack of innovation. They are constrained by workforce shortages, fewer federally designated research centers, and longer distances between patients and advanced care sites.
Large coastal states show a more complex pattern. States with world-class research institutions still rank well overall, but their size dilutes per-capita access. Patients may have access to cutting-edge AI tools in major metro areas, while rural or inland regions wait much longer. The result is a patchwork of availability within the same state, shaped by where research hospitals and specialist networks are concentrated.
Over time, many of these gaps will narrow as AI tools become easier to deploy and less dependent on large academic centers. Early access, however, continues to favor regions that already combine physician density with strong research infrastructure. For patients and policymakers alike, the data underscores a simple reality. Where you live still influences how quickly medical innovation reaches your exam room.
What This Means for Patients & Policymakers
For patients, these rankings help explain why some treatments may be available nearby while others are not. Early access often depends on where people live, not just on medical need.
For policymakers, the results highlight the role of investment. Training more doctors, supporting research hospitals, and improving health data systems can all help states become better positioned for future medical innovation.
