AI in healthcare and your next doctor visit: How the new medical revolution changes men’s care

AI in healthcare is already changing your next doctor visit by automating note taking, speeding up image reads, and turning your labs and wearable data into clearer clinical decisions. The real win is not a robot doctor. It is a more focused conversation, with fewer missed signals in men’s health.

Key takeaways

• According to a 2025 American Medical Association survey, about two thirds of physicians report using AI tools in practice, up sharply from the prior year.
• In a blinded randomized clinical trial, an AI system called EchoNet assessed cardiac ultrasound heart function as accurately as experienced sonographers.
• Testosterone deficiency decisions should be anchored to symptoms plus confirmed testing: guidelines (for example, the AUA) support diagnosing testosterone deficiency only after consistent, repeat early-morning low total testosterone (often using a cutoff around 300 ng/dL), while recognizing assay variability and that clinicians may individualize decisions near the borderline range.^[3]
• Low or inappropriately normal luteinizing hormone (LH) can suggest secondary hypogonadism; in selected men who want to preserve fertility, clinicians may discuss off-label use of SERMs (for example, clomiphene; enclomiphene where available) as part of specialist-guided shared decision-making rather than defaulting to testosterone replacement.^[3]
• Before you agree to any AI that “listens,” ask what tool is used, where audio or transcripts are stored, who can access them, and whether you can opt out without affecting your care.^[4]

Why AI in healthcare matters for men at the next visit

AI in healthcare is changing your next doctor visit in two practical ways. First, it can reduce documentation workload so the clinician can be more present with you. Second, it can help your clinician make sense of the growing volume of tests, images, and device data that increasingly shape men’s health decisions.^[1]

According to research published in Annals of Internal Medicine, clinicians in outpatient practice spend substantial time in the electronic health record, including “desktop medicine” after clinic hours.^[1] That time pressure shows up in the visit. More typing often means less eye contact, fewer follow up questions, and missed chances to discuss sensitive issues that many men downplay, like erectile dysfunction, low libido, depressed mood, fatigue, and urinary symptoms.

In other words, this is not just tech hype. The point is improving attention and decision-making in the exam room. If AI can take on a chunk of clerical work and highlight the most relevant signals in your data, your clinician can spend more of the appointment doing what no model can do: build trust, interpret context, and make a plan that fits your life.

How it works in the exam room and behind the scenes

Ambient note taking that gives you your doctor back

An ambient AI scribe is software that listens to a clinical conversation and drafts a note. It is like a smart recorder that turns the visit into a structured summary. In real clinics, the goal is not to replace the medical record. It is to reduce the manual typing burden that competes with conversation.

According to a 2025 American Medical Association survey, about two thirds of physicians report using AI tools, reflecting how fast these tools are entering everyday care. In practice, you may be asked for consent at check in, then see your clinician glance less at the keyboard and more at you. After the visit, you may receive a clearer after visit summary because the note is more complete.

Privacy note: “Consent” should mean you can say no. You should also be able to ask where the audio, transcript, or note draft is stored and who can access it.^[4]

AI assisted imaging that flags problems faster

Modern imaging creates more data than any human can scan perfectly every time. That is where AI pattern recognition helps. Ultrasound is imaging that uses sound waves to create pictures of organs and blood flow. A sonographer is a specialist who performs ultrasound scans and measures key features.

A 2023 blinded randomized trial tested EchoNet, an AI system that analyzes cardiac ultrasound videos to assess heart function. The AI’s assessments were as accurate as experienced sonographers in that study. This matters to men because heart disease risk often intersects with erectile dysfunction and low testosterone symptoms, and earlier detection can change the urgency and direction of follow up testing.

Other AI tools are also used in radiology and pathology. Radiology is the field that interprets imaging like CT scans and MRIs. Pathology is the field that examines tissue to detect diseases like cancer. Tools such as Aidoc are designed to help radiologists detect issues like brain bleeds and blood clots faster, and platforms such as PathAI can flag early cancer patterns on slides. These systems do not “diagnose you” alone. They prioritize what needs attention and reduce missed findings when case volume is high.^[2]

Turning biomarker and wearable overload into a short problem list

Men now show up with more health data than ever. A biomarker is a measurable signal in blood, urine, or tissue that reflects a body process. A continuous glucose monitor is a wearable sensor that tracks glucose trends throughout the day. HRV, or heart rate variability, is the beat to beat variation in heart rhythm that often reflects stress and recovery.

This flood is useful only if it becomes action. AI in healthcare can analyze time series patterns and connect them to clinical risk in a way that is hard to do manually in a 15 minute visit. The reference use case is straightforward: if your ApoB rises when sleep and activity fall, the “why” may be lifestyle, but the “so what” is clinical. ApoB is a blood marker that estimates the number of atherogenic particles that can drive plaque in arteries.

Research published in European Heart Journal supports ApoB as a strong marker for cardiovascular risk and a useful target alongside traditional cholesterol measures. AI does not replace risk counseling. It can simply make the signal obvious sooner.

Hormone testing example that AI can help surface (without turning it into a shortcut): when symptoms suggest testosterone deficiency, clinicians typically confirm with repeat early-morning testing (because testosterone varies day to day and assays differ), and many guidelines use total testosterone around 300 ng/dL as a reasonable diagnostic cutoff in symptomatic men. Free testosterone may be considered in specific situations (for example, when sex hormone-binding globulin is abnormal), and decisions near the borderline range are often individualized after clinician evaluation.^[3]

Clinical language models that explain, summarize, and sometimes hallucinate

Large language models are AI systems trained to predict text. In healthcare, they can summarize clinical notes, draft patient instructions, and help a clinician interpret a question or lab pattern. Google’s Med PaLM 2 is a well known example discussed as approaching clinician level accuracy on some medical question benchmarks.

Research published in Nature shows that large language models can perform strongly on certain medical knowledge tasks, but accuracy depends on context, question type, and guardrails. This is why you may see AI generated visit summaries that read clearly, but still need clinician review. A model can sound confident while being wrong, especially if your case is complex or missing key details.

This aligns with what many clinicians observe in practice. AI can be “quite good” for common questions, but can make mistakes in complex cases without full clinical context. The safest use is clinician supervised, with human accountability for what gets charted and what becomes your plan.

Drug discovery and repurposing that may reach rare cases first

AI in healthcare is also being used upstream, before a drug ever reaches your pharmacy. Drug repurposing is the process of finding new uses for existing medications. The nonprofit Every Cure, founded by physician scientist David Fajgenbaum, uses AI to scan large datasets for hidden treatment options for rare or overlooked diseases, including Castleman’s disease.

A 2020 review in Nature Reviews Drug Discovery describes how machine learning can speed target discovery and drug repurposing by finding patterns across large biomedical datasets.^[5] For the average man, this may not change tomorrow’s appointment. But it is part of the same medical revolution: more pattern recognition, faster hypothesis generation, and more personalized pathways when standard care fails.

Men’s health conditions AI is already touching

Most men will not experience AI as a single “AI diagnosis.” Instead, it shows up as small upgrades across a workflow: a more complete chart, a faster imaging read, a clearer risk summary, or a reminder that keeps follow up on track. Those upgrades matter in men’s health because many high-impact issues build slowly (cardiometabolic risk, sleep disruption, prostate symptoms) and can be missed when visits are rushed or data is fragmented.

That said, AI’s usefulness depends on how it is deployed. A well-validated model can help surface patterns a busy clinician might not see quickly, but it can also over-call findings, underperform in groups underrepresented in training data, or create false reassurance when data quality is poor. The best results come when AI output is treated as decision support, not a substitute for exam, history, and clinician accountability.

• Heart disease risk: AI assisted imaging and better synthesis of biomarkers like ApoB can speed risk recognition and follow up testing when men have family history, rising markers, or symptoms that overlap with sexual health concerns.
• Type 2 diabetes and insulin resistance: CGM trend analysis can help link meals, sleep, and activity to glucose spikes, which can shape weight loss and cardiometabolic plans.
• Obstructive sleep apnea: Sleep apnea is repeated airway collapse during sleep that lowers oxygen and fragments rest. Symptom tracking and wearable trend summaries can help identify patterns that justify a formal sleep study.
• Testosterone deficiency: AI can help identify men who need a proper workup by connecting symptoms to repeat morning hormone labs and related markers, rather than treating one number in isolation.^[3]
• Male infertility: Fertility workups can involve hormones, semen analysis, and lifestyle factors. Decision support tools may help clinicians keep the evaluation complete and avoid missing reversible causes.
• Prostate cancer and prostate health: Pathology support tools can help flag subtle tissue patterns, and structured follow up reminders can reduce missed surveillance steps after a biopsy or treatment.^[2]

Limitations that matter: despite the marketing, AI can inherit bias from the data it learns from. If training datasets underrepresent certain groups, performance can drop for those patients. Research published in Science has documented how widely used health algorithms can produce biased outputs when they rely on flawed proxies or incomplete data.^[6]

Privacy is also central. Health data is sensitive. You should expect clearer standards on transparency and safeguards, and you should feel comfortable asking how your data is handled.^[4]

What to watch for at your next doctor visit

In a typical clinic, you will rarely be told, “An AI just evaluated you.” Instead, you will notice changes in the flow of the visit: less typing, faster access to prior records, and more structured follow up instructions. Those are good signs when they reflect better documentation and clearer coordination, not automated decisions made without clinician review.

Also keep expectations realistic. A cleaner summary is not the same as a correct plan, and a “normal” AI-generated explanation does not replace clinical judgment when symptoms persist. Your job is to bring the highest-signal information: the symptoms you are actually experiencing, when they started, what makes them better or worse, and any key trends from wearables or home measurements.

• You are asked for consent for an AI scribe: expect a clear yes or no option. Ask what is recorded, how long it is stored, and who can see it.
• Your clinician spends less time typing: this is one of the best signs AI is being used for admin support, not decision making without oversight.
• You get a cleaner after visit summary: look for accurate medication lists, follow up timing, and a problem list that matches what you discussed.
• Your lab results come with plain language explanations: that can be helpful, but confirm that your clinician reviewed anything that sounds definitive.
• You are asked about wearable or CGM data: bring a short trend view, not a month of screenshots. Focus on patterns tied to symptoms.
• Men’s health signals you should mention explicitly: erections that are weaker or less reliable, lower libido, fewer morning erections, persistent fatigue, depressed mood, loss of strength, increased belly fat, loud snoring, and nocturia. Nocturia is waking at night to urinate.

What to do about it

The practical goal is not to become an “AI power user.” It is to use AI-driven workflows to get a more accurate history, cleaner documentation, and more consistent follow up, while keeping decision-making accountable to a licensed clinician. That means asking better questions about what tools are used and how outputs are verified.

This matters most when care decisions have long-term consequences, like hormone therapy, cardiometabolic risk management, sleep apnea workups, or prostate surveillance. In those situations, AI can help ensure the checklist is complete, but it cannot replace diagnosis confirmation, risk discussion, and shared decision-making based on your priorities (including fertility goals).

1. Step 1: Treat AI like a tool and ask three questions. Ask what the tool is, whether it is used for documentation, imaging support, or patient messaging, and who is responsible for the final clinical decision. Then ask about privacy: where the data lives, who can access it, and whether you can opt out without affecting care.^[4]
2. Step 2: Use AI to get a more complete men’s health workup, not a shortcut. If your goal is hormone health, sexual function, energy, or body composition, ask for a guideline based evaluation that looks beyond one testosterone number. The American Urological Association guideline emphasizes symptoms plus confirming low testosterone with appropriate testing (typically two early-morning total testosterone measurements), while considering factors like assay variability and when free testosterone may add value.^[3] If LH is low or inappropriately normal, that pattern can suggest secondary hypogonadism and should prompt evaluation for contributing factors and, when appropriate, referral to urology or endocrinology. In selected men who want to preserve fertility, clinicians may discuss off-label SERMs (for example, clomiphene; enclomiphene where available) as one option, but the choice should be individualized and based on risks, benefits, and goals. If testosterone replacement is considered, it should follow confirmed diagnosis and clinician oversight rather than being started based on a single borderline lab value.^[3]
3. Step 3: Pick a monitoring system before you start any plan. AI shines at follow up. Ask how often labs will be checked, how symptoms will be tracked, and what triggers a dose or protocol change. Monitoring matters for testosterone related care because benefits and risks both depend on dose, baseline risk, and response over time.^[3] A reasonable clinic workflow should spell out what will be monitored (for example, symptoms and side effects, hematocrit/hemoglobin, and prostate screening considerations based on age and risk), how results will be reviewed, and what happens if goals are not met or adverse effects emerge.

Myth vs fact

• Myth: AI in healthcare means your doctor is being replaced.
  Fact: In most clinics, AI is used to draft notes, triage data, and flag risks. A clinician still owns the diagnosis and plan, and should verify outputs.^[2]
• Myth: If an AI summary sounds confident, it is probably correct.
  Fact: Language models can produce convincing text even when key context is missing. Use them for clarity, not authority, unless a clinician validates the content.
• Myth: More trackers always equal better health decisions.
  Fact: Data helps only when it changes actions. A few high signal metrics, reviewed consistently, beat a pile of numbers no one interprets.
• Myth: Testosterone treatment decisions are based on one total testosterone lab value.
  Fact: Good care ties symptoms to repeat morning labs, considers when free testosterone adds value, and evaluates upstream hormones such as LH to clarify potential causes and align therapy with goals, including fertility considerations.^[3]
• Myth: AI eliminates medical bias.
  Fact: AI can inherit bias from training data. Performance can vary across patient groups, so oversight and validation matter.^[6]

Bottom line

AI in healthcare is already here, and how the new medical revolution changes your next doctor visit is mostly about time, clarity, and earlier pattern recognition. Use it to get a more complete workup and a tighter follow up loop, but insist on consent, privacy, and human clinical accountability for every diagnosis and prescription.

References

1. Sinsky C, Colligan L, Li L, et al. Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties. Annals of internal medicine. 2016;165:753-760. PMID: 27595430
2. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine. 2019;25:44-56. PMID: 30617339
3. Mulhall JP, Trost LW, Brannigan RE, et al. Evaluation and Management of Testosterone Deficiency: AUA Guideline. The Journal of urology. 2018;200:423-432. PMID: 29601923
4. Rieke N, Hancox J, Li W, et al. The future of digital health with federated learning. NPJ digital medicine. 2020;3:119. PMID: 33015372
5. Pushpakom S, Iorio F, Eyers PA, et al. Drug repurposing: progress, challenges and recommendations. Nature reviews. Drug discovery. 2019;18:41-58. PMID: 30310233
6. Obermeyer Z, Powers B, Vogeli C, et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science (New York, N.Y.). 2019;366:447-453. PMID: 31649194