Artificial Intelligence in Medical Diagnosis: Real-World Examples and Applications

Key takeaways

Clinicians can use artificial intelligence in medical diagnosis as a decision-support tool to improve diagnostic accuracy and efficiency.

• As of 2025, 76 percent of AI-enabled medical devices approved by the US Food & Drug Administration are intended for radiological use, making medical imaging the largest target for AI among medical specialties, according to a study [1, 2].

• You can use AI to diagnose cardiovascular and neurological diseases, cancer, skin disorders, and a range of rare diseases.

• You can find AI-focused health care career paths in technical, clinical, and operational areas with job roles such as bioinformatics analyst, clinical informaticist, and AI health care ethicist.

Explore a variety of artificial intelligence tools used for medical diagnosis, and discover real-world implementations, pros and cons, and career opportunities related to artificial intelligence in medical diagnosis. To expand your understanding of how AI is impacting various industries, including health care, consider enrolling in IBM’s AI Foundations for Everyone Specialization. In as little as four weeks, you’ll have the opportunity to learn more about deep learning, generative artificial intelligence (GenAI), natural language processing, machine learning, and more.

AI in Action: Revolutionizing Diagnostics

AI models for medical diagnostics may use machine learning, deep learning, natural language processing (NLP), computer vision, robotics, or a combination of techniques to aid in the process of making a diagnosis.

As you evaluate the various AI tools available for medical diagnosis and their underlying technology, consider how well a) the model addresses the specific task you want it to perform, b) the data you have matches the data needs of the model, c) your organization can maintain regulatory compliance while also getting maximum performance from the model, d) the model meets your risk tolerance threshold, and e) the AI can explain its decisions.

Which AI is good for medical diagnosis?

The AI you use for medical diagnosis will likely differ depending on whether you are a clinician using AI as a tool in your medical practice or if you are a member of the public just looking for a quick opinion on some symptoms you’ve been experiencing. 

Popular tools with medical diagnosis capabilities designed for use by health care providers include BioMorph, Doximity GPT, Dr. CaBot, and Microsoft’s AI Diagnostic Orchestrator (MAI-DxO). Some health care providers also use these tools in clinical settings. 

If you’re looking for a tool to help you evaluate your own symptoms, consider ChatGPT or Claude, both of which are affordable and easily accessible generic AI services available to the general public. It’s important not to use AI tools in place of professional medical advice, and to consult a health care professional before taking any action. 

Uses of AI in medical diagnostics

Artificial intelligence can aid in medical diagnostics in various areas, including medical imaging analysis, pathology and laboratory testing, predictive analytics, personalized medicine, genomic and multi-omic data analysis, and enhanced patient monitoring. Its ability to process and analyze vast amounts of data for anomalies and patterns imperceptible to the human eye provides improved decision support that can lead to earlier detection and enhanced patient outcomes.

Given its potential, it is no surprise that AI use by physicians increased by 78 percent from 2023 to 2024, with nearly two-thirds of doctors reporting AI usage in 2024 [3]. In the report from the American Medical Association, doctors reported using AI in the creation of patient discharge instructions, documentation, research, and standard of care summarizations [3].

Artificial intelligence in medical imaging

According to a study published in November 2025, 76 percent of AI-enabled medical devices approved by the US Food & Drug Administration were intended for radiological use [1]. As such, medical imaging is currently the most significant target for AI among medical specialties [2]. Integrating artificial intelligence in medical imaging can enhance the patient’s experience and improve the clinician’s work environment by expediting disease diagnosis, automating workflows, improving image quality, and helping tailor treatment plans based on individual patient profiles.

What diseases can AI diagnose?

AI can assist in detecting many diseases, from common or chronic illnesses like diabetes, depression, heart disease, cancer, stroke, liver diseases, and respiratory issues, to complex neurological conditions like Alzheimer’s and Parkinson’s diseases. Additionally, AI may also expedite the diagnosis of rare diseases, which can be challenging to diagnose.

Clinicians use AI as a decision-support tool to help diagnose many diseases you’re likely familiar with. 

• AI in cardiovascular disease diagnosis: Analyzes medical data, including electrocardiograms, CT scans, and echocardiograms, to spot patterns that can predict risks for heart failure, identify plaque buildup, and detect valve issues and arrhythmias.

• AI in cancer diagnosis: Analyzes medical data such as images (including mammograms, MRIs, and colonoscopies), genomics, and electronic health records (EHRs), detecting subtleties, flagging potential tumors, highlighting abnormalities, predicting cancer types and genetic mutations from tissue slides, and assessing treatment responses

• AI in neurological disease diagnosis: Analyzes complex data such as images, brain wave patterns (electroencephalogram, or EEG), and genetics for earlier and more accurate detection of conditions like Alzheimer’s, stroke, multiple sclerosis, and Parkinson’s, paving the way for disease prediction, earlier detection, more personalized treatment options, and improved accessibility

• AI in dermatology: Analyzes skin images to improve skin cancer detection, classifies skin lesions, and assesses skin conditions such as psoriasis and acne, potentially reducing unnecessary biopsies and improving early disease detection

• AI in rare disease diagnosis: Analyzes multiple types of data, including genetic, lifestyle, and environmental variables, to aid in the diagnosis of complex or rare diseases; identifies patterns invisible to the human eye; flags at-risk patients; and prioritizes genetic variants, potentially shortening a long diagnostic process and reducing misdiagnosis, both of which are common to those ultimately diagnosed with rare diseases

Real-World Impact and Specialized Applications of AI in Diagnosis

Artificial intelligence is changing the landscape in health care and research facilities nationwide, with scientists using it to read images, flag abnormal results, and support earlier diagnosis while also searching for ways to increase the technology’s reliability and accuracy. From enhanced reliability in cancer detection to advances in the accuracy of neurological disorder diagnosis and the early detection of sepsis, AI is making its mark in the field of medical diagnostics.

The following programs provide just a small sample of AI use in medical diagnostics in health care settings, including the Cleveland Clinic, Mayo Clinic, Johns Hopkins, and the Ludwig Center, nationwide.

Neurology AI Program (NAIP)

At the Mayo Clinic in Rochester, MN, scientists are using AI and machine learning to provide faster, more accurate diagnoses and personalized treatment of neurological conditions such as Alzheimer’s and other types of dementia. Researchers are testing StateViewer, a Mayo-created AI platform that analyzes brain scans to detect patterns earlier and more precisely than the human eye alone. The platform focuses on identifying patterns characteristic of different types of dementia, helping clinicians determine the cause and stage of a patient’s condition, which can help personalize their treatment plan [4].

Multidimensional informed generalized hypothesis testing (MIGHT)

Researchers from the Ludwig Center, Johns Hopkins Kimmel Cancer Center, and Johns Hopkins Whiting School of Engineering have been investigating an AI method that has the potential to significantly enhance the reliability and accuracy of artificial intelligence in various areas, including cancer detection. Multidimensional informed generalized hypothesis testing (MIGHT) focuses on measuring uncertainty and increasing reliability, especially in cases with limited sample sizes and highly complex data, providing information to meet the high level of confidence needed for AI tools used in clinical decision-making. Using real data to fine-tune itself and decision trees to verify its accuracy, MIGHT has demonstrated effectiveness in analyzing biomedical data sets with numerous variables but limited patient samples, which can be challenging for traditional AI models [5]. 

Targeted real-time early warning system (TREWS)

Clinicians at several Cleveland Clinic hospitals have partnered with Bayesian Health to implement their AI sepsis detection software. Sepsis, a life-threatening medical emergency, is a common cause of in-hospital death. Bayesian Health’s targeted real-time early warning system (TREWS) software helps health care providers determine if a patient is at risk for developing sepsis by analyzing vast amounts of data, including laboratory tests, vital signs, and clinical notes, to obtain a picture of a patient’s unique clinical history. Based on this information, 

clinicians can prioritize and intervene with at-risk patients, providing effective early treatment [6].

Pros and cons of using AI in medical diagnostics

As with artificial intelligence in general, AI in medical diagnostics provides myriad advantages (efficiency, access, and personalization, to name a few) as well as areas that require special attention, including bias, accountability, and over-reliance. 

To gain a complete understanding of AI’s implications in medical diagnostics, consider the pros and cons of the technology.

Pros: Artificial intelligence in medical diagnosis

From enhanced quality, accuracy, and speed in diagnosis to the technology’s ability to detect subtle patterns in images or data, artificial intelligence offers a range of benefits in medical diagnostics. Additionally, the technology provides quality diagnostics in low-resource settings, reduces the workload for health care providers, and allows for more personalized treatment plans. Overall, these benefits reduce uncertainty in medicine, leading to improved patient outcomes.

• Quality, accuracy, and speed improvements: Earlier detection, reduced false positives

• Pattern detection: Analyzes massive data sets, detecting subtle and complex patterns more efficiently and consistently than humans alone can

• Care democratization: More accessible and affordable diagnostic capabilities in areas with limited access to specialized health care providers 

• Workload reduction: Provides seamless access to information; automates administrative tasks such as clinical documentation, making more time for patient care; eases remote patient monitoring; and reduces previsit preparation time

• Treatment personalization: Offers efficient analysis of patient-specific data to help in the development of personalized treatment plans tailored to individual health needs

Cons: Artificial intelligence in medical diagnosis

The limitations of AI in medical diagnostics include data issues, technical hurdles, integration challenges, and ethical factors. These factors can lead to misdiagnosis, perpetuate health disparities, and necessitate significant oversight. Therefore, it is important to remember that AI is not a replacement for human judgment; rather, it is a decision-support tool.

• Data issues: Bias, quality, privacy

• Technical hurdles: Lack of transparency, generalizability

• Integration challenges: Cost, interoperability

• Ethical factors: Lack of empathy, accountability, and over-reliance, leading to automation bias

Learn more: AI Ethics: What It Is, Why It Matters, and More

Can AI diagnose my symptoms?

AI can offer you information and assist you with checking your symptoms, but it can’t provide you with a formal medical diagnosis. You can use AI tools for symptom diagnosis to learn more about potential causes for your symptoms and to guide you on potential next steps, but not to replace the judgment of professional licensed health care providers. 

Career options for working with AI focused on medical diagnostics

Your career options for working with AI in medical diagnosis span from technical engineering roles in which you might design and develop medically focused AI-powered tools to specialized clinical positions focusing on improving accuracy, speed, and personalization in patient care and operations-centered jobs that leverage AI to streamline health care operations.

If you’re interested in a career in AI focused on medical diagnostics, the following job titles are just a few examples of the types of roles you may want to consider.

Technical roles might include:

• Health care AI engineer: Leverage your computer science and programming skills to design and develop health care-specific AI systems in various areas.

• Bioinformatics analyst: Draw on your expertise in biology, computer science, and information technology to conduct studies, maintain biology data, and develop software that can analyze and interpret vast amounts of data. 

• Health care robotics engineer: Apply your knowledge of mechanical design, control systems, sensing technology, computing, and biomechanics to design, build, and maintain robots for medical applications such as surgical assistance and patient rehabilitation.

• Data scientist or analyst: Rely on your experience to collect, clean, analyze, and interpret complex medical data to enhance clinical decision-making and support improved patient care

Clinical roles might include:

• AI diagnostics specialist: Integrate the use of machine learning models into your practice to help you analyze complex medical data to detect anomalies, predict disease risk, and personalize treatment plans.

• AI-enhanced radiologist or pathologist: Leverage your unique knowledge of machine learning principles, AI-based software, and real-world applications in diagnostic imaging to capitalize on the benefits AI can provide.

• Clinical informaticist: Combine your understanding of health care and information technology to design, implement, and manage secure and efficient computerized systems, train staff, improve workflows, ensure data security, and optimize clinical processes.

• Telemedicine specialist: Use AI-powered diagnostic tools to provide remote care. 

Operational roles might include:

• AI health care ethicist: Collaborate with developers, clinicians, and patients to create frameworks that prevent harm from biased algorithms or data misuse, and ensure AI systems align with human values and focus on transparency, privacy, and accountability.

• Health care project manager: Rely on your project management skills, including timeline adherence, budgeting, and facilitation, to help implement AI technologies that improve clinical workflows.

Stay current on trends in artificial intelligence

If you’re considering a career in artificial intelligence or health care, subscribe to our LinkedIn newsletter, Career Chat, to stay current on industry-related topics. You can also explore these free resources and more to learn more about both of these exciting fields.

• Watch on YouTube: Artificial General Intelligence vs. AI: How Close Are We?

• Hear from industry professionals: AI Problem-Solving Tactics: Expert Insights from Vanderbilt’s Dr. Jules White

• Join the conversation: AI in health care: uses, benefits, and concerns

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