AI is rapidly transforming healthcare in exam rooms, imaging labs, operating suites, billing departments, and patient smartphones. It is being used to reshape how healthcare organizations diagnose disease, streamline operations, manage patient data, and deliver care. From AI-powered documentation tools to predictive analytics, healthcare systems and organizations of all sizes are leveraging automation and machine learning to improve outcomes while simultaneously reducing administrative burdens.
There are a lot of advantages to using AI in the healthcare system, such as the ability to analyze data faster and more accurately, to offer improved access to care and support, and to reduce some of the administrative burdens associated with running a medical practice. While there are some risks to using AI in medicine, with careful implementation and oversight, the benefits far outweigh the downsides. Our list of some of the best current tools and use cases for AI in healthcare is a great starting point if you are considering using AI in your healthcare practice.
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Top Use Cases for AI in Healthcare
Artificial intelligence in healthcare refers to the use of natural language processing (NLP), machine learning algorithms, computer vision, and predictive analytics to analyze complex medical data and support clinical or administrative decision-making. Healthcare AI systems can be used in a number of different ways, such as:
- Analyzing large data sets (such as lab results)
- Automating repetitive tasks
- Identifying patterns humans may miss
- Generating predictions or recommendations
This technology can be incredibly beneficial, allowing for faster diagnoses, fewer errors, better workflows, and more time for patient care. In an era where medical professionals are feeling the proverbial crunch, these advantages can’t be overstated.
With the right controls, AI can be used for:
- Medical imaging and diagnostics: AI excels at pattern recognition, which makes it well-suited for certain specialities like radiology, pathology, and dermatology. AI algorithms can detect abnormalities in pathology slides, X-rays, MRIs, CT scans, and more, often much more quickly than a human could. Of course, these results should always be checked by a trained professional.
- Clinical decision support: AI-powered systems can be used to analyze patient medical histories, records, and lab results to assist practitioners with treatment recommendations, predicting disease progression, identifying high-risk patients, and risk stratification.
- Personalized medicine: AI can allow healthcare professionals to offer individualized treatment strategies by analyzing genetic data, lifestyle factors, medication response history, and biomarkers. This can be particularly useful in specialities like oncology, where targeted treatments can offer greater results.
- Administrative automation: AI can reduce administrative overhead costs by automating things like billing and coding, appointment scheduling, claims processing, medical transcription, and appointment scheduling. This can reduce operational costs and staff workload while minimizing errors.
- Remote patient monitoring: when appropriate, patients can wear AI-powered devices to track things like blood glucose, heart rate, blood pressure, and oxygen levels. These tools can alert providers to concerning trends before an emergency arises and can also be helpful for chronic disease management.
- Clinical documentation: AI can be used to summarize patient visits and generate structured notes automatically.
- Staffing: predictive analytics can be used to forecast admissions in hospitals, which can be utilized for more efficient staffing.
These are just some of the current uses for AI in healthcare, with so many more on the horizon, such as AI-enhanced robotic surgeries and drug research and development.
The Risks of Implementing The Wrong AI Tools
Of course, as with any technology, there are potential downsides to using AI in a healthcare practice or system. Any technology that you implement must be HIPAA-compliant so that private patient data is protected. There should also be strong data security protections in place to ensure that patient data is protected.
There are other potential issues with AI use in healthcare, such as the potential for bias when using AI models that have been trained on skewed datasets. AI is also known to make errors, so it is always critical to have a human with the appropriate training review work generated by this technology. Finally, AI software can be expensive, particularly for more specialized programs that meet the needs of healthcare organizations.
Nevertheless, when the right AI tools are integrated correctly into your existing tech stack, the implementation can be transformational for your organization.
The Best Tools for AI in Healthcare
There are many different AI tools on the market now, including those specifically designed for the healthcare industry. So how do you know which one is best?
First, the right software for your organization will depend on your specific needs. If you mostly need help reducing the administrative burden on your staff, then you won’t need a clinical analysis tool. It is always important to identify the exact problem that you want to address before choosing any technology.
Second, AI tools vary widely in price and function. If you run a relatively small dental practice, you might not have the budget (or need) for an enterprise-level software that costs tens of thousands of dollars a year. Our team can help you not only choose tools that meet your needs, but also the ones that fit your budget.
Below, we have put together a list of some great options to consider if you are looking to implement AI in your healthcare practice.
Merative
Formerly IBM Watson Health, Merative is an AI-powered analytics platform for the healthcare industry. It can be used to analyze large sets of clinical and patient data to assist with diagnosis, treatment planning, and patient monitoring.
Merative features several different options, including Micromedex for clinical decision support, Truven for data and analytics, Merge for medical imaging, and Zelta for clinical development. For example, clinicians can use Micromedex to make more informed decisions based on the latest evidence-based research. Truven allows practitioners to offer personalized care by analyzing patient health data to provide actionable insights.
Common Uses
- Clinical decision support: evidence-based insights and drug information at the point of care.
- Healthcare analytics: predictive analytics, population health insights, and benchmarking.
- Medical imaging and workflow: cloud-based imaging solutions to optimize radiology and cardiology workflows.
- Clinical development & trial support: unified clinical data management to accelerate outcomes.
- Real-world data and research: datasets and tools for health economics and outcomes research.
- Social/health services delivery: platforms to manage benefit programs and casework.
Pricing
Pricing for Merative depends on the solutions chosen and is available upon request.
Tempus
Tempus is an AI technology that was specifically developed for oncology. It uses AI to scan genomic data to personalize cancer treatment, matching patients with therapies that are tailored to their specific genetic profiles. It can be used for precision oncology, genomic sequencing analysis, and personalized treatment planning.
Today, Tempus has expanded to specialities beyond oncology. It can be used by neurologists and psychiatrists to optimize medication selection and dosage and track patients’ response to treatment. It can be used by cardiologists to identify care gaps, while radiologists can use the technology to generate actionable insights from diagnostic images.
To use Tempus, medical practices can contact the company directly for the necessary manuals, forms, and testing kits.
Common Uses
- AI-powered clinical assistants: generative AI tools to query clinical and unstructured EHR data.
- Oncology support: insights and decision support for cancer treatment planning.
- Clinical data and genomics: access and analysis of large clinical and molecular datasets.
- Diagnostic AI applications: for example, ECG AI to flag cardiac abnormalities.
- Research and cohort building: AI to identify patient cohorts and enable life sciences analytics.
Pricing
Pricing for Tempus is available upon request. The cost of testing is often covered by a patient’s insurance.
Nuance Dragon Medical One
Nuance Dragon Medical One is an AI-powered clinical documentation tool. It uses AI-driven speech recognition to help physicians and other practitioners document patient encounters in real time. It can be used for clinical documentation and faster charting through electronic health record (EHR) systems. It can also reduce burnout, minimizing administrative burdens so that clinicians can focus more on patients.
Dragon Medical One is cloud-based, so that clinicians can dictate on multiple devices with ease. It also has an automatic accent detection to increase accuracy.
Common Uses
- Clinical speech-to-text dictation: voice-driven clinical documentation to reduce typing time.
- EHR integration: speech recognition works across major EHRs for seamless charting.
- HIPAA-compliant clinical notes: secure, compliant documentation for healthcare environments.
- Accent and specialty support: supports regional accents and specialized medical vocabularies.
Pricing
The software is relatively low-cost, starting with a $525 one-time payment, with a monthly subscription cost of $79 to $99 per month.
Ada
Ada is an AI chatbot that offers self-service diagnostic information to patients. It works by asking users questions about their health and symptoms. It then generates a personalized assessment, which can also include recommendations for relevant care services.
Patients can use Ada directly through the website. Healthcare providers can also partner with Ada to help them identify, engage with, and connect undiagnosed patients to medical care. It integrates with EHRs like Epic to bring symptom assessment directly into the clinical workflow. This allows doctors and healthcare organizations to use Ada to help patients understand healthcare options and request the appropriate care.
Common Uses
- Symptom checker: users input symptoms and receive AI-guided health assessments.
- Care navigation: guidance on next steps (e.g., self-care vs urgent care).
- Enterprise solutions: health plans and systems can embed Ada for patient triage and clinical handovers.
Pricing
The consumer version of Ada is free to use for consumers to check individual symptoms. Enterprise solutions are custom, and pricing depends on deployment complexity and the services required.
Aidoc
Aidoc uses AI to analyze medical images to turn clinical data and signals into actionable insights. It can be utilized across a variety of specialities, such as radiology, cardiology, vascular, and neurovascular practices. The technology uses algorithms to scan medical images for disease and issues such as stroke and deep vein thrombosis.
Aidoc can help to diagnose patients more quickly and reliably. Because it is a unified system, it can also connect care teams across a health system. The end result is better patient care.
Common Uses
- AI radiology analysis: automated triage and flagging of critical imaging findings.
- Workflow prioritization: alerts care teams to urgent cases for faster action.
- Integration with EHR/PACS: deployed inside existing imaging and clinical systems.
- Cardiology and vascular support: extended AI algorithms for structured care.
Pricing
Aidoc is available through the Amazon Web Services Marketplace for $6 per scan per hospital at the starter level. Pricing for enterprise contracts can vary depending on the solutions utilized and the organization's size.
Doximity GPT
DoxGPT is based on the same technology used by ChatGPT, but with extra protections for HIPAA compliance. It is an AI assistant that clinicians can use to enhance clinical workflows and reduce administrative burdens. For example, it can be used to draft letters, charts, and communicate with patients.
This technology can also be used to provide clinical support for healthcare professionals. It offers evidence-based answers along with information on medication dosages, interactions, and side effects. It can also be used as a diagnostic aid to assist with diagnosis.
Common Uses
- Clinical writing support: generate clinical notes, appeal letters, and patient instructions.
- Summarizing literature: condense medical articles and updates.
- Clinical reference queries: contextual answers to clinical questions.
- Administrative automation: drafts correspondence, admin documents, and more.
Pricing
Doximity GPT is free for all doctors, nurse practitioners, physician assistants, pharmacists, podiatrists, CRNAs, and students in these programs. There are also enterprise licensing options available for healthcare systems.
Automate Your Healthcare Practice with Inclind
These AI tools are just a sampling of the many options currently available for healthcare practices. This type of technology can be incredibly valuable, both in terms of improving patient outcomes and in reducing administrative burdens. As with any other kind of technology, the key lies in choosing the right AI for your needs and your budget.
At Inclind, we know that AI can be transformative for healthcare practices when it is implemented correctly. If you are overwhelmed by all of the choices on the market, we can help you narrow down the search and find AI solutions that are a perfect fit for your practice with our AI automation and AI consulting services. We can also create custom integrations to maximize the potential of these tools, such as by integrating AI technology with your EHR systems.
We're happy to chat if you’d like to learn more about our website design and development services for healthcare organizations, including AI consulting and integration. Fill out our online contact form or give us a call at 800-604-8139 to talk to one of our experts about your website.