Oncology is a medical specialty with fast changing research, difficult treatment plans, and a lot of data to manage. Doctors have to work with a large amount of clinical information, including genetic markers, imaging results, lab reports, and updated treatment methods. This work takes a lot of time and can be inefficient.
Clinical guidelines, like those from ASCO, give important advice based on clinical studies and expert opinions. But, using these guidelines regularly during patient care is hard with normal electronic health record (EHR) systems. This leads to inconsistent use and takes more time for doctors, which can affect how well patients do.
Artificial intelligence can help by putting these guidelines right into the clinical workflow. It gives doctors real-time recommendations based on the latest evidence. This helps oncologists get the newest information, matched to each patient’s data, and supports shared decision-making and personalized treatments.
Tempus AI, Inc. made Tempus One, an AI clinical helper built directly into EHR systems. It supports personalized cancer treatment. One key feature is that it includes ASCO clinical practice guidelines to give doctors the latest, evidence-based treatment choices for each cancer patient.
Tempus One links to EHRs to look at a large amount of patient data, like clinical history, molecular info, images, and biomarker data collected from millions of patients. By combining these different kinds of data, the AI assistant offers several workflow improvements:
Ryan Fukushima, COO of Tempus, says this system changes the usual doctor workflow. Instead of doctors spending a lot of time looking for information, the AI gives useful insights fast. The platform has helped build more than 1,000 custom AI agents for internal use. There are plans to expand this to more partner practices. These AI agents follow the institution’s rules and workflows while meeting special needs.
Medical administrators in the U.S. will see the benefits of such AI tools. They can save time spent on documentation and improve care quality by supporting decisions with evidence.
Another new tool for precise cancer care is using multimodal AI frameworks. These combine imaging data with clinical and pathology results. For example, a study by Liyang Wang showed a framework using radiomics, deep learning, and large language model (LLM) agents to improve treatment advice for hepatocellular carcinoma (HCC), a tough liver cancer.
This framework uses a special GhostNet deep learning design with features like dilated convolutions and attention modules. These help detect key pathological markers in MRI scans, such as microvascular invasion and tumor type. Blending image features with deep learning results makes predictions more accurate, reaching scores close to 0.89 for major markers.
Six AI agents, including DeepSeek-R1, GPT-4, and Med-PaLM 2, look at this multimodal data to create personal treatment plans. Hepatobiliary surgeons checked the AI’s treatment suggestions and found them clinically useful. This shows AI can help doctors make better decisions by combining complex patient data that is not usually linked in clinics.
For practice leaders, these models offer a way to improve diagnosis and use resources better by matching treatments closely to each patient’s disease.
AI is useful not only for treatment choices but also for automating clinical work and reducing paperwork in medical practices. Oncology centers in the U.S. handle a lot of documentation, approvals, and clinical trial matching, which takes a lot of doctor time.
Tools like Tempus One’s Agent Builder GenAI help by creating AI agents that follow an institution’s Standard Operating Procedures (SOPs). These agents automate tasks such as:
This automation cuts down on repeated work and errors from manual data entry. It lets doctors and staff spend more time with patients.
From an administration view, automation leads to:
IT managers must make sure AI agents work smoothly with current EHR systems and workflows. They also need to keep data safe and private following U.S. rules like HIPAA. Designing AI tools that are easy to use helps doctors accept and use them in daily work.
AI-powered Clinical Decision Support Systems (CDSS) have changed how doctors handle diagnosis and treatment plans. Using machine learning, natural language processing, and deep learning, AI-CDSS analyze large amounts of data to provide patient-specific advice.
In oncology, AI-CDSS help with:
These systems solve the problem of complex and split oncology data by giving doctors timely and useful information. Still, challenges include making sure the AI’s reasoning is clear, reducing bias in algorithms, and fitting AI smoothly into daily work without causing trouble.
Teamwork among doctors, data experts, and IT staff is key to making AI-CDSS work well. With more research and improvements, these systems can become standard parts of cancer care, helping patients and lowering doctor workloads.
A main tool for personal cancer treatment is having large, detailed healthcare datasets. Tempus AI uses one of the biggest multimodal data collections, including clinical, molecular, and imaging data from millions of patients. Using this big data helps AI find patterns that support precise treatment.
Precision oncology tries to tailor treatments to the molecular and clinical details of each tumor instead of using one-size-fits-all methods. AI helps by quickly combining data from genomics, radiology, and pathology to guide the best treatment for each patient.
Healthcare administrators and IT teams managing cancer clinics in the U.S. need systems to safely store and combine these different data types. Cloud platforms that support AI help spread these technologies, making precise cancer care available outside research hospitals.
Administrators and IT managers thinking about using AI to support personalized cancer treatment should consider several points:
By paying attention to these factors, oncology clinics in the U.S. can use AI with clinical guidelines to make work easier and improve patient care.
Using clinical guidelines inside AI tools is an important step in managing cancer care. Systems like Tempus One and multimodal AI models show how artificial intelligence can help personalize treatment and reduce paperwork. For administrators, owners, and IT managers in U.S. healthcare, adopting these tools needs careful planning but can bring clear benefits in care quality and running efficiency.
Tempus One is an AI-enabled clinical assistant integrated directly into electronic health record (EHR) systems. It supports clinicians by querying patient data, providing AI-driven insights, and streamlining workflow across the clinical care process, particularly in oncology and other specialties.
Tempus One incorporates ASCO’s clinical practice guidelines, providing physicians with evidence-based treatment and care recommendations. This ensures up-to-date, personalized patient care by embedding authoritative guidelines directly into the AI assistant’s functionality.
During appointments, Tempus One transcribes conversations, takes intelligent notes, and highlights critical information. This enables physicians to concentrate on patient care while the AI manages documentation and relevant clinical details in real-time.
Post-appointment, Tempus One assists with documentation, treatment planning aligned with updated guidelines, preparing prior authorizations, and matching patients to relevant clinical trials, thereby enhancing efficiency and clinical decision-making.
Agent Builder is a generative AI tool used to create custom AI agents tailored to provider needs. These agents automate workflow tasks such as generating patient overviews and notes, integrating with institutional SOPs and data for seamless EHR inclusion.
Tempus One summarizes patient history, treatment journey, and biomarker statuses, ensuring physicians arrive well-informed and ready to make personalized clinical decisions during appointments.
Tempus One aggregates real-time clinical, molecular, and imaging data from millions of patients into an accessible format. This rich data integration enhances AI-driven insights to support precision medicine and individualized treatment plans.
By automating documentation, authorizations, note-taking, and clinical trial matching, Tempus One reduces time spent on administrative tasks, relieving physician workload and improving care efficiency.
Tempus One targets rising healthcare costs, clinical complexity, and fragmented data systems by delivering actionable real-time insights and automating workflows to boost physician productivity and patient care quality.
Tempus uses one of the world’s largest multimodal data libraries combined with AI to provide physicians with precision medicine tools that learn and improve over time, enhancing personalized treatment and clinical research outcomes.
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