With chronic illnesses on the rise, SaMD is becoming standard care, whether it&rsquo;s predictive analytics, AI diagnostics, sepsis prediction, or cardiac monitoring tools. Regulatory growth illustrates this, with the FDA authorizing over 1,350 AI-enabled devices in early 2026 (double the number cleared in 2022). Each type of SaMD comes with different regulatory pathways and potential cybersecurity risks, particularly as integration accelerates, making specialist hiring a critical business priority.
<hr>
<h3>Key Takeaways:</h3>
<ul>
<li>Remote patient monitoring is expected to quadruple in value by 2033, from $26bn to $110.7bn.</li>
<li>Predictive analytics, AI diagnostics, and remote monitoring are seeing significant strides in critical and everyday care in areas such as chronic disease management, sepsis and cardiac event prediction, and non-invasive remote monitoring in heart failure patients.</li>
<li>Security risks are becoming a key issue in the SaMD industry, with 53% of networked medical devices containing at least one known critical vulnerability and 1 in 4 healthcare organizations experiencing a device attack in the past year that disrupted patient care.</li>
<li>The FDA&rsquo;s Section 524B has made cybersecurity documentation a formal submission requirement, making the specialist hiring of regulatory, cyber and product security professionals critical for SaMD businesses looking to stay ahead of the curve.</li>
</ul>
<hr>
<h2>What is SaMD in Healthcare?</h2>
Software as a Medical Device (SaMD) is <a href="https://www.fda.gov/medical-devices/digital-health-center-excellence/software-medical-device-samd" target="_blank" rel="noopener">defined by the FDA under the IMDRF definition</a> as &ldquo;software intended to be used for one or more medical purposes that perform these purposes without being part of a hardware medical device&rdquo;.
The primary difference between traditional medical devices and Software as a Medical Device is that traditional medical devices are physical hardware products that use mechanical or electrical parts to treat, diagnose, or monitor patients. SaMD is standalone software that fulfils these medical tasks on standard devices like smartphones, laptops, and cloud servers.
Whilst you might see traditional medical devices such as an ECG machine with physical wires and patches used for cardiac monitoring, the SaMD alternative could be an AI algorithm app that analyzes heart-rate data from a smartwatch to detect atrial fibrillation. Similarly, a continuous glucose monitor with a physical needle sensor for a patient&rsquo;s arm would have the alternative SaMD option of a dose-calculator app for patients to manually log sugar levels to receive custom insulin recommendations.
<h2>How is Software Becoming the Medical Device?</h2>
In early 2026 alone, the <a href="https://intuitionlabs.ai/articles/fda-ai-ml-samd-guidance-compliance" target="_blank" rel="noopener">FDA had authorized over 1,350 AI-enabled devices</a> (double the number cleared in 2022). With the rise in chronic illnesses, SaMD is undoubtedly being treated as a potential solution. Healthentia, a certified medical application acting as a SaMD, conducted <a href="https://healthentia.com/samd-mhealth-apps/" target="_blank" rel="noopener">controlled studies on several chronic diseases</a>: type II diabetes, COPD, cancer, and heart diseases. They found that:
<ul>
<li>HbA1c levels decreased in several of the studies</li>
<li>Physical activity, performance, and body fat had significant improvements</li>
<li>Self-reporting improved self-monitoring and patient knowledge</li>
<li>The 30-day hospital readmission rate was much lower</li>
</ul>
This shift is playing out in critical and everyday care as well, with developments across areas such as predictive analytics, AI diagnostics, and remote monitoring:&nbsp;
<ul>
<li>Predictive analytics: SaMD utilizes predictive analytics for chronic disease management, as outlined above, alongside early diagnosis and screening, and patient monitoring and alerts through artificial intelligence and wearable data.</li>
<li>AI diagnostics: In imaging, deep learning models achieved a <a href="https://www.diagnosticimaging.com/view/advances-in-ai-october-2025" target="_blank" rel="noopener">pooled sensitivity of 89% for detecting cancer spread</a>, compared with 65% for unassisted radiology.</li>
<li>Sepsis prediction: One model, COMPOSER, provided <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC8429719/" target="_blank" rel="noopener">early warning up to 12 hours before the first antibiotics order</a>, tested across six patient cohorts totaling over 500,000 patients.</li>
<li>Cardiac event prediction: Wearable-based deep learning models can <a href="https://link.springer.com/article/10.1007/s11886-026-02358-4" target="_blank" rel="noopener">predict coronary, structural, and infiltrative heart disease with near-clinical accuracy</a>.</li>
<li>Remote monitoring: Non-invasive remote monitoring <a href="https://link.springer.com/article/10.1007/s11897-025-00725-w" target="_blank" rel="noopener">reduces hospitalizations and mortality in heart failure patients</a> by catching early signs of deterioration before they escalate.</li>
</ul>
As SaMD becomes more integrated as a standard of care, the industry undoubtedly will encounter issues relating to regulatory alignment. This will have a knock-on impact on the hiring landscape for software as a medical device that pushes for more cybersecurity and product security professionals to spearhead these changes and overcome potential barriers proactively.
<h2>Types of Medical Device Software</h2>
Not all medical software is under the same umbrella. Each type functions differently for compliance, procurement, and hiring. The main categories are:
<ul>
<li>Software in a Medical Device (SiMD)/embedded software: Embedded within physical hardware and can&rsquo;t function independently (e.g., software controlling an insulin pump).</li>
<li>Software as a Medical Device (SaMD): Standalone software that performs medical functions on its own, covering diagnosis, monitoring, analysis, or treatment decisions and run on general-purpose platforms like smartphones or the cloud.</li>
<li>AI/ML-enabled software: A growing subset of SaMD/SiMD using machine learning for image recognition, predictive analytics, and diagnostic or treatment recommendations. The FDA has already <a href="https://arxiv.org/pdf/2506.15028" target="_blank" rel="noopener">authorized over 1,016 ML-enabled devices</a> across 17 medical disciplines, showing the growth in the area.</li>
<li>Mobile health (mHealth) applications: Apps supporting remote patient monitoring and digital therapeutics, usually overlapping with SaMD when they perform diagnostic or therapeutic functions rather than general wellness tracking.</li>
</ul>
Each category carries different and complex regulatory pathways, which makes specialist hiring critical for businesses looking to stay ahead of the curve.
<h2>What Are the Latest Trends in Medical Device Software Integration?&nbsp;</h2>
The medical device industry has undoubtedly been experiencing a rapid development phase in software innovation. AI and ML are at the forefront of these changes, with AI algorithms that can process complicated medical data helping facilitate early identification of disease, risk stratification, and patient-specific therapy planning.
The interoperability of medical device software is another key trend for medical software integration. Medical device software needs to be integrated with electronic health records, telehealth systems, and population health systems, with shared data standards rather than records existing in silos.
In 2020, the ONC finalized its Cures Act rule, requiring EHR systems to use the FHIR data standard for certified APIs and becoming a <a href="https://www.anisolutions.com/2026/04/02/21st-century-cures-act-ehr-integration/" target="_blank" rel="noopener">compliance requirement in 2026</a>. In essence, it&rsquo;s a common format so that any app or device can plug into any certified EHR and expect the same data, structured in the same way.
ML-enabled devices are now easily interfaced with third-party software, cloud platforms, and IoT systems to be as interconnected as possible, with additional emphasis on remote and real-time monitoring due to the rise of chronic disease rates and wider telehealth adoption.
<h2>How Do Medical Devices Software &amp; Healthcare Apps Ensure Patient Data Security?</h2>
Cybersecurity is now embedded into every stage of development with SaMD:
<ul>
<li>Security embedded into the design: Updated FDA guidance now aligns with multiple <a href="https://compliancehub.wiki/fda-updated-cybersecurity-guidance-medical-devices-premarket-submissions/" target="_blank" rel="noopener">regulatory bodies, global markets, and the IMDRF framework</a>, recommending that device manufacturers adopt and implement a Secure Product Development Framework. This makes security central to the design and development process rather than an add-on.</li>
<li>Continuous threat modelling: Manufacturers are expected to identify potential threats to devices, analyze the attack surface, and evaluate the impact of successful attacks on patient safety and device functionality <a href="https://compliancehub.wiki/fda-updated-cybersecurity-guidance-medical-devices-premarket-submissions/" target="_blank" rel="noopener">early in the development process and as the design evolves</a>, not as a final step.</li>
<li>DevSecOps as standard practice: The software development lifecycle is embedded with security throughout, with DevSecOps automating security checks, encryption standards, and risk management into every phase.</li>
<li>Rising regulatory expectations: The <a href="https://bluegoatcyber.com/guides/fda-cybersecurity-guidance" target="_blank" rel="noopener">FDA&rsquo;s February 2026 guidance</a> has tied cybersecurity into the Quality Management System, making a documented threat model, SBOM, vulnerability management plan, and individual penetration testing a requirement that is scoped into the devices real-world interfaces.</li>
</ul>
For organizations, this makes hiring a critical component in avoiding falling behind compliance requirements. Teams need development lifecycle and FDA cybersecurity- fluent talent to drive their organization.
<h2>The Importance of Cybersecurity and Product Security Professionals for SaMD Companies</h2>
The expansion of SaMD means increased exposure, putting cybersecurity and product security at the forefront. <a href="https://www.mordorintelligence.com/industry-reports/medical-device-cybersecurity-market" target="_blank" rel="noopener">53% of networked medical devices contain at least one critical known vulnerability</a>, and RunSafe Security&rsquo;s 2026 index found one in four healthcare organizations experienced a medical device attack in the past year, which directly disrupted patient care.
These risks are also applicable, with an added layer, to AI-enabled devices, due to <a href="https://www.cybrsecmedia.com/medical-device-cybersecurity-in-2026-more-investment-more-attacks-more-harm/" target="_blank" rel="noopener">model manipulation, data poisoning, and adversarial inputs</a>, all of which are categories that procurement checklists weren&rsquo;t built to assess. If the industry fails to account for the speed of AI adoption with SaMD, the accumulated exposure could be significant.
These potential threats are changing the hiring landscape of SaMD companies to match the pace of changing security needs:
<ul>
<li>Threat modelers and secure development specialists: Embedded from the beginning stages proactively rather than at the end.</li>
<li>SBOM and supply-chain security specialists: As transparency becomes a baseline from a market and regulatory perspective, these professionals will be at the forefront of this change.</li>
<li>AI security specialists: Addressing the specific, unique challenges of ML models, which will require adaptability as the regulations continue to evolve.</li>
<li>Compliance professionals fluent in the FDA&rsquo;s 524B requirements: Cybersecurity documentation is now a necessity for submissions, requiring professionals that can translate engineering work into regulatory language in the appropriate format.</li>
</ul>
<a class="primaryBtn" style="color: rgb(255, 255, 255);" href="https://redbock.com/news/which-life-sciences-roles-are-most-in-demand-right-now/">Read more on the top life sciences roles in 2026 here.</a>
<h2>Why Partner with Redbock?</h2>
Redbock is a leading <a href="https://redbock.com/about-us/" target="_blank" rel="noopener">life sciences consulting firm</a> backed by a global framework, with over 600+ active consultants across the US and a highly specialized approach to resourcing solutions.
If you&rsquo;re looking to keep pace with where the SaMD industry is heading, we can connect you with specialists, from software quality, engineering and validation to data scientists and clinical affairs consultants. <a href="https://redbock.com/contact/">Get in touch today</a> to expand your team.

SaMD Explained: How is Software Becoming the Medical Device? 

The use of AI in clinical trials is transforming precision medicine, genomics, real-world evidence, decentralized trials and predictive analytics. Advances in these areas have increased the need for specialist talent to provide human oversight, from clinical operations and regulatory affairs talent to data scientists and biostatisticians. Organizations now need people who can direct, validate, and act on AI outputs to combat challenges such as data quality, algorithmic bias, and regulatory uncertainty.
<hr>
<h3>Key Takeaways:</h3>
<ul>
<li>AI-assisted recruitment tools are improving clinical trial enrollment by 65% at a time when clinical trials are facing recruitment delays affecting 80% of studies.</li>
<li>Research has indicated that AI can expedite the clinical trial process from recruitment and protocol optimization to data analysis and risk prediction, though human oversight and guidance are still required to keep its use in a relevant context and within ethical boundaries.</li>
<li>Rising protocol complexity and trial failure rates have expanded the use of AI in precision medicine, with emerging trends including the use of AI for protocol risk assessment, predictive recruitment modeling, integration of historical data, and cloud-based feasibility tools.</li>
<li>Human oversight is still critical for regulatory credibility and for genuine human, cognitive engagement with AI outputs, creating significant demand for specialists to meet these increasingly important needs.</li>
</ul>
<hr>
<h2>How is AI Being Used in Clinical Trials?</h2>
AI in clinical trials has a multitude of uses, each providing greater efficiency when implemented effectively. <a href="https://redbock.com/expertise/clinical/">Clinical trials</a> are facing mounting challenges, including <a href="https://www.sciencedirect.com/science/article/pii/S1386505625003582" target="_blank" rel="noopener">recruitment delays affecting 80% of studies</a>, escalating costs exceeding $200 billion annually in pharmaceutical R&amp;D, success rates below 12% and data quality issues affecting 50% of datasets. AI in clinical trials is being treated as a transformative solution.
If the shifting approach to clinical trials could be summarized, it would be that companies are moving from a reactive, tediously manual approach to an automated, predictive one, still reliant on human oversight to avoid biases and include context.
AI is currently being implemented in clinical trials for:
<ul>
<li>Protocol and trial design: AI can choose the eligibility criteria, predict trial success, and build more efficient study designs before the trial starts.</li>
<li>Recruitment: Research has indicated that AI-assisted recruitment tools <a href="https://pubmed.ncbi.nlm.nih.gov/41075423/" target="_blank" rel="noopener">improved enrollment by 65%</a> and cut trial timelines by 30-50%, making them a valuable asset for screening, cohort identification, eligibility extraction, and retention workflows.&nbsp;</li>
<li>Data analysis: AI can expedite data analysis through processing imaging, HER, wearable, and trial data to detect patterns and support overall analysis without lengthy manual work.</li>
<li>Risk prediction: <a href="https://pubmed.ncbi.nlm.nih.gov/40731070/" target="_blank" rel="noopener">AI models are being more frequently implemented to estimate safety, efficacy, and operational risks</a> (e.g., adverse events, trial failure, recruitment issues).</li>
</ul>
<h2>How is AI Transforming Clinical Trial Design and Management?</h2>
The aim of most AI use in clinical trial design and management is to streamline protocol design, automate data management, and to reduce overall timelines and costs.
Clinical trials and medication development rely heavily on recruitment efforts, which can result in failure for the trial if participants are not sourced quickly enough. AI can <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC10720846/" target="_blank" rel="noopener">help to find those who would get the most value out of taking part in clinical research, analyzing data and pinpointing the best sites for recruitment</a>.
A recurrent issue with traditional clinical trials, particularly those intended for smaller and diverse patient groups, is that they lack the analytical sophistication and speed required, combined with difficulty tracking participants&rsquo; adherence to protocols. AI has been shown to improve protocol design alongside patient recruitment, leading to higher chances of trial success.
<a href="https://www.sciencedirect.com/science/article/pii/S1386505625003582" target="_blank" rel="noopener">Analysis revealed significant AI benefits</a>:
<ul>
<li>Patient recruitment tools improved enrollment rates by 65%.</li>
<li>Predictive analytics models achieved 85% accuracy in forecasting trial outcomes.</li>
<li>AI integration accelerated trial timelines by 30-50% while reducing costs by up to 40%.</li>
</ul>
In short, AI can expedite the clinical trial process from recruitment and protocol optimization to data analysis and risk prediction, though human oversight and guidance are still required to keep its use in relevant context and within ethical boundaries.
<h2>What Are the Latest Trends in AI Applications for Clinical Trials?</h2>
There has been a <a href="https://arxiv.org/pdf/2605.29096" target="_blank" rel="noopener">marked increase in AI-related trials over time</a>, with recent growth in reference to machine learning, deep learning, chatbots, GPTs, and large language models, with the United States accounting for the largest number of AI-related trials.
Rising protocol complexity and trial failure rates have <a href="https://www.globenewswire.com/news-release/2026/04/24/3280628/28124/en/artificial-intelligence-clinical-trial-protocol-feasibility-tool-research-report-2026-2-75-bn-market-opportunities-trends-competitive-landscape-strategies-and-forecasts-2020-2035f.html" target="_blank" rel="noopener">expanded the use of AI in precision medicine</a>, with emerging trends including the use of AI for protocol risk assessment, predictive recruitment modeling, integration of historical data, and cloud-based feasibility tools.&nbsp;
Genomic profiling analyzed by AI has also identified actionable mutations in far more tumors (<a href="https://lifebit.ai/blog/ai-powered-clinical-trials-real-world-examples-transforming-research-in-2025/" target="_blank" rel="noopener">67% vs. 33% for smaller panels</a>), improving matching to targeted therapies. Subsequently, demand for biomarker scientists and precision oncology specialists has increased due to a need for greater understanding of companion-diagnostic pathways.
AI and machine learning in clinical trials is also being used to identify complex biomarkers that predict a patient&rsquo;s response, with multi-omic data (<a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC12819606/" target="_blank" rel="noopener">genomics, proteomics, imaging, HER</a>) now integrated via AI, aiding drug target identification and treatment-response to expedite the drug development process. Hybrid and decentralized trials are also becoming more common clinical trial trends, utilizing virtual consent, tele-visits, remote assessments, and home-based data to cut down trial duration and reduce dropouts. Subsequently, hybrid professionals that understand both the science and advanced data systems are essential.
Another up-and-coming element is <a href="https://www.merative.com/blog/real-world-data-trends-2026-the-shift-to-quality-and-ai-precision" target="_blank" rel="noopener">AI-curated real-world evidence (RWE) to build synthetic control arms</a>, which is being used in oncology and rare-disease contexts to reduce control-group size and costs.
The FDA&rsquo;s &ldquo;<a href="https://www.fda.gov/regulatory-information/search-fda-guidance-documents/enhancing-participation-clinical-trials-eligibility-criteria-enrollment-practices-and-trial-designs" target="_blank" rel="noopener">Enhancing Participation in Clinical Trials</a>&rdquo; recommends that sponsors broaden eligibility criteria, optimize trial sites, and monitor enrollment to increase representation, which is a gap AI has filled with predictive analytics, with models flagging geographic and demographic coverage gaps before enrollment opens.
AI in clinical trials is moving from experimentation into core infrastructure, which has meant that an organization&rsquo;s digital fluency is being measured by its specialized talent rather than generalist AI skills.
<h2>Why Human Expertise Remains Essential in AI-Enabled Clinical Research</h2>
There can be a temptation when it comes to AI in clinical trials to view AI as processing data at a scale no team could match manually. The benefits of AI in clinical trials are already well established, from faster timelines and better data quality to lower burden on sites and patients.
However, industry leaders are pointing towards one caveat &mdash; <a href="https://www.bioxconomy.com/clinical-and-research/ai-can-transform-trials-but-human-oversight-is-critical-says-acro" target="_blank" rel="noopener">these gains are only durable when AI augments expert judgment rather than replacing it</a>. Human oversight is still critical for regulatory credibility and the genuine human, cognitive engagement with AI outputs. This has created significant demand for specialists to meet these increasingly important needs:
<ul>
<li>Clinical operations: When AI flags risks (e.g., performance issues, enrollment gaps, safety signals), clinical operations professionals are critical in translating these into real trial decisions using operational judgment AI simply doesn&rsquo;t have on its own.</li>
<li>Regulatory affairs: With AI tools moving into submissions and monitoring, regulators are clear that human accountability is a non-negotiable, as <a href="https://www.florencehc.com/blog-post/rethinking-ai-in-clinical-trials/" target="_blank" rel="noopener">AI should be implemented to strengthen oversight rather than bypass it</a>, requiring regulatory affairs talent to oversee the application of AI.</li>
<li>Data scientists: Data scientists are necessary to validate tools for specific clinical contexts and govern with meaningful oversight, and their critical role is leading to <a href="https://zipdo.co/upskilling-and-reskilling-in-the-pharma-industry-statistics/" target="_blank" rel="noopener">considerable skill gaps at a time when data scientist employment is projected to grow significantly</a>.</li>
<li>Biostatisticians: Bringing statistical judgment to AI-generated risk-benefit and safety predictions before approving, modifying, or rejecting them is essential. This human oversight model for biostatisticians would include reviewing AI predictions and considering data integrity and clinical relevance (particularly important from a patient safety and trial integrity perspective).</li>
<li>Medical experts: Clinical knowledge is paramount in countering a key weakness of AI in clinical trials &ndash; generating confident output that isn&rsquo;t always factually accurate &ndash; by translating complex AI-generated data into decisions without the risk of inaccurate information.</li>
<li>Validation professionals: By building the oversight layer that covers third-party AI models, validation professionals are increasingly important from a regulation perspective, ensuring learning models and automated systems comply and produce accurate data in line with regulatory bodies.</li>
</ul>
<a class="primaryBtn" style="color: rgb(255, 255, 255);" href="https://redbock.com/news/which-life-sciences-roles-are-most-in-demand-right-now/">Read more on the top life sciences roles in 2026 here.</a>
All of the in-demand roles driving clinical trial technology trends ensure that AI systems augment rather than replace human expertise, utilizing hybrid scientific, technical, and regulatory expertise to integrate human oversight.
Sourcing these professionals can be extremely difficult due to high demand, particularly with the pace of technological advancement in clinical trials. At Redbock, we specialize in <a href="https://redbock.com/contact/">sourcing hybrid professionals</a> across quality, clinical, regulatory, and engineering, and can find the specialized expertise you need to be at the forefront of innovation.
<h2>What Are the Main Challenges of Using AI in Clinical Trials?</h2>
The use of AI in clinical trials isn&rsquo;t without challenges and potential barriers. One key concern is <a href="https://www.ppd.com/blog/managing-opportunities-risks-generative-ai-clinical-research/" target="_blank" rel="noopener">AI&rsquo;s tendency to confidently produce factually incorrect information</a>, which for clinical trials where accuracy, reliability, and regulation are paramount is a significant challenge.
Research has also indicated that AI in clinical trials has resulted in selection bias, limited prospective studies, and data quality issues even with the potential to transform trials. Additionally, significant implementation barriers are another issue, including data interoperability challenges, regulatory uncertainty, algorithmic bias concerns, and limited stakeholder trust.
It&rsquo;s clear that the use of AI in clinical trials needs parameters and oversight to overcome these challenges, including <a href="https://www.bio-itworld.com/news/2026/06/19/ai-readiness-in-pharma---getting-the-foundation-right" target="_blank" rel="noopener">evidence, governance, cyber, interoperability, and operational readiness</a>. The ecosystems that can help AI to thrive in clinical trials need to be built on trusted, privacy-first and preserving evidence systems with oversight to function effectively and ethically.
Human expertise is the key to unearthing AI&rsquo;s potential in clinical trials.
<h2>Why Partner with Redbock?</h2>
Redbock is a leading <a href="https://redbock.com/about-us/">life science consulting firm</a>, utilizing a network of 600+ active consultants with deep domain expertise and 24+ years of experience resourcing quality, clinical, regulatory and engineering talent.
If you&rsquo;re struggling to source specialists in the hybrid, hard-to-fill roles that the AI clinical trial landscape demands, <a href="https://redbock.com/contact/">get in touch with Redbock today</a> to build a team that can keep pace with AI-enabled research.

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SaMD Explained: How is Software Becoming the Medical Device?

AI in Clinical Trials: What Are the Latest Trends?