{"id":41385,"date":"2026-11-13T12:07:42","date_gmt":"2026-11-13T12:07:42","guid":{"rendered":"https:\/\/itbusinesstoday.com\/?p=41385"},"modified":"2026-11-13T12:07:42","modified_gmt":"2026-11-13T12:07:42","slug":"ai-powered-clinical-trial-matching-improving-patient-recruitment-and-research-outcomes","status":"publish","type":"post","link":"https:\/\/itbusinesstoday.com\/ja\/health-tech\/ai-powered-clinical-trial-matching-improving-patient-recruitment-and-research-outcomes\/","title":{"rendered":"AI\u3092\u6d3b\u7528\u3057\u305f\u81e8\u5e8a\u8a66\u9a13\u306e\u30de\u30c3\u30c1\u30f3\u30b0\uff1a\u88ab\u9a13\u8005\u306e\u52df\u96c6\u3068\u7814\u7a76\u6210\u679c\u306e\u5411\u4e0a"},"content":{"rendered":"

\u6709\u671b\u306a\u533b\u85ac\u54c1\u3067\u3042\u3063\u3066\u3082\u3001\u958b\u767a\u306b\u306f\u6570\u5e74\u3092\u8981\u3059\u308b\u3053\u3068\u304c\u3042\u308b\u304c\u3001\u7814\u7a76\u8005\u304c\u5341\u5206\u306a\u6570\u306e\u9069\u683c\u60a3\u8005\u3092\u898b\u3064\u3051\u3089\u308c\u306a\u3044\u305f\u3081\u3001\u81e8\u5e8a\u8a66\u9a13\u306e\u9032\u5c55\u304c\u6ede\u308b\u3053\u3068\u304c\u3042\u308b\u3002\u3053\u308c\u306f\u3001\u88fd\u85ac\u696d\u754c\u3084\u533b\u7642\u6a5f\u5668\u696d\u754c\u304c\u4f9d\u7136\u3068\u3057\u3066\u76f4\u9762\u3057\u3066\u3044\u308b\u554f\u984c\u3060\u3002 \u5f93\u6765\u306e\u88ab\u9a13\u8005\u52df\u96c6\u306f\u3001\u75c5\u9662\u304b\u3089\u306e\u7d39\u4ecb\u3001\u624b\u4f5c\u696d\u306b\u3088\u308b\u30ab\u30eb\u30c6\u306e\u78ba\u8a8d\u3001\u3042\u3089\u304b\u3058\u3081\u5b9a\u3081\u3089\u308c\u305f\u30b9\u30af\u30ea\u30fc\u30cb\u30f3\u30b0\u57fa\u6e96\u306b\u5927\u304d\u304f\u4f9d\u5b58\u3057\u3066\u3044\u308b\u3002\u305d\u306e\u7d50\u679c\u3001\u30ab\u30eb\u30c6\u306b\u3059\u3067\u306b\u9069\u5207\u306a\u81e8\u5e8a\u60c5\u5831\u304c\u8a18\u8f09\u3055\u308c\u3066\u3044\u308b\u306b\u3082\u304b\u304b\u308f\u3089\u305a\u3001\u9069\u683c\u306a\u60a3\u8005\u304c\u898b\u904e\u3054\u3055\u308c\u3066\u3057\u307e\u3046\u3053\u3068\u304c\u3042\u308b\u3002AI\u3092\u6d3b\u7528\u3057\u305f\u81e8\u5e8a\u8a66\u9a13\u306e\u30de\u30c3\u30c1\u30f3\u30b0\u6280\u8853\u306f\u3001\u7814\u7a76\u8005\u304c\u3053\u306e\u8ab2\u984c\u306b\u53d6\u308a\u7d44\u3080\u65b9\u6cd5\u3092\u5909\u3048\u3064\u3064\u3042\u308b\u3002\u3057\u304b\u3057\u3001\u305d\u306e\u771f\u306e\u4fa1\u5024\u306f\u3001\u60a3\u8005\u30c7\u30fc\u30bf\u3092\u3044\u304b\u306b\u6b63\u78ba\u306b\u89e3\u91c8\u3057\u3001\u9069\u683c\u6027\u3092\u8a55\u4fa1\u3057\u3001\u81e8\u5e8a\u4e0a\u306e\u610f\u601d\u6c7a\u5b9a\u3092\u652f\u63f4\u3067\u304d\u308b\u304b\u306b\u304b\u304b\u3063\u3066\u3044\u308b\u3002.<\/p>

AI\u306b\u3088\u308b\u81e8\u5e8a\u8a66\u9a13\u306e\u30de\u30c3\u30c1\u30f3\u30b0\u304c\u3001\u5b9f\u969b\u306b\u306f\u3069\u306e\u3088\u3046\u306b\u6a5f\u80fd\u3057\u3066\u3044\u308b\u306e\u304b<\/h3>

\"Traditional patient screening largely depends on Boolean searches and predefined database queries. Researchers enter specific terms, diagnosis codes or combinations of medical conditions to identify potential candidates. However, this approach has a major limitation. It can find records containing the right keywords without understanding the patient’s complete medical history. A reference to chemotherapy, for instance, does not establish when treatment occurred or whether it makes someone ineligible for a particular study.<\/p>

AI-powered clinical trial matching takes a more contextual approach. Natural language processing (NLP), Named Entity Recognition (NER) and machine learning help systems identify medical conditions, treatments, medications and their relationships within clinical documents. Microsoft’s Text Analytics for Health, for example, processes clinical notes and trial protocols using entity recognition, relation extraction, assertion detection and entity linking. This allows systems to distinguish between a confirmed diagnosis, a ruled-out condition and a historical treatment record. Microsoft’s<\/a> \u6280\u8853\u6587\u66f8\u3067\u306f\u3001\u3053\u308c\u3089\u306e\u6a5f\u80fd\u306b\u3064\u3044\u3066\u89e3\u8aac\u3057\u3066\u3044\u308b\u3002.<\/p>

More importantly, modern systems can examine information beyond structured ICD-10 codes. Pathology reports, physician notes, genomic findings and imaging summaries may contain details that standard database searches overlook. Large Language Models (LLMs) can interpret this context, while retrieval-augmented generation (RAG) retrieves relevant source information before generating an assessment. This helps ground the output in actual clinical records rather than relying entirely on the model’s generated response.<\/p>

Google’s BigQuery<\/a> architecture provides another useful example. Its clinical research scenario uses embedding generation, natural-language AI.SEARCH and hybrid search across thousands of clinical trial documents. By combining semantic retrieval with exact keyword matching, the system can identify related medical concepts while retaining precision for technical identifiers. Google’s August 2026 publication describes this approach.<\/p>

The next step is eligibility assessment. Consider a trial that excludes patients who received chemotherapy within the previous six months. The system must examine treatment history, identify relevant dates and compare them against the protocol’s requirements. It can then rank potential candidates according to their available evidence and match probability. However, a high match score should never be treated as confirmed eligibility. Missing information and uncertain clinical findings still require human review.<\/p>

\u63a1\u7528\u30b9\u30d4\u30fc\u30c9\u3068\u7814\u7a76\u6210\u679c\u306b\u4e0e\u3048\u308b\u6e2c\u5b9a\u53ef\u80fd\u306a\u5f71\u97ff<\/h3>

\u5f93\u6765\u306e\u88ab\u9a13\u8005\u52df\u96c6\u30d7\u30ed\u30bb\u30b9\u306f\u3001\u591a\u304f\u306e\u5834\u5408\u3001\u81e8\u5e8a\u8a66\u9a13\u5b9f\u65bd\u65bd\u8a2d\u304c\u30d7\u30ed\u30c8\u30b3\u30eb\u3092\u53d7\u3051\u53d6\u3063\u305f\u5f8c\u306b\u59cb\u307e\u308b\u3002\u305d\u306e\u5f8c\u3001\u7814\u7a76\u30b3\u30fc\u30c7\u30a3\u30cd\u30fc\u30bf\u30fc\u304c\u75c5\u9662\u306e\u8a18\u9332\u3092\u691c\u7d22\u3057\u3001\u533b\u5e2b\u306b\u9023\u7d61\u3092\u53d6\u308a\u3001\u60a3\u8005\u306e\u75c5\u6b74\u3092\u624b\u4f5c\u696d\u3067\u78ba\u8a8d\u3059\u308b\u3002\u3053\u306e\u305f\u3081\u3001\u88ab\u9a13\u8005\u52df\u96c6\u306f\u6642\u9593\u304c\u304b\u304b\u308a\u3001\u500b\u3005\u306e\u7814\u7a76\u30c1\u30fc\u30e0\u306e\u5bfe\u5fdc\u80fd\u529b\u306b\u5927\u304d\u304f\u5de6\u53f3\u3055\u308c\u308b\u3002AI\u3092\u6d3b\u7528\u3057\u305f\u81e8\u5e8a\u8a66\u9a13\u30de\u30c3\u30c1\u30f3\u30b0\u306f\u3001\u7814\u7a76\u8005\u304c\u88ab\u9a13\u8005\u3078\u306e\u50cd\u304d\u304b\u3051\u3092\u59cb\u3081\u308b\u524d\u306b\u3001\u65e2\u5b58\u306e\u81e8\u5e8a\u30c7\u30fc\u30bf\u304b\u3089\u6f5c\u5728\u7684\u306a\u60a3\u8005\u30b3\u30db\u30fc\u30c8\u3092\u7279\u5b9a\u3059\u308b\u3053\u3068\u3067\u3001\u3053\u306e\u30d7\u30ed\u30bb\u30b9\u3092\u9006\u8ee2\u3055\u305b\u308b\u6a5f\u4f1a\u3092\u751f\u307f\u51fa\u3059\u3002.<\/p>

\u3057\u304b\u3057\u3001\u60a3\u8005\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u898f\u6a21\u304c\u3001\u5fc5\u305a\u3057\u3082\u88ab\u9a13\u8005\u52df\u96c6\u306b\u304a\u3051\u308b\u6709\u7528\u6027\u3092\u6c7a\u5b9a\u3065\u3051\u308b\u308f\u3051\u3067\u306f\u306a\u3044\u3002. AWS<\/a> shared an example involving Novo Nordisk, where researchers analysed 1,175 tokenised trial participants across four real-world data categories. In one category, 11% of participants had some relevant data available, but only 1% had records that were completely usable for the variables being examined. The finding exposed a significant gap between data coverage and data usability during trial feasibility assessment. AWS’s October 2026 clinical trials report discusses the example.<\/p>

This distinction matters because identifying a patient is only the beginning. Researchers need sufficiently complete records to establish whether that patient meets the protocol’s requirements. Consequently, AI matching must account for data quality alongside recruitment speed.<\/p>

\u3053\u306e\u6280\u8853\u306f\u3001\u3088\u308a\u4ee3\u8868\u7684\u306a\u88ab\u9a13\u8005\u306e\u52df\u96c6\u306b\u3082\u5bc4\u4e0e\u3057\u5f97\u308b\u3002\u5f93\u6765\u306e\u7d39\u4ecb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3067\u306f\u3001\u5927\u898f\u6a21\u306a\u5927\u5b66\u75c5\u9662\u3092\u53d7\u8a3a\u3059\u308b\u60a3\u8005\u3084\u3001\u3059\u3067\u306b\u5c02\u9580\u533b\u7642\u3078\u306e\u30a2\u30af\u30bb\u30b9\u3092\u6301\u3063\u3066\u3044\u308b\u60a3\u8005\u304c\u512a\u5148\u3055\u308c\u304c\u3061\u3060\u3002AI\u3092\u6d3b\u7528\u3059\u308c\u3070\u3001\u53c2\u52a0\u3057\u3066\u3044\u308b\u5730\u57df\u8a3a\u7642\u6240\u3084\u3055\u307e\u3056\u307e\u306a\u533b\u7642\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u5168\u4f53\u304b\u3089\u9069\u683c\u306a\u500b\u4eba\u3092\u7279\u5b9a\u3059\u308b\u3053\u3068\u3067\u3001\u5bfe\u8c61\u7bc4\u56f2\u3092\u5e83\u3052\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3002\u3068\u306f\u3044\u3048\u3001\u305d\u308c\u306b\u3088\u3063\u3066\u7d39\u4ecb\u30d0\u30a4\u30a2\u30b9\u304c\u81ea\u52d5\u7684\u306b\u89e3\u6d88\u3055\u308c\u308b\u308f\u3051\u3067\u306f\u306a\u3044\u3002 \u57fa\u790e\u3068\u306a\u308b\u8a18\u9332\u306b\u53cd\u6620\u3055\u308c\u3066\u3044\u308b\u60a3\u8005\u5c64\u3001\u691c\u67fb\u3078\u306e\u30a2\u30af\u30bb\u30b9\u72b6\u6cc1\u3001\u304a\u3088\u3073\u8a18\u9332\u306e\u8cea\u306f\u3001\u4f9d\u7136\u3068\u3057\u3066\u30b7\u30b9\u30c6\u30e0\u304c\u8ab0\u3092\u7279\u5b9a\u3059\u308b\u304b\u306b\u5f71\u97ff\u3092\u53ca\u307c\u3059\u3002.<\/p>

Similarly, researchers can use historical patient data to examine whether exclusion criteria are unnecessarily restrictive before a trial begins. If particular requirements eliminate large portions of otherwise relevant patient cohorts, trial designers can review whether those restrictions are scientifically justified. This can help reduce avoidable screening failures and improve recruitment feasibility. However, better matching alone cannot guarantee patient retention. Trial complexity, treatment burden and the patient’s ability to continue participating remain important considerations.<\/p>

\u5b9f\u74b0\u5883\u3067\u306e\u5c0e\u5165\u3068\u81e8\u5e8a\u30ef\u30fc\u30af\u30d5\u30ed\u30fc\u3078\u306e\u7d71\u5408<\/h3>

\u305f\u3068\u3048\u6b63\u78ba\u306a\u7167\u5408\u30b7\u30b9\u30c6\u30e0\u3067\u3042\u3063\u3066\u3082\u3001\u305d\u308c\u3092\u5229\u7528\u3059\u308b\u3053\u3068\u304c\u60f3\u5b9a\u3055\u308c\u308b\u75c5\u9662\u3084\u81e8\u5e8a\u30c1\u30fc\u30e0\u3068\u306f\u5207\u308a\u96e2\u3057\u3066\u904b\u7528\u3055\u308c\u3066\u306f\u3001\u305d\u306e\u6a5f\u80fd\u306f\u5341\u5206\u306b\u767a\u63ee\u3067\u304d\u306a\u3044\u3002\u60a3\u8005\u8a18\u9332\u306f\u3001\u3055\u307e\u3056\u307e\u306a\u90e8\u7f72\u3084\u96fb\u5b50\u30ab\u30eb\u30c6\uff08EHR\uff09\u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0\u3001\u5c02\u9580\u30b7\u30b9\u30c6\u30e0\u306b\u5206\u6563\u3057\u3066\u7ba1\u7406\u3055\u308c\u3066\u3044\u308b\u3002\u3057\u305f\u304c\u3063\u3066\u3001\u76f8\u4e92\u904b\u7528\u6027\u306f\u3001\u5c0e\u5165\u3092\u6210\u529f\u3055\u305b\u308b\u305f\u3081\u306e\u4e2d\u5fc3\u7684\u306a\u8981\u4ef6\u3068\u306a\u308b\u3002.<\/p>

2026\u5e749\u6708\u306b\u516c\u8868\u3055\u308c\u305f\u3001\u30de\u30a4\u30af\u30ed\u30bd\u30d5\u30c8\u304c\u59d4\u8a17\u3057\u305fOnePoll\u306e\u8abf\u67fb\u306b\u3088\u308b\u3068\u3001 97%<\/a> of surveyed healthcare leaders reported that data silos already affect their ability to deliver timely care. The research covered 500 healthcare decision-makers across seven countries. Although this finding concerns healthcare more broadly, it highlights the data fragmentation that clinical research systems must address. Microsoft’s healthcare data research provides the findings.<\/p>

HL7 FHIR\u3084SMART on FHIR\u3068\u3044\u3063\u305f\u6a19\u6e96\u898f\u683c\u306f\u3001\u533b\u7642\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3068\u65e2\u5b58\u306e\u81e8\u5e8a\u30b7\u30b9\u30c6\u30e0\u3092\u9023\u643a\u3055\u305b\u308b\u306e\u306b\u5f79\u7acb\u3064\u3002\u7814\u7a76\u8005\u304c\u8907\u6570\u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u884c\u304d\u6765\u3059\u308b\u5fc5\u8981\u304c\u306a\u304f\u3001\u7d71\u5408\u3055\u308c\u305f\u30bd\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u306b\u3088\u308a\u3001\u627f\u8a8d\u3055\u308c\u305f\u81e8\u5e8a\u533b\u306e\u30ef\u30fc\u30af\u30d5\u30ed\u30fc\u5185\u3067\u95a2\u9023\u3059\u308b\u81e8\u5e8a\u8a66\u9a13\u306e\u6a5f\u4f1a\u3092\u63d0\u793a\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3002\u305f\u3060\u3057\u3001\u7d71\u5408\u306e\u8a2d\u8a08\u306b\u306f\u7d30\u5fc3\u306e\u6ce8\u610f\u3092\u6255\u3046\u5fc5\u8981\u304c\u3042\u308b\u3002 \u30a2\u30e9\u30fc\u30c8\u304c\u591a\u3059\u304e\u308b\u3068\u8a3a\u5bdf\u306e\u59a8\u3052\u3068\u306a\u308a\u3001\u30a2\u30e9\u30fc\u30c8\u75b2\u52b4\u3092\u5f15\u304d\u8d77\u3053\u3057\u3001\u81e8\u5e8a\u533b\u304c\u95a2\u9023\u6027\u306e\u9ad8\u3044\u63a8\u5968\u4e8b\u9805\u3055\u3048\u3082\u7121\u8996\u3057\u3066\u3057\u307e\u3046\u539f\u56e0\u3068\u306a\u308b\u3002.<\/p>

\u3057\u305f\u304c\u3063\u3066\u3001AI\u3092\u6d3b\u7528\u3057\u305f\u81e8\u5e8a\u8a66\u9a13\u306e\u30de\u30c3\u30c1\u30f3\u30b0\u306f\u3001\u81e8\u5e8a\u610f\u601d\u6c7a\u5b9a\u652f\u63f4\u306e\u300c\u30b3\u30d1\u30a4\u30ed\u30c3\u30c8\u300d\u3068\u3057\u3066\u6a5f\u80fd\u3059\u3079\u304d\u3067\u3042\u308b\u3002. AWS<\/a> illustrates this through an architecture involving three specialised agents. One handles preliminary screening, another evaluates detailed eligibility criteria, and a third supports site and enrollment logistics. The system produces Eligible, Ineligible or Requires Review recommendations, supported by criterion-level evidence, confidence scores and citations to patient records. Clinicians retain final decision-making authority, while the workflow records actions through an audit trail. AWS’s August 2026 architecture explains the model.<\/p>

Consider an oncology centre treating newly diagnosed non-small cell lung cancer (NSCLC) patients. During a tumour board review, the matching system could examine each patient’s diagnosis, disease stage, genomic findings and treatment history. It could then compare these records against biomarker-specific clinical trial protocols and flag relevant studies. The Clinical Research Coordinator (CRC) would review the supporting records, identify missing information and check whether the patient meets the preliminary requirements. Finally, the Principal Investigator (PI) would assess clinical suitability and oversee the next steps. The patient would only enter the study after the required consent and eligibility procedures.<\/p>

\u3053\u3053\u3067 \u30c6\u30af\u30ce\u30ed\u30b8\u30fc<\/a> \u6709\u7528\u3068\u306a\u308b\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u60a3\u8005\u30b1\u30a2\u3092\u62c5\u5f53\u3059\u308b\u8005\u304c\u81e8\u5e8a\u7684\u5224\u65ad\u3092\u4e0b\u3057\u3064\u3064\u3001\u6709\u671b\u306a\u5019\u88dc\u8005\u3092\u63a2\u3059\u624b\u9593\u3092\u8efd\u6e1b\u3067\u304d\u308b\u3002.<\/p>

\u4fe1\u983c\u3001\u30ac\u30d0\u30ca\u30f3\u30b9\u3001\u304a\u3088\u3073\u898f\u5236\u4e0a\u306e\u30ac\u30fc\u30c9\u30ec\u30fc\u30eb<\/h3>

\"Governance\"\u8a3a\u7642\u8a18\u9332\u306b\u306f\u3001\u60a3\u8005\u304c\u533b\u7642\u63d0\u4f9b\u8005\u306b\u958b\u793a\u3059\u308b\u60c5\u5831\u306e\u4e2d\u3067\u3082\u7279\u306b\u6a5f\u5bc6\u6027\u306e\u9ad8\u3044\u3082\u306e\u304c\u542b\u307e\u308c\u3066\u3044\u308b\u3002\u3057\u305f\u304c\u3063\u3066\u3001AI\u3092\u6d3b\u7528\u3057\u305f\u81e8\u5e8a\u8a66\u9a13\u306e\u30de\u30c3\u30c1\u30f3\u30b0\u306f\u3001\u53b3\u683c\u306a\u30d7\u30e9\u30a4\u30d0\u30b7\u30fc\u304a\u3088\u3073\u30bb\u30ad\u30e5\u30ea\u30c6\u30a3\u7ba1\u7406\u306e\u4e0b\u3067\u904b\u7528\u3055\u308c\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044\u3002\u3053\u308c\u306b\u306f\u3001\u500b\u4eba\u8b58\u5225\u60c5\u5831\uff08PII\uff09\u306e\u4fdd\u8b77\u3001\u4fdd\u8b77\u5bfe\u8c61\u533b\u7642\u60c5\u5831\uff08PHI\uff09\u306e\u4fdd\u8b77\u3001\u304a\u3088\u3073\u60a3\u8005\u8a18\u9332\u3078\u306e\u30a2\u30af\u30bb\u30b9\u6a29\u9650\u306e\u7ba1\u7406\u304c\u542b\u307e\u308c\u308b\u3002.<\/p>

\u7c73\u56fd\u3067\u306f\u3001HIPAA\u304c\u3001\u5bfe\u8c61\u6a5f\u95a2\u304a\u3088\u3073\u305d\u306e\u696d\u52d9\u63d0\u643a\u5148\u306b\u3088\u308b\u4fdd\u8b77\u5bfe\u8c61\u5065\u5eb7\u60c5\u5831\u306e\u53d6\u308a\u6271\u3044\u65b9\u6cd5\u3092\u898f\u5b9a\u3057\u3066\u3044\u308b\u3002\u7814\u7a76\u8005\u3084\u6280\u8853\u30d7\u30ed\u30d0\u30a4\u30c0\u30fc\u306f\u3001\u3053\u3046\u3057\u305f\u30c7\u30fc\u30bf\u3092\u51e6\u7406\u3059\u308b\u969b\u306b\u9069\u5207\u306a\u4fdd\u8b77\u63aa\u7f6e\u3092\u8b1b\u3058\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044\u3002\u307e\u305f\u3001\u8b58\u5225\u53ef\u80fd\u306a\u60c5\u5831\u304c\u4e0d\u8981\u306a\u5834\u5408\u306b\u306f\u3001\u975e\u8b58\u5225\u5316\u3092\u884c\u3046\u3053\u3068\u3067\u30ea\u30b9\u30af\u3092\u4f4e\u6e1b\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u308b\u3002 \u7c73\u56fd\u4fdd\u5065\u793e\u4f1a\u798f\u7949\u7701\uff08HHS\uff09\u306f\u3001HIPAA\u306e\u4e0b\u3067\u300c\u5c02\u9580\u5bb6\u306b\u3088\u308b\u5224\u65ad\u300d\u3068\u300c\u30bb\u30fc\u30d5\u30cf\u30fc\u30d0\u30fc\u300d\u3068\u3044\u30462\u3064\u306e\u65b9\u6cd5\u3092\u8a8d\u3081\u3066\u3044\u308b\u3002\u3057\u304b\u3057\u3001\u660e\u3089\u304b\u306a\u8b58\u5225\u5b50\u3092\u524a\u9664\u3057\u305f\u304b\u3089\u3068\u3044\u3063\u3066\u3001\u518d\u8b58\u5225\u3055\u308c\u308b\u30ea\u30b9\u30af\u304c\u3059\u3079\u3066\u81ea\u52d5\u7684\u306b\u6392\u9664\u3055\u308c\u308b\u308f\u3051\u3067\u306f\u306a\u3044\u3002HHS\u306e\u975e\u8b58\u5225\u5316\u306b\u95a2\u3059\u308b\u30ac\u30a4\u30c0\u30f3\u30b9\u3067\u306f\u3001\u3053\u308c\u3089\u306e\u8981\u4ef6\u306b\u3064\u3044\u3066\u8aac\u660e\u3055\u308c\u3066\u3044\u308b\u3002.<\/p>

\u540c\u69d8\u306b\u3001GDPR\u306f\u6b27\u5dde\u9023\u5408\uff08EU\uff09\u5185\u306e\u95a2\u9023\u3059\u308b\u6cd5\u7684\u9069\u7528\u7bc4\u56f2\u306b\u304a\u3051\u308b\u500b\u4eba\u30c7\u30fc\u30bf\u306e\u51e6\u7406\u306b\u9069\u7528\u3055\u308c\u308b\u3002\u7d44\u7e54\u306f\u3001\u9069\u5207\u306a\u6cd5\u7684\u6839\u62e0\u3092\u78ba\u7acb\u3057\u3001\u6a5f\u5bc6\u60c5\u5831\u3092\u4fdd\u8b77\u3057\u3001\u9069\u7528\u3055\u308c\u308b\u30c7\u30fc\u30bf\u4e3b\u4f53\u306e\u6a29\u5229\u3092\u5c0a\u91cd\u3057\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044\u3002\u4e00\u65b9\u3001\u30bc\u30ed\u30c8\u30e9\u30b9\u30c8\u30bb\u30ad\u30e5\u30ea\u30c6\u30a3\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u306f\u3001\u5358\u306b\u75c5\u9662\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u5185\u3067\u52d5\u4f5c\u3057\u3066\u3044\u308b\u3068\u3044\u3046\u7406\u7531\u3060\u3051\u3067\u30e6\u30fc\u30b6\u30fc\u3084\u30b7\u30b9\u30c6\u30e0\u3092\u4fe1\u983c\u3067\u304d\u308b\u3082\u306e\u3068\u898b\u306a\u3059\u306e\u3067\u306f\u306a\u304f\u3001\u8a8d\u8a3c\u3092\u7fa9\u52d9\u4ed8\u3051\u308b\u3053\u3068\u3067\u3001\u30a2\u30af\u30bb\u30b9\u5236\u5fa1\u3092\u5f37\u5316\u3059\u308b\u3002.<\/p>

\u3057\u304b\u3057\u3001\u30c7\u30fc\u30bf\u30bb\u30ad\u30e5\u30ea\u30c6\u30a3\u306f\u554f\u984c\u306e\u4e00\u90e8\u306b\u3057\u304b\u5bfe\u51e6\u3057\u3066\u3044\u306a\u3044\u3002. AI\u30e2\u30c7\u30eb<\/a> \u533b\u7642\u7528\u8a9e\u3092\u8aa4\u3063\u3066\u89e3\u91c8\u3057\u305f\u308a\u3001\u91cd\u8981\u306a\u6587\u8108\u3092\u898b\u843d\u3068\u3057\u305f\u308a\u3001\u6839\u62e0\u306e\u306a\u3044\u7d50\u8ad6\u3092\u5c0e\u304d\u51fa\u3057\u305f\u308a\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u3002\u3057\u305f\u304c\u3063\u3066\u3001\u3059\u3079\u3066\u306e\u63a8\u5968\u4e8b\u9805\u306b\u306f\u8aac\u660e\u53ef\u80fd\u306a\u7d4c\u7def\u304c\u793a\u3055\u308c\u3066\u3044\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044\u3002\u7814\u7a76\u8005\u306f\u3001\u3069\u306e\u9069\u683c\u57fa\u6e96\u304c\u4e00\u81f4\u3092\u5f15\u304d\u8d77\u3053\u3057\u305f\u304b\u3001\u305d\u308c\u3092\u88cf\u4ed8\u3051\u308b\u6b63\u78ba\u306a\u81e8\u5e8a\u7684\u6839\u62e0\u3001\u304a\u3088\u3073\u3069\u306e\u8981\u4ef6\u304c\u4f9d\u7136\u3068\u3057\u3066\u4e0d\u78ba\u5b9f\u3067\u3042\u308b\u304b\u3092\u78ba\u8a8d\u3067\u304d\u308b\u5fc5\u8981\u304c\u3042\u308b\u3002\u3053\u308c\u306b\u3088\u308a\u3001CRC\u3084PI\u306f\u3001AI\u304c\u751f\u6210\u3057\u305f\u30b9\u30b3\u30a2\u3092\u7121\u6761\u4ef6\u306b\u53d7\u3051\u5165\u308c\u308b\u306e\u3067\u306f\u306a\u304f\u3001\u8aa4\u3063\u305f\u8a55\u4fa1\u306b\u5bfe\u3057\u3066\u7570\u8b70\u3092\u5531\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3002.<\/p>

\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u30d0\u30a4\u30a2\u30b9\u306b\u3064\u3044\u3066\u3082\u3001\u7d99\u7d9a\u7684\u306a\u6ce8\u610f\u304c\u5fc5\u8981\u3060\u3002\u4e3b\u306b\u5927\u90fd\u5e02\u306e\u75c5\u9662\u306e\u8a18\u9332\u3092\u7528\u3044\u3066\u5b66\u7fd2\u3055\u308c\u305f\u30e2\u30c7\u30eb\u306f\u3001\u5c0f\u898f\u6a21\u306a\u8a3a\u7642\u6240\u3084\u3001\u30c7\u30fc\u30bf\u304c\u5341\u5206\u306b\u53ce\u96c6\u3055\u308c\u3066\u3044\u306a\u3044\u60a3\u8005\u96c6\u56e3\u306e\u8a3a\u7642\u8a18\u9332\u3092\u51e6\u7406\u3059\u308b\u969b\u3001\u7570\u306a\u308b\u7d50\u679c\u3092\u793a\u3059\u53ef\u80fd\u6027\u304c\u3042\u308b\u3002\u3057\u305f\u304c\u3063\u3066\u3001\u7d44\u7e54\u306f\u3055\u307e\u3056\u307e\u306a\u60a3\u8005\u30b0\u30eb\u30fc\u30d7\u306b\u308f\u305f\u3063\u3066\u30e2\u30c7\u30eb\u306e\u6027\u80fd\u3092\u691c\u8a3c\u3057\u3001\u8aa4\u3063\u305f\u4e00\u81f4\u3084\u9664\u5916\u3092\u76e3\u8996\u3059\u308b\u3068\u3068\u3082\u306b\u3001\u81e8\u5e8a\u30c7\u30fc\u30bf\u306e\u5909\u5316\u306b\u4f34\u3044\u6027\u80fd\u306b\u751f\u3058\u308b\u5909\u5316\u3092\u691c\u8a3c\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u3002.<\/p>

Regulatory alignment adds another layer of accountability. The FDA’s Good Machine Learning Practice (GMLP) principles emphasise safe, effective and high-quality development across the lifecycle of AI-enabled medical devices. The EU AI Act also establishes requirements for certain high-risk AI systems, including applicable medical-device-related AI. However, not every clinical matching application automatically falls into the same regulatory category. Its intended purpose and regulatory status matter. FDA’s GMLP principles and the EU AI Act provide the relevant foundations.<\/p>

\u7d50\u5c40\u306e\u3068\u3053\u308d\u3001\u4fe1\u983c\u306f\u3001\u305d\u306e\u30b7\u30b9\u30c6\u30e0\u304c\u63a8\u5968\u5185\u5bb9\u3092\u8aac\u660e\u3067\u304d\u308b\u304b\u3001\u60a3\u8005\u60c5\u5831\u3092\u4fdd\u8b77\u3067\u304d\u308b\u304b\u3001\u305d\u3057\u3066\u8a55\u4fa1\u304c\u8aa4\u3063\u3066\u3044\u305f\u5834\u5408\u306b\u8cac\u4efb\u3092\u8ca0\u3048\u308b\u304b\u3069\u3046\u304b\u306b\u304b\u304b\u3063\u3066\u3044\u308b\u3002.<\/p>

\u3053\u3061\u3089\u3082\u304a\u8aad\u307f\u304f\u3060\u3055\u3044\uff1a UCB\u3001Veeva Vault\u3092\u6d3b\u7528\u3057\u3066CRM\u6a5f\u80fd\u3092\u62e1\u5145<\/a><\/h4>

\u7d50\u8ad6<\/h3>

AI\u3092\u6d3b\u7528\u3057\u305f\u81e8\u5e8a\u8a66\u9a13\u306e\u30de\u30c3\u30c1\u30f3\u30b0\u306f\u3001\u60a3\u8005\u60c5\u5831\u3092\u95a2\u9023\u3059\u308b\u81e8\u5e8a\u8a66\u9a13\u3068\u7d50\u3073\u3064\u3051\u308b\u3053\u3068\u3067\u3001\u81e8\u5e8a\u7814\u7a76\u306e\u5bfe\u5fdc\u529b\u3092\u9ad8\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u308b \u30ea\u30b5\u30fc\u30c1<\/a> opportunities. Yet, the industry’s biggest challenge is not simply finding more candidates. It is building systems that can distinguish a possible match from a medically suitable participant.<\/p>

\u6b21\u306e\u6bb5\u968e\u3067\u306f\u3001\u30de\u30eb\u30c1\u30e2\u30fc\u30c0\u30ebAI\u304c\u30b2\u30ce\u30df\u30af\u30b9\u3001\u30e9\u30b8\u30aa\u30df\u30af\u30b9\u3001\u304a\u3088\u3073\u7e26\u65ad\u7684\u306a\u96fb\u5b50\u30ab\u30eb\u30c6\uff08EHR\uff09\u30c7\u30fc\u30bf\u3068\u7d71\u5408\u3055\u308c\u308b\u4e00\u65b9\u3001\u5206\u6563\u578b\u81e8\u5e8a\u8a66\u9a13\u306b\u3088\u3063\u3066\u3001\u5f93\u6765\u306e\u7814\u7a76\u62e0\u70b9\u4ee5\u5916\u306e\u5834\u6240\u3067\u3082\u7814\u7a76\u3078\u306e\u30a2\u30af\u30bb\u30b9\u304c\u5e83\u304c\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u3002\u3068\u306f\u3044\u3048\u3001\u3044\u305a\u308c\u306e\u9032\u5c55\u3082\u3001\u5b8c\u5168\u306a\u8a18\u9332\u3001\u73fe\u5b9f\u7684\u306a\u30d7\u30ed\u30c8\u30b3\u30eb\u3001\u305d\u3057\u3066\u81e8\u5e8a\u4e0a\u306e\u76e3\u7763\u306e\u5fc5\u8981\u6027\u3092\u306a\u304f\u3059\u3082\u306e\u3067\u306f\u306a\u3044\u3002\u771f\u306e\u9032\u6b69\u306f\u3001\u7814\u7a76\u30c1\u30fc\u30e0\u304c\u5358\u306b\u65e2\u5b58\u306e\u30d7\u30ed\u30bb\u30b9\u3092\u8fc5\u901f\u5316\u3059\u308b\u3060\u3051\u3067\u306a\u304f\u3001AI\u3092\u6d3b\u7528\u3057\u3066\u3088\u308a\u9069\u5207\u306a\u88ab\u9a13\u8005\u52df\u96c6\u306e\u5224\u65ad\u3092\u4e0b\u305b\u308b\u3088\u3046\u306b\u306a\u3063\u305f\u3068\u304d\u306b\u5b9f\u73fe\u3059\u308b\u3060\u308d\u3046\u3002.<\/p>","protected":false},"excerpt":{"rendered":"

A promising drug can spend years in development, yet its clinical trial can struggle to move forward because researchers cannot find enough eligible patients. This is a problem the pharmaceutical and medical device industries continue to face. Traditional recruitment depends heavily on hospital referrals, manual record reviews and predefined screening criteria. As a result, eligible patients can remain unidentified even when relevant clinical information already exists in their records. AI-powered clinical trial matching is changing how researchers approach this challenge. However, its real value depends on how accurately it interprets patient data, evaluates eligibility and supports clinical decisions.How AI Clinical Trial Matching Actually Works Under the HoodTraditional patient screening largely […]<\/p>\n","protected":false},"author":6,"featured_media":41409,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"wprm-recipe-roundup-name":"","wprm-recipe-roundup-description":"","postBodyCss":"","postBodyMargin":[],"postBodyPadding":[],"postBodyBackground":{"backgroundType":"classic","gradient":""},"footnotes":""},"categories":[89,8098],"tags":[1309,16199,6546,16867,224],"ppma_author":[325],"class_list":["post-41385","post","type-post","status-publish","format-standard","has-post-thumbnail","category-health-tech","category-news-articles","tag-aws","tag-bigquery","tag-clinical-trial","tag-healthcare-applications","tag-microsoft"],"yoast_head":"\nAI Boosts Patient Matching for Clinical Trials<\/title>\n<meta name=\"description\" content=\"AI-powered matching connects patients with relevant clinical trials, helping improve recruitment and advance medical research.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/itbusinesstoday.com\/ja\/health-tech\/ai-powered-clinical-trial-matching-improving-patient-recruitment-and-research-outcomes\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI Boosts Patient Matching for Clinical Trials\" \/>\n<meta property=\"og:description\" content=\"AI-powered matching connects patients with relevant clinical trials, helping improve recruitment and advance medical research.\" \/>\n<meta property=\"og:url\" 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