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Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations.

Evgenia Baykova1, Òscar Raya2, Cristina Lombardía1

  • 1Institute of Health Care (ICS-IAS), Girona, Spain.

Frontiers in Psychiatry
|September 8, 2025
PubMed
Summary

TDApp, a clinical decision support system, offers personalized ADHD treatment recommendations. It provides a more diverse and tailored approach compared to traditional clinical practice guidelines, enhancing shared decision-making.

Keywords:
Artificial intelligence (AI)Attention defcit hyperactivity disorder (ADHD)clinical practice guidelinesevidence base for decision makingpatient empowermentrecommendation systemsshared decision making

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems
  • Pharmacogenomics

Background:

  • Clinical practice guidelines (CPGs) face limitations including obsolescence, lack of personalization, and insufficient patient involvement, potentially leading to suboptimal treatment outcomes.
  • APPRAISE-RS, an adaptation of the GRADE heuristic, utilizes symbolic AI to generate automated, updated, personalized, participatory, and explanatory treatment recommendations.
  • TDApp is a clinical decision support system (CDSS) designed to implement APPRAISE-RS for Attention-Deficit/Hyperactivity Disorder (ADHD) treatment.

Purpose of the Study:

  • To evaluate the efficacy and characteristics of treatment recommendations generated by TDApp compared to existing CPGs for ADHD.
  • To assess the diversity and concordance of TDApp's recommendations against multiple established CPGs.
  • To determine the potential of TDApp as a tool for shared treatment decision-making in ADHD management.

Main Methods:

  • Two clinical trials enrolled 33 and 32 ADHD patients, respectively, for treatment initiation or modification.
  • TDApp recommendations were compared against CPGs from five international organizations.
  • Treatment diversity was analyzed using Blau's index, concordance was assessed by drug endorsement overlap, and recommendation distances were visualized using dendrograms.

Main Results:

  • TDApp provided favorable treatment recommendations for 50-75% of patients, evaluating over 10 drugs, with amphetamine derivatives frequently suggested.
  • TDApp generated 8-12 distinct recommendations with a higher diversity index (0.70-0.88) than CPGs.
  • Dendrogram analysis showed TDApp's recommendations clustered separately from CPGs, indicating a distinct approach.

Conclusions:

  • TDApp functions as an advanced CDSS prototype, delivering automated, personalized, and explanatory ADHD treatment recommendations.
  • The system demonstrates a promising alternative to traditional CPGs, facilitating improved shared decision-making between clinicians and patients.
  • TDApp's approach offers greater personalization and diversity in treatment options for ADHD management.