Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations
IntroductionClinical practice guidelines (CPGs) have several limitations, namely: obsolescence, lack of personalization, and insufficient patient participation. These factors may contribute to suboptimal treatment recommendation compliance and poorer clinical outcomes. APPRAISE-RS is an adaptation o...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1582746/full |
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| author | Evgenia Baykova Òscar Raya Cristina Lombardía Begoña Gonzalvo Inés Andreu David Losada Tania Falkenhain Ruth Cunill Domènec Serrano David Rigau David Ramírez-Saco Beatriz López Xavier Castells |
| author_facet | Evgenia Baykova Òscar Raya Cristina Lombardía Begoña Gonzalvo Inés Andreu David Losada Tania Falkenhain Ruth Cunill Domènec Serrano David Rigau David Ramírez-Saco Beatriz López Xavier Castells |
| author_sort | Evgenia Baykova |
| collection | DOAJ |
| description | IntroductionClinical practice guidelines (CPGs) have several limitations, namely: obsolescence, lack of personalization, and insufficient patient participation. These factors may contribute to suboptimal treatment recommendation compliance and poorer clinical outcomes. APPRAISE-RS is an adaptation of the GRADE heuristic designed to generate CPG-like treatment recommendations that are automated, updated, personalized, participatory, and explanatory using a symbolic AI approach. TDApp is a clinical decision support system (CDSS) that implements APPRAISE-RS for ADHD.MethodsTwo clinical trials were conducted. In both studies a total of 33 and 32 ADHD patients, respectively, requiring treatment initiation or a major treatment change were enrolled. TDApp recommendations were compared to those of selected CPGs (American Academy of Pediatrics, National Institute for Health and Care Excellence, Spanish Health System, Canadian ADHD Resource Alliance, and the Australasian ADHD Professionals Association) CPGs. The diversity of treatment recommendations was analyzed using Blau’s index. Concordance between TDApp and CPGs recommendations was assessed by calculating the proportion of patients for whom TDApp recommended one drug that was also endorsed by CPGs. Dendrograms were plotted to compare the distance between treatment recommendations as calculated using the NbN nomenclature.ResultsThe first study investigated eight methods that differed in how patient and clinician preferred outcomes were handled and the extent to which TDApp tailored the analysis of evidence. The method deemed most suitable was examined in the second study, which found that 50-75% of the patients received at least one favorable treatment recommendation. TDApp evaluated over 10 drugs, including recently marketed ones, with amphetamine derivatives emerging as the most frequently recommended interventions. TDApp generated 8–12 distinct treatment recommendations with a diversity index of 0.70-0.88, which was higher than those of CPGs. The proportion of patients for whom TDApp recommendations overlapped with at least one drug endorsed by CPGs ranged from 21.9% to 100%. Dendrogram analysis revealed that TDApp was positioned on one side of the tree, while CPGs clustered together on the opposite side.ConclusionsTDApp is an advanced prototype of an CDSS offering automated, participatory, personalized, and explanatory treatment recommendations for ADHD. It represents a promising alternative to CPGs for aiding clinicians and patients in shared treatment decision-making. |
| format | Article |
| id | doaj-art-d637fc2992154200bf3951e53d2af36e |
| institution | Kabale University |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Psychiatry |
| spelling | doaj-art-d637fc2992154200bf3951e53d2af36e2025-08-22T05:27:01ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-08-011610.3389/fpsyt.2025.15827461582746Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendationsEvgenia Baykova0Òscar Raya1Cristina Lombardía2Begoña Gonzalvo3Inés Andreu4David Losada5Tania Falkenhain6Ruth Cunill7Domènec Serrano8David Rigau9David Ramírez-Saco10Beatriz López11Xavier Castells12Institute of Health Care (ICS-IAS), Girona, SpainControl Engineering and Intelligent Systems (eXiT), University of Girona, Girona, SpainInstitute of Health Care (ICS-IAS), Girona, SpainInstitute of Health Care (ICS-IAS), Girona, SpainInstitute of Health Care (ICS-IAS), Girona, SpainInstitute of Health Care (ICS-IAS), Girona, SpainInstitute of Health Care (ICS-IAS), Girona, SpainSant Joan de Deu-Numancia Health Park, Barcelona, SpainInstitute of Health Care (ICS-IAS), Girona, SpainIbero-American Cochrane Center (CCIb), Barcelona, SpainDepartment of Clinical Pharmacology, Valll d’Hebron Barcelona Hospital Campus, Barcelona, SpainControl Engineering and Intelligent Systems (eXiT), University of Girona, Girona, SpainTransLab Research Group, Department of Medical Sciences, University of Girona, Girona, SpainIntroductionClinical practice guidelines (CPGs) have several limitations, namely: obsolescence, lack of personalization, and insufficient patient participation. These factors may contribute to suboptimal treatment recommendation compliance and poorer clinical outcomes. APPRAISE-RS is an adaptation of the GRADE heuristic designed to generate CPG-like treatment recommendations that are automated, updated, personalized, participatory, and explanatory using a symbolic AI approach. TDApp is a clinical decision support system (CDSS) that implements APPRAISE-RS for ADHD.MethodsTwo clinical trials were conducted. In both studies a total of 33 and 32 ADHD patients, respectively, requiring treatment initiation or a major treatment change were enrolled. TDApp recommendations were compared to those of selected CPGs (American Academy of Pediatrics, National Institute for Health and Care Excellence, Spanish Health System, Canadian ADHD Resource Alliance, and the Australasian ADHD Professionals Association) CPGs. The diversity of treatment recommendations was analyzed using Blau’s index. Concordance between TDApp and CPGs recommendations was assessed by calculating the proportion of patients for whom TDApp recommended one drug that was also endorsed by CPGs. Dendrograms were plotted to compare the distance between treatment recommendations as calculated using the NbN nomenclature.ResultsThe first study investigated eight methods that differed in how patient and clinician preferred outcomes were handled and the extent to which TDApp tailored the analysis of evidence. The method deemed most suitable was examined in the second study, which found that 50-75% of the patients received at least one favorable treatment recommendation. TDApp evaluated over 10 drugs, including recently marketed ones, with amphetamine derivatives emerging as the most frequently recommended interventions. TDApp generated 8–12 distinct treatment recommendations with a diversity index of 0.70-0.88, which was higher than those of CPGs. The proportion of patients for whom TDApp recommendations overlapped with at least one drug endorsed by CPGs ranged from 21.9% to 100%. Dendrogram analysis revealed that TDApp was positioned on one side of the tree, while CPGs clustered together on the opposite side.ConclusionsTDApp is an advanced prototype of an CDSS offering automated, participatory, personalized, and explanatory treatment recommendations for ADHD. It represents a promising alternative to CPGs for aiding clinicians and patients in shared treatment decision-making.https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1582746/fullAttention defcit hyperactivity disorder (ADHD)recommendation systemsevidence base for decision makingshared decision makingArtificial intelligence (AI)patient empowerment |
| spellingShingle | Evgenia Baykova Òscar Raya Cristina Lombardía Begoña Gonzalvo Inés Andreu David Losada Tania Falkenhain Ruth Cunill Domènec Serrano David Rigau David Ramírez-Saco Beatriz López Xavier Castells Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations Frontiers in Psychiatry Attention defcit hyperactivity disorder (ADHD) recommendation systems evidence base for decision making shared decision making Artificial intelligence (AI) patient empowerment |
| title | Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations |
| title_full | Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations |
| title_fullStr | Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations |
| title_full_unstemmed | Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations |
| title_short | Assessing TDApp: An AI-based clinical decision support system for ADHD treatment recommendations |
| title_sort | assessing tdapp an ai based clinical decision support system for adhd treatment recommendations |
| topic | Attention defcit hyperactivity disorder (ADHD) recommendation systems evidence base for decision making shared decision making Artificial intelligence (AI) patient empowerment |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1582746/full |
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