AI in Diagnosis and Medical Treatment: Real Uses, Benefits and Risks

Reviewed 15 November 2026 · Approx. 10-minute read · AI in healthcare

An AI system highlights a suspicious area on a scan. A screening programme uses software to prioritise people who need further testing. A cancer team reviews computer-generated outlines before planning radiotherapy. These are practical ways AI can support medicine—but they represent different levels of evidence, responsibility and autonomy.

Where AI fits in diagnosis and treatment

The FDA maintains a list of AI-enabled devices authorised for marketing in the United States. That status applies to particular devices and intended uses; it does not establish that every AI health app is effective, or authorise its use in another country. [1]

Documented applications in diagnosis

1. Diabetic eye-disease screening

Software can analyse retinal photographs for specified signs of diabetic retinopathy. IDx-DR is a documented US regulatory example of a device for this defined detection task. Such a system has a particular patient population, imaging requirements and referral pathway; it is not a comprehensive eye examination. An ungradable image also needs an appropriate follow-up route. [3]

2. Stroke triage from imaging

The FDA's 2018 announcement for Viz.AI Contact describes software that analyses brain imaging and alerts providers to findings associated with a potential stroke. This illustrates prioritisation and communication support. It does not replace the full scan review, clinical assessment or treatment decision. A negative alert must not be interpreted as excluding every type of stroke. [4]

3. Assistance during colonoscopy

GI Genius is a regulatory example of machine-learning assistance that highlights potential lesions during colonoscopy. The endoscopist evaluates the finding and determines what action is appropriate. A highlighted region is not, by itself, a cancer diagnosis; removal and pathology decisions follow the clinical pathway. [5]

4. Tuberculosis screening on chest X-rays

WHO has issued guidance on computer-aided detection for TB screening. Software-generated scores can support a programme's decision about who needs further evaluation. Screening must be linked to confirmatory testing and care; a score does not independently establish active TB. The cited 2025 WHO policy evaluated screening in people aged 15 years and older; it did not recommend CAD screening for younger children or adolescents. Check later policy updates, local protocols, thresholds and operating conditions before use. This is relevant to resource-constrained settings, including Indian programmes evaluating such tools. [6]

5. Other imaging and pattern-recognition tasks

AI-enabled devices span radiology and other specialties. A system may measure a structure, highlight a possible abnormality or support an interpretation. These are different claims. Ask what the tool detects, which scans and patients it was validated on, and whether its output changes care safely—not simply whether it has a high headline accuracy. [1]

How AI can support medical treatment

6. Radiotherapy planning

AI auto-contouring tools can draft outlines of anatomical structures used in radiotherapy planning. NICE's assessment describes this application and the need to review contours before use. The treatment team checks boundaries and the resulting plan; an incorrectly outlined structure can affect the radiation plan. Time saved on a draft does not remove clinical quality assurance. [7]

7. Treatment monitoring and device control

Automated insulin-delivery systems illustrate computer-assisted treatment using glucose monitoring, a control algorithm and an insulin pump. They are a useful comparison with AI-enabled care, but not every automated control algorithm uses machine learning. These are specialised, regulated systems with compatibility and use requirements—not an instruction to let a chatbot calculate or change a person's insulin dose. [8]

8. Decision support and individualised planning

Specialist teams may evaluate models that estimate outcomes or help organise relevant evidence for a patient's treatment options. A prediction is not proof that choosing one treatment will cause a better outcome. Clinical review must consider diagnosis, comorbidities, patient preferences and the evidence for the intervention. A research model should not be presented as a routine clinical standard merely because it performs well on a dataset.

9. Surgical assistance and navigation

AI may be incorporated into selected image-analysis, planning or navigation functions. Robotic surgery, however, is not automatically AI, and a robot used by a surgeon should not be described as independently performing an entire operation. Establish the exact device function, intended use and level of human control.

10. Documentation, follow-up and communication

AI scribes can draft consultation notes, and language tools can help prepare patient information for review. These may reduce administrative work, allowing clinicians to focus on the encounter. Incorrect summaries, omitted allergies or invented findings can still cause harm if entered into a record without checking. NHS England has issued specific guidance on AI-enabled ambient scribing. [11]

What benefits are realistic?

• Timeliness: prioritising suspicious findings can help teams direct attention where it is needed.
• Capacity: well-integrated tools may reduce repetitive review or preparation work.
• Consistency: a defined measurement can be applied systematically, while still requiring quality checks.
• Access: screening support may help where specialists are scarce, if referral and treatment remain available.
• Communication: reviewed summaries and plain-language explanations can help people prepare questions.

Benefits are not automatic. A tool that generates many false alarms, needs repeated scans or adds substantial review work can increase workload. Evaluate the full patient pathway, including follow-up, cost and outcomes.

The major risks

WHO's guidance on large multimodal models highlights risks including inaccurate outputs, bias, privacy and governance. Its recommendations call for responsible oversight rather than assuming a conversational interface makes a system clinically reliable. [2]

Why “95% accurate” can be misleading

Illustrative arithmetic, not a product result: imagine 1,000 people are screened, and 10 actually have a condition. A test with 90% sensitivity flags 9 of those 10 and misses 1. At 95% specificity, roughly 50 of the 990 people without the condition are also flagged. About 9 of roughly 59 positive results would represent the condition—around 15%.

The example shows why sensitivity, specificity and the prevalence of disease all matter. A positive screen often needs confirmation. A negative result does not override a clinician's judgment, particularly when symptoms or risk factors warrant investigation.

Indian context: evidence, ethics and authorisation

ICMR's 2023 ethical guidelines address AI in biomedical research and healthcare. They provide an ethical framework; they are not a product-specific approval. Institutions should establish accountability, privacy safeguards, informed participation where applicable and appropriate oversight. [9]

For an Indian implementation, verify the product's applicable medical-device status, intended use and local requirements through CDSCO and relevant authorities. CDSCO's 2026 medical-device software guidance explains existing requirements under the Medical Devices Rules, 2017; it is an awareness document and does not replace the governing legislation or product-specific licensing. Overseas authorisation should not be treated as an automatic substitute for Indian requirements. [10]

Questions a patient can ask

1. What part of my care is this AI tool supporting?
2. Is it a screening aid, a diagnostic device or a general information tool?
3. Who reviews the result, and what happens if it conflicts with the clinical assessment?
4. What additional tests or follow-up may be needed?
5. How will my information be stored, shared and protected?

Do not start, stop or change a medicine solely because of a chatbot answer. If symptoms suggest an urgent problem, seek timely medical care rather than waiting for an AI explanation.

Two useful, bounded prompts

Prepare for a consultation

Turn my notes into a concise timeline and a list of questions for my clinician. Use only what I wrote. Separate symptoms, dates, existing diagnoses and medicines already prescribed. Mark missing details. Do not diagnose, recommend a medicine or suggest a dose change.

Understand an explanation

Explain this clinician-provided text in plain language without adding conclusions. Define unfamiliar terms, retain uncertainty and list questions I should ask the treating team. Do not infer a diagnosis from one result. I will remove identifying details before sharing the text.

What hospitals should check before deployment

Define the clinical task and responsible professional, inspect evidence for the intended patient population, validate the local workflow and establish a failure pathway. Record software versions, monitor false alarms and missed cases, review updates and retain an incident-reporting process. Judge value by safe patient outcomes and total workload, not a demonstration or benchmark alone.

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