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How Healthcare Startups Can Use AI to Personalize Treatment Plans

July 24, 2025 1:53 PM

Tired of finding a way around the one-size-fits-all approach of treatment plans?

What you need to know is that even when diagnosed with the same condition, patients respond to treatments differently.

Inherent differences like genetic makeup, physiological characteristics, lifestyle factors, and environmental exposures contribute to the individual reaction of patients towards the same medication.

Luckily, we have a way out, precision medicine tailors medical interventions and treatments for individuals. It involves scanning patient and clinical data to provide targeted and personalised care.

So, without any delay, let’s uncover how AI in healthcare startups can create personalised treatments.

Advancements of AI In Healthcare Personalization

According to reports by Grand View Research, machine learning in healthcare held the largest market share of over 35 % in 2024. This context-aware computing segment is expected to grow at the fastest CAGR over the next five years.

Healthcare professionals can’t be omnipresent, thus we have AI to vouch for individual variability factors and generate personalized treatment recommendations based on data from patients' back records.

Let’s take a peek at the success of AI models in automating healthcare tasks:

Predictive Analytics

Advanced equipment powered with machine learning and statistical models is used to forecast future health events. AI uses historic patient data to predict risk factors, disease progression, and reaction to specific treatments.

This ability allows healthcare experts to intervene earlier and proactively manage disease for early-stage elimination of critical illness.

Image Recognition

Deep learning models exhibit exceptional expertise in image recognition and diagnosis. These models can examine CT scans, MRIs, and X-rays with accuracy.

Comparing images with already existing databases, these algorithms can detect abnormalities that might escape the human eye.

Data Analysis

It is self-evident that AI in healthcare personalization can scan through and integrate large volumes of patient data.

These digital health AI solutions combine electronic records, genetic data, and historical reports to plot patterns and interlink associations, enabling healthcare professionals to perform accurate diagnoses.

AI Touchpoints in Building Personalized Treatment Plans for Healthcare Startups

If you’re a startup founder on the mission of building a product around personalized treatment with AI in healthcare, here are some must-have areas for AI-tailored solutions:

Diagnostic Support

Diagnostic AI systems can interpret medical images, lab test results, and monitor real-time patient symptoms.

This data equips AI models to identify early signs of potential health issues and disease progression, leading to timely clinical interventions and proactive care plan changes.

Medicine Customization

After considering individual characteristics, such as genetics, lifestyle factors, allergies, and biomarkers, AI models can foresee how patients would respond to specific drug components.

These models can track the efficiency of medicine intake and suggest dosage adjustments and drug alternatives.

Remote Patient Monitoring

Medical practitioners can set remote personalised thresholds based on environmental factors and patients’ needs. AI systems can then detect anomalies in routine and alert physicians on time.

They can also track patient progress throughout the treatment and suggest plan adjustments that can then be sent to healthcare professionals for approval.

Patient Sentiment Analytics

Natural processing language(NLP) can help gauge a patient's voice tone and word choice to tailor communication style based on the user's emotional state.

AI systems can advise healthcare professionals on adjusting their interactions with patients to make sure they feel heard and understood.

The Upsides of AI Incorporation in Personalized Treatment Plans

Integrating digital health AI solutions in treatment processes brings several benefits to both patients and healthcare professionals. Have a look:

Economies of Scale

AI-integrated personalized healthcare treatments can detect disease early, reducing the additional costs and space takeups of unnecessary hospitalizations and interventions.

This implies improved operational efficiency, resource allocation, and cost savings for healthcare organizations.

Polished Clinical Workflows

There is no reason for manual data entry when natural processing language can extract relevant information from clinical notes, to reduce the burden on professionals, allowing them to spend more time on critical cases.

Constant Patient Engagement

AI technology provides on-time feedback on vital signs by interpreting metrics from wearable devices.

This empowers patients to take a proactive role in maintaining their health and managing chronic conditions.

Multidisciplinary Collaboration

AI tools combine data across multiple systems and specialities(care takers to specialised doctors), this holistic collaboration supports the tailoring of personalised treatment plans.

AI-Integration For Next-Gen Treatment Personalization

These advanced use cases can give you an upper hand when building AI healthcare apps:

Drug Monitoring

Employing AI to monitor drug concentrations over time to measure drug levels in blood to ensure optimal dosing.

Ultimately, supporting healthcare providers to adjust dosage regimens for individual variability in drug metabolism and clearance rates.

Pharmacogenetics Testing

With ongoing research in fields like pharmacogenetics (how variations in genes affect a person's response to drugs), AI systems can spot individual genetic makeup to determine suitable medicines and dosages.

Shared Decision-Making

Healthcare providers can collaborate with AI systems to develop treatment plans after accounting for unique characteristics, preferences, and responses.

The AI in patient care takes the lead by providing reports on real-time response to the treatment to make instant adjustments.

When developing a healthcare MVP AI, prioritize features that can evolve with patient data and changing treatment needs.